bio-inspired approach for improving performance of ...1319632/fulltext01.pdf · tion som till˚ater...

INOM EXAMENSARBETE MASKINTEKNIK,AVANCERAD NIVÅ, 30 HP

, STOCKHOLM SVERIGE 2018

Bio-inspired approach for improving performance of information routing in Vehicular Ad Hoc Networks

DAN NGUYEN

KARL HANSEN

KTHSKOLAN FÖR INDUSTRIELL TEKNIK OCH MANAGEMENT

Examensarbete TRITA-ITM-EX 2018:717

Bio-inspirerad informationsrouting inatverk anpassade for fordonstrafik

Dan NguyenKarl Hansen

Godkant: Examinator:

Dejiu ChenHandledare:

Lei FengUppdragsgivare:

Cybercom Group ABKontaktperson:

Gabriel Andersson Santiago

SammanfattningEn underliggande teknologi for Intelligenta Transport System (ITS) ar Vehicle-2-Vehicle (V2V) kommunika-tion som tillater interkonnektivitet mellan bilar. Andamalet med denna interkonnektivitet ar att minska bi-lutslapp, minska trangsel och oka sakerheten i trafiken. Vehicular Ad-hoc Natverk (VANETs), en delgruppav Mobila Ad-hoc Natverk, ar ett peer-to-peer typ av natverk som kommunicerar information mellan bilar itrafiken. Eftersom natverket ar av typen peer-to-peer maste algoritmer utnyttjas for att valja nasta relafordon.Dessa relafordon for att skicka informationen vidare baserat pa multihop pricipen. Anvandandet av VANET gordet mojligt for enskilda bilar att kommunicera utanfor sin maximala kommunikationsomrade utan att behovabygga eller utnyttja infrastruktur. Bade Institute of Electrical and Electronics Engineers(IEEE) i USA och Euro-pean Telecommunications Standards Institute (ETSI) i Europaomradet har standardiserat V2V kommunikationi sina respektive omraden. MANET algoritmer ar olampliga att anvanda i VANET miljoer da de ar inte artillrackligt adaptiva for att hantera de hoga hastigheterna och snabba natverksforandringar samt de stora skill-naderna i mangden traffik. VANET behover darfor en battre information distribuerings algoritm som snabbtkan overfora informationen med hog tillforlitlighet i leveransfrekvensen.

Denna rapport undersoker en bio-inspirerad losning for forbattrandet av informationsrouting inom VANETs.En svarmbaserad algoritm baserad pa Particle Swarm Optimization (PSO) anvands for att optimisera valet avnasta relafordon. PSOn anvander sig av informationen inhamtad genom mottagandet av Cooperative AwernessMessages (CAM) fran narliggande bilar. Dessa meddelanden innehaller information om grannbilarna. PSOnanvander sig av en lamplighetsfunktion som utvarderar informationen fran grannbilarna. Lamplighetsfunktionenar i sin tur optimiserad med hjalp av en genetisk algoritm som bestammer lampliga vikter av de olika komponen-terna. PSO algoritmen jamfors sedan mot de redan implementerade Greedy Forwarding (GF) och Contention-Based Forwarding (CBF).

Resultaten visar att PSO algoritmen har battre prestanda jamfort med GF i termer av leveranspalitlighet medvaldigt liknande overforingstid. Samtidigt har PSO algoritmen samre leveranspalitlighet jamfort mot CBF menavsevart lagre end-to-end fordrojning. PSO algoritmen ar potentiellt valdigt attraktiv men pa grund av en relativstor variation i end-to-end fordrojning och paketleveransfrekvens maste algoritmen forfinas ytterligare.

III

Master of Science Thesis TRITA-ITM-EX 2018:717

Bio-inspired approach for improving performance ofinformation routing in Vehicular Ad Hoc Networks

Dan NguyenKarl Hansen

Approved: Examiner:

Dejiu ChenSupervisor:

Lei FengCommissioner:

Cybercom Group ABContact person:

Gabriel Andersson Santiago

AbstractAs a key technology for Intelligent Transportation Systems (ITS), vehicle-2-vehicle (V2V) communication en-ables the interconnection of vehicles to limit emissions, decrease congestion, increase traffic safety and muchmore. Vehicular Ad-hoc Networks (VANETs), a subset of Mobile Ad-hoc Networks (MANETs) is a peer-to-peer network among vehicles that is utilized to route information through different forwarding algorithms.Utilizing VANETs it is possible to share information out of communication range without building expensiveinfrastructure. The underlying technology is standardized by Institute of Electrical and Electronics Engineers(IEEE) in the US and by the European Telecommunications Standards Institute (ETSI) in Europe. One of thecurrent problems in VANETs is the poor performance of traditional MANET routing algorithms when appliedto VANETs due to its high speeds, rapid variation in link connectivity and extremely varied density of vehicu-lar nodes in the network. Therefore there exists a need for a better routing algorithm for VANETs which canguarantee a sufficient end-to-end delay and delivery performance.

This thesis seeks to evaluate a bio-inspired approach for improving performance of information routing inVANETs. Specifically, a swarm-based routing algorithm based on Particle Swarm Optimization (PSO) is usedto optimize the Next Forwarding Vehicle (NFV) selection in the routing process. The PSO consists of a fitnessfunction that includes position and mobility information of neighbouring vehicles. The fitness function isoptimized with a Genetic Algorithm that determines the optimal weights of the different components.

The weights are determined from three scenarios on which they are tested and compared to Greedy Forwarding(GF) and Contention-Based Forwarding (CBF). The results show that PSO performs better than GF in terms ofboth end-to-end delay and packet delivery ratio (PDR). CBF achieves better PDR than PSO but at the cost oflarger end-to-end delay. The PSO shows potential as an information routing algorithm but the high variation inits results indicates that refinements are necessary.

V

Acknowledgements

Firstly, we would like to thank our industrial supervisor at Cybercom, Gabriel Andersson Santiago for hisguidance throughout the thesis.

Additionally, we would like to thank our academic supervisor at KTH Royal Institute of Technology, Lei Fengfor his insight on the subject and research aspect of the thesis.

Finally, we would like to thank Raphael Riebl at Technische Hochschule Ingolstadt for his continuous supporton the code frameworks Vanetza and Artery.

Dan Nguyen and Karl HansenStockholm, August 2018

VII

Nomenclature

Acronyms Description

ACO Ant Colony OptimizationBTP Basic Transport ProtocolCAM Cooperative Awareness MessageCBF Contention-Based ForwardingC-ITS Cooperative Intelligent Transport SystemCEPT The European Conference of Postal and Telecommunications AdministrationDEN Decentralized Environmental NotificationDENM Decentralized Environmental Notification MessageETSI European Telecommunications Standards InstituteFSPL Free Space Path LossGA Genetic AlgorithmGBC Geographically-Scoped BroadcastGF Greedy ForwardingGN GeoNetworkingGUI Graphical User InterfaceGPS Global Positioning SystemIPv6 Internet Protocol version 6IEEE Institute of Electrical and Electronics EngineersITS Intelligent Transportation SystemLocT Location TableMAC Medium Access ControlMANET Mobile Ad-hoc NETworkNED Network DescriptionOBU On Board UnitOSI Open Systems InterconnectionOSM Open Street MapOFDM Orthogonal Frequency-Division MultiplexingPDR Package Delivery RateRSU Road Side UnitTCP Transmission Control ProtocolUDP User Datagram ProtocolPSO Particle Swarm OptimizationPV Position VectorQoS Quality of ServiceV2B Vehicle-To-BroadbandV2I Vehicle-To-InfrastructureV2V Vehicle-To-VehicleV2X Vehicle-To-EverythingVANET Vehicular Ad-hoc NETworkWAVE Wireless Access in Vehicular NetworksWGS84 World Geodetic System 1984

IX

Contents

Contents

Chapter 1 Introduction1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Research Question . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.4 Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.5 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.6 Ethical Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.7 Divison of Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Chapter 2 Literature Review2.1 Vehicular Ad-Hoc networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2 Information Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.3 Simulation Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.4 Bio-Inspired Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.5 Parameter determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.6 Previous Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Chapter 3 Implementation3.1 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233.2 Particle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263.3 Fitness Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293.4 Weight Determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.5 Simulation Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333.6 Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353.7 Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Chapter 4 Results4.1 Weight Determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414.2 Test runs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Chapter 5 Discussion & Conclusion5.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Chapter 6 Future Work

Bibliography

Appendices

Chapter A ETSI GeoNetworking Data Structures

XI

Chapter 1

Introduction

1.1 Background

Intelligent Transport Systems (ITS) is the idea of interconnecting vehicles and infrastructure to improve trans-portation in such a way as to limit emissions, decrease congestion and increase traffic safety. This is sometimessimplified to the idea of Vehicle-2-Vehicle (V2V) communication but does expand beyond that. The concepthas been around since the 1980s but has not gained much attention in research and development until the fre-quency allocation and standardization of the technology. The US uses Institute of Electrical and ElectronicsEngineers (IEEE) 802.11p and IEEE 1609 whereas the EU has an equivalent Cooperative Intelligent Trans-port System (C-ITS) set of standards. There are several possible applications, for example a forward collisionwarning system. [1]

The industrial stakeholder of this thesis, Cybercom, is very much involved in the software development andproducts for mass-produced vehicles. Cybercom has developed a proprietary Bluetooth software for advancedvehicle infotainment systems which has been installed in over 30 million vehicles. Cybercom is also a keycontributor in the SCOOP@F joint project between Renault, Peageot, the French gonvernment and the EU [2].SCOOP@F is a pilot deployment project for C-ITS that intends to connect 3000 vehicles with 2000 km of road[3]. Cybercom is currently pursuing its communication software product, OsCar, which is being integratedinto cars in order to enable communication with other cars, infrastructure and other factors in the vehicle’ssurroundings.

Radio-communication has limited range and building infrastructure along the roadside is very inefficient, there-fore there exists a need to use the vehicles as a base for the communication. These vehicles form networks thatneed to be self-organizing and able to scale in proportion to traffic. Vehicular networks are also required tohandle vehicles dropping out of the networks, be it due to failure or not [1]. Networks of this type are calledVehicular Ad-hoc Networks (VANETs), which is a subset of Mobile Ad-hoc Networks (MANETs).

The highly dynamic characteristics of VANETs have a significant impact on the performance of routing al-gorithms. The currently developed routing algorithms for MANETs cannot be applied on VANET efficientlybecause of the rapid variation in link connectivity, high speeds and extremely varied density of vehicular nodesin the network. The algorithms also assume that the network is fully connected and fails to route messages ifthat is not the case. This is a common scenario in sparse networks which will occur when only a few vehiclesare equipped with the communication hardware. A key challenge is to guarantee a sufficient end-to-end delayand delivery performance in a varying sized network. [4]

1

CHAPTER 1. INTRODUCTION

Bio-inspired engineering is a discipline in which concepts observed in nature are applied to engineering so-lutions. There are several fields in which bio-inspired engineering has been applied, such as mechanical en-gineering, robotics, communication, optimization and computer science. Swarm intelligence is a subset ofBio-inspired engineering that deals with systems composed of many self-organizing and relatively homoge-neous entities that coordinate using decentralized control. The overall behavior of the system results frominteractions between the entities and their environment, which are based on a set of simple behavioral rules thatonly exploits local information. Swarm intelligence shows great promise in its application on information rout-ing networks. Swarm-based protocols are often inspired by large-scale societies, such as ant societies, whichmakes them more scalable as compared to conventional routing algorithms. They are also characterized by itsself-organizing nature which increases the routing robustness against link or node failures [5].

1.2 Purpose

Due to the frequent disconnections and ever-changing topology in vehicular traffic, MANET algorithms forinformation routing are unable to cope with the requirements of today. The purpose of this thesis is to explorebio-inspired approach for information routing algorithms as a possible solution to the current problems inVANETs regarding information routing.

1.3 Research Question

This thesis seeks to evaluate how swarm-based algorithms may improve information routing in VANETs. Thiscan also be described in the research question:

How does the implementation of a swarm based information routing algorithm improve end-to-end delay andpacket delivery rate in VANETs as compared to existing routing algorithms?

Routing algorithms greedy forwarding (GF) and contention-based forwarding (CBF) defined in the Europeanstandards is used as reference. The algorithms are tested on several different scenarios including a suburbanenvironment, a city environment and a highway environment. Typically vehicular communication is utilizedfor safety application which puts time constraints on the network. The network is therefore analyzed with theend-to-end delay and packet delivery ratio.

1.4 Scope

The scope of this thesis is limited to the following points.

• The results are limited by the modeled behavior of the simulation environment and thus the conclusionsmay most likely not be directly transferable to a real-world scenario.

• The radio propagation is not affected by obstacle loss or ground reflections.

• Antennas are assumed to be isotropic, meaning that the radio signal radiates with the same intensity ofradiation in all directions.

• The implementation is limited by the functionality and flaws of existing implementations of standardsfor vehicular communication.

• This thesis does not consider any security aspects in the implementation.

2


1.5 Method

In order to evaluate the research question an evaluation strategy is developed.

First and foremost a swarm-based information routing algorithm has to be implemented. This algorithm shouldbe designed for VANET purposes and should be based on either the American or European standards. It shouldimplement Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) and should not include any Vehicleto Broadband (V2B) functionality. This would entail design in the third and fourth layers according to theOpen Systems Interconnection (OSI)-model, while also providing an interface for higher layer applications. Ageographical mesh topology will be utilized in the design of the swarm-based routing algorithm. The swarm-based routing algorithm will be based on the results of the Literature Review.

An environment for the system to operate in is needed for evaluation. A fully integrated network and trafficsimulator would be advantageous as traffic has to be generated by a realistic traffic simulator and this gives riseto network topographical changes that need to be simulated by a realistic network simulator. Additionally inorder to test any routing or networking algorithm the simulator needs be able to analyze the relevant param-eters. Unfortunately no such simulator has been found. Fortunately, there exists viable alternatives. One cancategorize the different simulators into two categories for the purpose of this thesis. First there exists simulatorswhich are primarily traffic simulators but with V2V communication. Secondly there exists simulators whichare primarily network simulators but with some traffic simulator component. A prestudy of the simulationenvironment is described in-depth in Section 2.3.

Due to the simulation focus of this project a research methodology based on quantitative data gathering issupported. An experimental method is therefore chosen, which serves to determine the effect of the implemen-tation. The swarm algorithm can then be compared to other routing algorithms.

1.6 Ethical Considerations

The thesis will be executed on a small scale and in a simulated environment where the information sharedbetween vehicles does not cause any ethical harm. However, if the research contributes to commercializingVANETs, challenges within security, privacy, anonymity and liability are introduced[6].

The vehicular network is threatened by false messages which generate requirements on the network regardingsecurity and consequently the privacy. There is trade-off between securing the network with message authenti-cation and the sender’s goal of being anonymous to avoid being tracked. Although vehicles benefit from sharedinformation by increased efficiency and safety, acceptance might be lowered if that information can be used forissuing speed tickets[1]. On the other hand if the communication is not secure the network can be sabotagedwith false messages which would cause danger in traffic. The network must be developed to accommodate bothparties.

There are also challenges regarding the liability of executing commands on shared information. In case of anaccident it is not obvious if the information source, driver or the software developer is to be held responsible.If the mentioned vehicle is also autonomous, the ethical issue becomes even more complex.

3


1.7 Divison of Work

All parts of this thesis are intertwined and both authors contributions are represented throughout all parts ofthe implementation and the report. A rough division would be that Dan worked on the PSO and Karl workedon the genetic algorithm. Both authors worked equally on the simulation with the development of use-case,storyboard and scenarios. The simulation environment was setup by Karl and the results extracted by Dan.

4

Chapter 2

Literature Review

2.1 Vehicular Ad-Hoc networks

Vehicular Ad-hoc Networks (VANETs) can best be described by breaking down the term into its parts. Vehic-ular indicates that the networks are to be used in a vehicular traffic. Ad-hoc indicates that the networks operateon a peer-to-peer basis without any form of access point. This means that the connections are temporary andonly the surrounding nodes are known for any given node. Networks mean that the nodes are interconnected.

Due to the ever-changing nature of vehicular traffic VANETs are highly dynamic networks with frequent dis-connections and topological changes. VANETs also have a varying density of vehicles ranging from scarcetraffic on countryside roads to rush hour in an urban environment. VANETs do not require any infrastructurealong the roads as direct communication via radio is possible. However, while no additional infrastructure isrequired VANETs have the possibility to communicate with Road Side Units. [7]

Communication in traffic can be categorized into four main types of communications: vehicle-to-vehicle(V2V), vehicle-to-infrastructure (V2I), vehicle-to-broadband (V2B) and in-vehicle. V2V refers to the com-munication between vehicles which mainly concern safety applications. These are time sensitive and have ahigh priority [8]. V2I communication is between the vehicle and infrastructure which is connected to the widearea network to provide the vehicle with real-time traffic information, road conditions etc. This is useful foractive driver assistance and vehicle tracking. V2B communication gives access to wireless broadband mecha-nisms, such as 4G, which may include more traffic information as well as infotainment. The different V2 partsare sometimes grouped together and called V2X, vehicle-to-everything. Finally, In-vehicle communicationrefers to the system within vehicle that can detect the vehicle’s performance and the driver’s physical statussuch as fatigue. [9]

VANETs enable vehicles to share information which can be gathered and processed for safety and non-safetyrelated applications. Due to the fast movement of vehicles, safety-related VANET applications require messagesto have low delays, high reliability and high security. Messages typically contain the vehicle’s location, speedand other information that creates awareness for other vehicles. This information is utilized to avoid intersectioncollisions, notify post-crash situations, extend signs and more. Non-safety-related applications are usuallycomfort and commercial applications such as improving traffic efficiency and passenger comfort as well aslocating points of interest. In order to avoid interference and distraction from the more important safety-relatedapplications, the physical network channels are separated.[6]

2.1.1 Radio Propagation

The purpose of this section is not to fully explain the phenomena of radio but rather to give a very basicoverview on how radio behaves in relation to this project.

Radio signals are electromagnetic waves that travel through space. The electromagnetic waves are influencedby how far it travels, background noise, obstacles, interference and more [10]. These phenomenons makes thesignal not fully predictable.

5

CHAPTER 2. LITERATURE REVIEW

Typically electromagnetic waves are produced at the transmitter (Tx) which are interpreted at a receiver (Rx)end. Naturally, the attributes of the transmitter and receiver as well as how the signal changes over time anddistance are important to consider [10]. The transmit power is the power that the transmitter broadcasts. Thereceiver sensitivity is the minimal signal strength at which a signal can be discerned. The power of radio signalsare often expressed in milliwatt (mW) or decibel with reference to one milliwatt (dBm). Equation (2.1) can beused to convert between the two units.

P (dBm) = 10log10(P (mW )) (2.1)

The radio signal attenuates as it travels through air and obstacles which is often referred to as path loss. Thepath loss is affected by the frequency of the radio waves and generally a lower frequency results in less pathloss and therefore longer communication range. It is apparent that these parts affect the communication range,but there exists different models to predict the path loss and consequently the communication range.

Free Space Path Loss (FSPL) formula, see equation (2.2), calculates the attenuation through air without passingthrough any obstacle in line-of-sight. FSPL depends on radio the frequency f, the speed of light c and thedistance between Tx and Rx d [11].

FSPL =

(4πdf

c

)2

(2.2)

The equation is only accurate in far field situations and not close to the Tx.

Two-Ray Ground Reflection predicts path loss in a line of sight with an additional multi-path component toinclude the interference from ground reflections. The path loss is equal to the FSPL before the crossoverdistance which is where the wave reflects off the ground. The crossover distance is determined by the altitudeof the Tx and Rx antenna, which then evidently affects the path loss after the crossover distance.[11]

If an obstacle is in line of sight of the Rx and Tx, the signal experiences diffraction losses. Diffraction refers tovarious phenomena that occur when the wave encounters an obstacle, including waves bending around corners.Diffraction is complicated and it is not trivial to predict the losses caused by obstacles. One model is Fresnel’sequation for Knife edge diffraction which can be used when the line of sight is obstructed by a single knife-edged obstacle. The wave is then modeled to bend over the obstacle leading to a longer diffraction path. Withan obstacle height hm and the signal wavelength λ in meters a diffraction parameter ν is defined in equation(2.3)

ν = hm

√2

λ

(1

dT+

1

dR

)(2.3)

where dT and dR is the distance between the obstacle and Tx and Rx respectively. The diffraction attenuationAd is then expressed in dB with eq (2.4)

AD =

0 ν = 0

6 + 9ν + 1.27ν2 0 < ν < 2.4

13 + 20logν ν < 2.4

(2.4)

For obstacle loss, one can also take into account the dielectric losses in the intersected obstacle, which is theenergy dissipation due to the electric field of dielectric materials. The material characteristics of the obstacle isthen considered for the signal attenuation. [11]

Another concept in wireless communication is fading, which is the variation of attenuation of a signal withvarious variables. Fading is a result of the presence of reflectors in the surrounding environment which createsmultiple paths which the transmitted signal can traverse. This can amplify or attenuate the signal power at theRx depending on different factors. Rician Fading is a stochastic model which treats the strongest signal of amulti-path signal according to a Rician distribution. That signal is often a line-of-sight signal. Rayleigh fading

6


is another stochastic model which specializes on multi-path signals without any dominant signal and then treatsthe signal according to a Rayleigh distribution. [11]

Background noise is unwanted electric signals always present at the Rx. The background noise is a combinationof natural electromagnetic noise in the atmosphere, radio interference from other devices, thermal noise fromthe Rx circuits and more. The received signal has to be more powerful than the average background noise inorder to be distinguishable. The background noise therefore determines the maximum sensitivity and receptionrange of the Rx. [11]

2.1.2 The IEEE 802.11 standards

The IEEE 802.11 are the family of standards for MAC and PHY-layers (according to the OSI-model)[12] inthe implementations of Wireless Local Area Networks (WLAN). The first standard was released in 1997 whichhas the base name of IEEE 802.11. However, this standard quickly proved inadequate for most applications asthe bandwidth was limited to 2 Mbps while operating in the 2.4 GHz frequency range. IEEE 802.11 is nowdeprecated. In 1999 IEEE 802.11a was released with a theoretical transfer rate of 54 Mbps while utilizing 5 GHzband. The IEEE 802.11a standard is a direct continuation of the IEEE 802.11 standard but with more complexmodulation allowing for higher transfer rates. The IEEE 802.11a standard also had the possibility to implementad-hoc networks, that is networks that do not need an access-point but establishes peer-to-peer network. Dueto manufacturing issues the 802.11a standard was not as popular as compared to the IEEE 802.11b standardin the early to mid 2000’s. IEEE 802.11b was approved by the American National Standards Institute in 2000and quickly grew in popularity. The IEEE 802.11b standard operates on the 2.4 GHz frequency range and hasa theoretical transfer rate of 11Mbps. The technology of Wi-Fi utilizes IEEE 802.11a/b and other IEEE 802.11standards to create WLANs and other types of networks. [13][14]

In 2010 the IEEE 802.11p standard was approved. The IEEE 802.11p standard is the standard which is designedfor the establishment of VANETs and other wireless communication solutions in a vehicular environment. Thestandard is limited to the MAC and PHY specifications and is a continuation of the IEEE 802.11a standard, withspecifics changed for better performance in a vehicular environment. Messages are short duration, designed tooperate at short range and at high speeds[4]. A frequency spectrum in the 5.9 GHz band has been allocated forITSs, based on IEEE 802.11p, in various regions, including Europe and the US, see Figure 2.1.

Figure 2.1: Frequency spectrum allocation for ITS in the U.S and Europe [15]

The frequency allocation of 70MHz is specified in Europe by the European Telecommunications StandardsInstitute (ETSI) in the ITS-G5 standard. The spectrum is divided into 10 MHz bandwidth channels whichamounts to seven channels in total, three channels dedicated for safety related applications, two for non-safetyrelated applications and two for future applications, see Table 2.1.

Table 2.1: Frequency allocation in the ETSI standard[16]5,855-5,875 GHz 2 Non-safety related application channels5,875-5,905 GHz 3 Safety related application channels5,905-5,925 GHz 2 Future application channels

The smaller channel bandwidth, compared to the 20MHz channels for IEEE 802.11a, and the frequency-division of the physical layer specified in IEEE 802.11p results in data rates from 3-27Mbps per channel[1].

7


Lower rates are beneficial in that a more robust communication can be established. The allotted frequenciesare higher in IEEE 802.11p than in IEEE 802.11a and separated such that regular devices do not interfere withsafety critical systems. IEEE 802.11p utilizes the same modulation as IEEE 802.11a (Orthogonal frequency-division multiplexing, OFDM).[17] [14]

In the US, 75MHz is allocated by the Federal Communication Commission in the 5.850-5.925 GHz. Theallocation is similar to the European standard in which the spectrum comprises of seven channels of 10MHzbandwidth and a 5MHz reserve (the reserve is allocated at the bottom of the frequency band, 5.850-5.855 GHz).Four channels are used as service channels, one as a control channel, one for critical safety of life and the lastchannel for high power public safety, see Figure 2.1. While the frequency range allocated in both Europe andthe US are almost identical, the channel allocation is different. In the US the higher layers of the communicationstack is controlled by the IEEE 1609, also referred to as Wireless Access in Vehicular Environment. While inthe EU the IEEE 802.11p standard has been incorporated into the ETSI ITS-G5.[18]

2.1.3 Wireless Access in Vehicular Environment

Wireless Access in Vehicular Environment (WAVE) defines the architecture that enables wireless communica-tion in VANETs. An overview of the layered architecture in WAVE, in regard to the OSI-model, is shown inFigure 2.2.

Figure 2.2: WAVE standard in relation to layers in the OSI model[19]

Vehicles in VANETs are equipped with On Board Units (OBUs) which allow short-range wireless communi-cation with other OBUs and Road Side Units (RSU) using IEEE 802.11p [6]. The upper layers of WAVE arestandardized according to the IEEE 1609[20]. IEEE 1609 consists of a family of standards that is developed towork with IEEE 802.11p. IEEE 1609 describes the middle layers, namely the MAC-, Network- and Transportlayers of the OSI stack. IEEE 1609 provides two parallel stacks on top of IEEE 802.11p, one for User Data-gram Protocol/Transmission Control Protocol (UDP/TCP) over Internet Protocol version 6 (IPv6) and the othercalled Wave Short Message Protocol (WSMP). WSMP is used to send short messages in time-critical safetyapplications, whereas IPv6 is used for less demanding applications. This dual stack architecture is describedin IEEE 1609.3 which specifies the services, in the network- and transport layer, that assists in establishingV2V and V2I communication. IEEE 1609.4 enables multi-channel communication between WAVE devicesby defining supporting MAC sublayer services and functions. IEEE 1609.2 secures applications by specifyingmessage formats and the corresponding processing by other WAVE units. Finally, IEEE 1609.1 is the resourcemanager which enables applications to establish communication with other vehicle’s OBUs and RSUs. [19]

8


2.1.4 ETSI C-ITS

The European set of standards was first released in 2013 by the European Telecommunications StandardsInstitute (ETSI) and comite Europeen de Normalisation (CEN) in close liaison with International Organizationfor Standardization (ISO). The focus in Europe has been on V2X communication, also referred as cooperativeITS (C-ITS), but includes all means of transportation including rail, water and air transport. An overview ofthe layered architecture of the European C-ITS standardization, in regard to the OSI model, is shown in Figure2.3.

Figure 2.3: The European C-ITS standards in relation to the OSI model [21]

Vehicles and roadside units are referred to as ITS stations which are equipped with OBUs which allow short-range wireless communication with other ITS stations using ETSI ITS-G5 radio technology in the 5.9 GHzfrequency band. ETSI ITS-G5 is based of the American WAVE and adapted to European requirements. Likeits US counterpart it utilizes the IEEE 802.11p standard. ETSI ITS-G5 functions primarily at the physical-and Medium Access Control (MAC) Layer and is developed to accommodate the characteristics of a vehicularnetwork by providing wireless nodes to communicate through short-duration messages, necessary between highspeed vehicles[4] [21].

The European C-ITS provides two parallel stacks in the networking- and transport layer. One stack consistsof GeoNetworking and Basic Transport Protocol (BTP) which is used for ad-hoc communication over ITS-G5utilizing geo-addressing. The other stack is used for IP-based communication with UDP/TCP over IPv6. GN6is a sublayer in the same middle layers which enables IPv6 packets to be transmitted over GeoNetworking. [21]

GeoNetworking (GN) is a routing protocol that utilizes geographical positions for addressing and forwarding.GN supports five packet handling modes: geo-unicast, geo-broadcast, geo-anycast, single-hop broadcast andtopologically scoped broadcast. Geo-unicast facilitates sending a packet to an individual ITS station with itsgeographical position. Geo-broadcast and geo-anycast enables sending a packet to a geographical target areadescribed by its geometrical shape (circle, rectangle or ellipse). Single-hop broadcast and topologically scopedbroadcast does not have the geographical addressing. GN does not establish and maintain routes, instead itenables forwarding of packets. Several algorithms are specified in the standard: greedy forwarding, simplegeo-broadcast, contention-based forwarding and advanced forwarding [21]. A GN package is composed ofthree headers whereas the basic and common headers carry fields needed by all packet types and the extendedheader is used for geographic-specific fields. BTP lays on top of the GN protocol and provides the end-to-endpacket transport similar to UDP.

9


On top of the network and transport layers, the facilities layer protocols are specified which comes with twomessaging protocols Cooperative Awareness Message (CAM) and Decentralized Environmental NotificationMessage (DENM). CAM is a periodic message that conveys critical vehicle state information to neighboringITS-stations over a single hop for traffic efficiency and safety applications. The CAM message is composedof an ITS PDU header which specifies protocol version, message type and sender address. It also consistsof several containers which appears with varying frequency to minimize the message size. A high-frequencycontainer is sent in every CAM and carries the vehicle’s kinematic data such as heading and speed. The low-frequency containers contains data with lower safety relevance or data of larger size such as path history. Thespecial containers are optional and could contain information about road works, dangerous goods and publictransport. The frequency of which a message can be sent is between 100 ms and 1 s. DENM protocol sendsevent-driven safety information messages over several ITS-G5 hops to a geographical area. It assigns an actionidentifier, unique to the ITS station, when a safety situation is detected. The messages consists of an PDUheader, management container with fields for action identifier, detection time and event position. The optionalcontainers are the situational container which describes the event by a predefined code, the location containerwhich contains fields regarding event spread, heading and trace as well as the a la carte container which containsapplication-specific content such as lane position and road works. Further message types are being standardizedfor vehicle-to-infrastructure communication.

Applications are not fully standardized, instead minimum requirements for three groups of applications: roadhazard signaling (RHS), Intersection collision risk warning (ICRW) and longitudinal collision risk warning(LCRW). RHS brings up 10 different use cases including emergency vehicle approaching and hazardous loca-tion. ICRW and LCRW refers to potential collisions at intersections and rear-end as well as head-on collisions.Current use of this technology could not be found as of writing this report. However, many automotive com-panies, including Volkswagen Group, are interested in the technology and has plans for deployments in thecoming years [22]. Additionally, SCOOP@F is a EU-driven pilot deployment project in collaboration withPeugeot and Renault which aims to connect approximately 3000 vehicles with 2000 kilometres of roads [3].

10


2.2 Information Routing

This section gives a basic overview of information routing. The routing protocols are categorized into Topol-ogy based and Geographic-based protocols. Different communication paradigms are described followed by adefinition and discussion of Quality of Service (QoS) and finally a State of The Art analysis of swarm-basedrouting algorithms is presented.

2.2.1 Traveling salesman problem

The travelings salesman problem is an infamous optimization problem within the field of optimization andcomputer science. The problem is based on the idea of a traveling salesman. The salesman wants to visit everycity in the country to sell his goods. The salesman also wants to optimize his route between the cities he isgoing to visit. Every city is connected to a number of other cities using train. However, the salesman wouldprefer not to revisit any city as that would be a waste of time. What would be the shortest route to achieve this?The problem is of type NP, non-deterministic polynomial and is very similar to a routing problem. However,now consider that the salesman cannot plan his route because he cannot see the trains leaving from any othertown than the one he is in. This is the kind of problem that an ad-hoc network needs to deal with. The salesmanneeds a strategy that still takes him around the country.

2.2.2 Topology-Based

Topology-based routing protocols use the link information to transfer data packets where the route is establishedthrough control packets prior to data transmission. They can be categorized into proactive, reactive and hybridprotocols.[19]

Proactive

Proactive (or table-driven) routing algorithms establishes routes based on shortest path algorithms, which aresaved with the associated nodes in a table. The tables are shared between the neighboring nodes and updatedperiodically. Although the protocol achieves low latency, it does not respond well to link failures and the tablemaintenance occupies a major part of accessible bandwidth by maintaining routes. [19] [4]

Reactive

Reactive (or on-demand) routing protocols establishes routing path by route requests and maintains solely theroutes that are in use. It is not suitable for highly dynamic networks as route discovery is time consuming andresults in high latency [19] [4]. Ad-hoc On Demand Distance Vector (AODV) and Dynamic Source Routing(DSR) are examples of reactive routing protocols[23].

Hybrid

Hybrid (or hierarchal) considers proactive and reactive mechanisms. The nodes are divided into intra-zoneswhere information routing is proactive and inter-zones where protocols are reactive. The hybrid solution mini-mizes routing overhead and delay but do not work well under high mobility conditions and frequent topologicalchanges. [19] [4]

11


2.2.3 Geographic-Based

Geographical routing protocols utilizes geographical location information obtained from maps, traffic modelsand/or navigational systems in vehicles to make routing decisions. It is in recent advancement and adoptionof Global Positioning System (GPS) that it has been identified as a promising routing technique. It has nowbecome the most often used approach for information routing in VANETs[24]. Unlike topology-based routing,it does not maintain routing tables and the next best hop is selected based on location related information. Datapackets can therefore be forwarded without any prior knowledge of the network topology or route discovery.Geographical routing protocols efficiently routes even in highly mobile conditions. [19][23]

Geographic-based routing protocols can be categorized by whether nodes use beacon messages. Protocols aresender-based since it knows its immediate neighbors to which it is optimal to forward to. This is due to theuse of beacon messages which periodically exchange information among neighbors. Beaconless protocols arereceiver-based and generally have a forwarding and waiting time criteria. The receiver node participates inthe ongoing communication when the forwarding criteria is satisfied, they are the next hop forwarder basedon timed criteria[19]. A beacon-based routing algorithm is Greedy Perimeter Stateless Routing (GPSR) whichforwards to the node closest to the final destination by using a beacon.

2.2.4 Communication Paradigms

Unicast provides one-to-one communication where target node location is known and in range of communica-tion through single or multi-hop[4]. Unicast protocols have low network overhead and packet delay[7]. This isalso often described as peer-to-peer.

Multicast provides one-to-many communication where one node can communicate with a group of targetnodes identified by common destination address [4]. Multicast routing can utilize cluster heads, which areselected for a group of nodes in the network, to communicate inside and outside of a cluster[7]. Inter-clustercommunication is performed via direct links while intra-cluster communication is established through routesbetween cluster-heads. The cluster-based approach aims to ensure scalability.[24]

Geocast is a specialized form of multicast where nodes within a geographical distance, Zone of Relevance(ZOI), are able to receive geocast messages.[4] In multi-hop forwarding, the Zone of Forwarding (ZOF) speci-fies all the potential next hop nodes of the current forwarder node. [25]

Anycast is a form of multicast where a node sends messages to any destination node within a group of nodes.[4]

Geographical Anycast is a specialized form of anycast where a node sends messages to a certain geographicarea to request data from any node found in that area. [4]

Multipath Routing find multiple paths between source and destination to make connections more robustagainst burst traffic, fault and higher aggregated bandwidth. The components of multipath routing are routediscovery, route maintenance and traffic algorithms. An example of a multipath routing protocol is the modi-fied AODV, Ad hoc on-demand multipath distance vector. [7]

12


2.2.5 ETSI Forwarding Algorithms

The European standards include several forwarding algorithms, greedy forwarding (GF) and contention-basedforwarding (CBF) are two of them. GF is a geographic-based unicast forwarding algorithm that uses beaconmessages in the form of CAMs. The algorithm selects the neighbour with the smallest geographical distanceto the destination which provides the greatest progress when the packet is forwarded, see Figure 2.4. It isimplemented without any recovery strategy which means that the GF can lead to a dead-end. In case of adead-end the packet is broadcasted if buffering is not enabled.

Figure 2.4: Greedy forwarding algorithm

CBF on the other hand is beaconless and is not dependent on CAMs. It is a re-broadcasting forwarding al-gorithm where the receiver vehicle decides to be the forwarder through a timer-based system. The senderbroadcasts a packet and the receiving vehicles starts a timer with a timeout based on the forwarding progress ofits position. Upon timer expiration, the vehicle re-broadcasts the packet. Before the timer expires, the vehiclemay receive a duplicate of the packet from a vehicle with shorter timeout. In this case the packet is forwardedby another vehicle and the packet is dropped. CBF has an implicit reliability mechanism at the cost of largerforwarding delay and additional processing.[18]

2.2.6 Quality of Service

Quality of Service (QoS) is the ability to satisfy certain services needed for certain applications. In the contextof VANETs, QoS ensures timely reception of safety messages and efficient dissemination of non-safety mes-sages. This means sending data with minimum end-to-end delay and routing overheads. In addition, it alsoimplies ensuring successful delivery of the maximum number of transmitted messages while optimally usingthe available network bandwidth. [24]. Typical QoS metrics are listed below.

• Average end-to-end delay is the average time (in milliseconds) for data packets to reach the destinationnode. It includes all possible delays such as route discovery latency, re-transmission of dropped data

13


packets, etc. It is calculated as the time from the source successfully sending the first packet to thedestination node receiving it.

• Packet delivery ratio (PDR) represents the percentage of successfully received packets. It specifies thepacket loss rate caused by dropped packets due to router failure, data corruption, arrival when buffersare full or by there simply being no route to the desired destination. It is calculated by dividing numberof packets received by the destination node through the number of packets originated from the sourcenodes.

• Average jitter represents the average variation (in milliseconds) in delay during different transmissions.

• Average available bandwidth expresses the data transmission speed and the maximum data transmittedin a time unit by computing the total number of delivered data packets divided by the total duration ofsimulation (kbps).

• Normalized overhead load evaluates the extra bandwidth consumed by overhead to deliver data. This isan important metric as the size of routing packets may vary. It is calculated by the total number of routingpackets divided by total number of delivered data packets.

14


2.3 Simulation Environment

No system can be evaluated unless it has an environment to operate in. A fully integrated network and trafficsimulator would be advantageous as traffic has to be generated by a realistic traffic simulator which gives rise tonetwork topographical changes that need to be simulated by a realistic network simulator. Additionally, in orderto test any routing or networking algorithm the simulator needs to be able to analyze the relevant parameters.Unfortunately no such simulator exists. Fortunately, there exists viable alternatives.

One can categorize the different simulators into two categories for the purpose of this thesis. Firstly, thereexists traffic simulators with V2V communication. Secondly, there exists network simulators with some trafficsimulator component.

2.3.1 PreScan

Traffic simulators are rapidly becoming more and more complex. Simulators such as PreScan are able to createelaborate traffic situations and utilize large numbers of variables such as computer vision, super sonic sensorsand V2V communication. By interfacing with MATLAB/Simulink it is also possible to design behaviors basedon the information received. The PreScan simulator implements some very advanced features such as reflec-tions made by headlights during nighttime driving. Meanwhile simulations are easy to setup providing the userwith an easily understandable user interface for scenario generation, see Figure 2.5. While combining PreS-can with ViSim it is also possible to generate simulated every day traffic where entities pass in and out of thesimulation with a randomized path. [26]

Figure 2.5: User Interface for PreScan

PreScan is very easy to automate for testing utilizing Python for easy scripting and can also be compiled toprovide a 3-D visualization. However, while there exists a V2V communication protocol it is very limited inits features. There exists no possibility for peer-to-peer communication. Additionally there exists no tools forGeoNetworking or other networking of any kind. The antenna model is simplified to an isotropic antenna whichis idealized as omnidirectional. [26]

15


2.3.2 Veins & Artery

Vehicles in Network Simulation (Veins) is an open source vehicular network simulation framework that im-plements the American IEEE 802.11p and IEEE 1609.4 WAVE standards. Its features include multi-channeloperations, noise and interference effects. The Veins framework interfaces with the event-based network simu-lator OMNeT++ and the road traffic simulator Simulation of Urban MObility (SUMO). With the use of modulesthe different layers of the OSI-model can be simulated. Users can write applications in C++ which are thenused in the higher level Network Description (NED) language to which a graphical user interface (GUI) is usedfor easy modeling. This enables user specific applications to be evaluated while protocols and underlying OSI-layers can be imported. Veins also ships with a number of MANET and VANET algorithm. Veins is greatlyutilized in academic papers [27] [28].

While setting up a SUMO simulation there are two possibilities for generating traffic maps. Either one drawsthem oneself with SUMO’s tools or one import maps from Open Street Map (OSM). Regardless of which ofthese alternatives the map will be represented as XML. In fact the map will be represented in a couple of XML-files, with the net.xml-file being the most important as it represents the road network. The net.xml also includesthe speed limits, one-directional-streets and traffic lights. However, one still needs to generate traffic. Thisis either done while getting the OSM, which is what the developers suggest, or by generating random trafficutilizing a script. Using a webwizard, used for getting the OSM, one can determine the density of traffic basedon real-world data. Traffic density is typically referred to car/(kilometer of road). The traffic is also stored asXML. One can add custom cars to the traffic by adding a XML-data element, where one can determine route,color of car and name of car.

Artery is a continuation of the Veins project which extends OMNeT++ to be used with Vanetza, which is anopen-source implementation of the European ETSI C-ITS standards. Vanetza provides GeoNetworking, BTPand other features included in the standards. Artery and Vanetza is part of an ongoing research project atTechnische Hochschule Ingolstadt in Germany and the development is coordinated by Raphael Riebl [29]. Theproject interactions are described in Figure 2.6.

Figure 2.6: Dependencies of the Artery project [30]

Artery also utilizes CMake and additional tools to make CMake aware of the .NED files such that .NED files,.cpp files as well as .h files are included in the linking and eventual compilation of projects. [30]

Since the European and the American standards are based on the same physical and MAC-layer, Artery can

16


utilize the physical and MAC-layers of Veins. However, it also supports physical and MAC models fromINET which is an open-source model library for OMNeT++ which includes models for wireless link layerprotocols such as the IEEE 802.11. INET models are also used outside of the vehicular communication domainand include scalable level of detail for physical layer with different propagation models. Artery adapts theINET link layer model to represent the link layer used in the European standards. INET is maintained by theOMNeT++ team. Artery imports different modules to utilize either the Veins or the INET for the underlyingbehavior. Both frameworks can be used to model radio propagation, interference and obstacle shadowing withdifferent levels of complexity. [31]

While not as easy to setup for automated testing when compared to PreScan it is possible to setup automatedtests, with randomization parameters, in both Artery and Veins by simply utilizing scripting.

2.3.3 Conclusion

Due to the limitations of PreScan in the area of V2V communication and specifically the lack of peer-to-peer communication makes the program unsuitable for the purposes of this report. The authors of this reportspeculate that it should be possible to implement a Not-network of sorts for peer-to-peer communication thatexcludes information rather than share it. However, such a system does not fall within the scope of report.

The capabilities of the Artery and Veins programs offer a sufficient toolbox for design and testing. The addedcomplexity of the underlying OSI-layers also provides credibility in the highly complex VANET environmentwhile requiring limited understanding of the underlying phenomenons.

17


2.4 Bio-Inspired Optimization

Optimization is the process of finding the best available value of a function given a defined domain or input.Optimization methods are used to solve quantitative problems in many different disciplines including physics,engineering and biology. The optimization problems typically have three fundamental elements: a function tobe optimized, a collection of variables which can be manipulated and a set of constraints which are restrictionson the values that the variables can take[32]. In this thesis several bio-inspired optimization algorithms such asAnt Colony Optimization (ACO) and Particle Swarm Optimization (PSO) has been explored. In this literaturereview PSO is presented.

PSO was introduced in 1995 by J. Kennedy and R. Ebehart[33] as an optimization method for non-linearfunctions inspired from artificial life, in particular bird flocking, fish schooling and swarming theory. Thealgorithm considers a swarm with an arbitrary number of particles, representing birds, with the three simpleattributes: position, velocity and fitness. The velocity corresponds to the particles’ ability to move to the bestposition associated with a fitness value, i.e. quality of solution. The fitness value is the calculated result of afitness function(any function) to be optimized. The particles move to the optimal position in problem space byutilizing information from the swarm.

PSO is an iterative process where the swarm of particles are randomly initialized with the position and velocitybeing uniformly distributed in the problem space. In each iteration the individual particles calculates a personalbest fitness value (pbest). In addition the swarm keeps track of the global best fitness value (gbest) which is thebest fitness value achieved by any particle in the swarm. The particles then uses these record values, pbest(x, y)and gbest(x, y), to calculate its new velocity vel(x, y)

vel(x, y) = velold(x, y)+c1·rand()·(pbest(x, y)−position(x, y))+c2·rand()·(gbest(x, y)−position(x, y))(2.5)

where position(x,y) is the particle’s current position, c1 is a learning factor for the individual particle and c2 isa learning factor for the entire swarm. rand() is a random function generating numbers uniformly distributedbetween 0-1. [33] proposes c1 = c2 = 2 which results to the term having a mean value of 1 with the randomfunction.

The particles’ new velocity is then used to calculate the position

position(x, y) = position(x, y) + vel(x, y) (2.6)

The PSO iterates until the maximum iteration count is exceeded or if the improvement of the global best fitnessvalue (gbest) from previous value is less than a minimum threshold value. The final gbest is the PSO solutionwhich represents the best location in problem space for the given fitness function.

18


2.5 Parameter determination

Regardless of what system that is designed a pragmatic approach is to design the system such that the differentvariables have weights attached to them. The purpose of the weights is to determine to what extent each variableaffects the final system. Combining the weights with a reasonable estimation of the maximum value the effectof each variable is limited to the weight. This also allows for weights to be limited by −1 ≤ Wi ≤ 1, inwhich a negative influence is denoted by a negative value, while a positive influence is denoted by a positivevalue. Naturally, this means that the maximum values need to be properly estimated before tests are performed.Failing that, if a value converges to 1, one should increase the maximum value (to allow for more influence onsystem) and restart simulation.

Since the nature of VANET is ever changing topology and frequent disconnects it is advantageous to simulatethe system. The simulation should be able to output QoS results. It is advantageous to first optimize for QoS,for example PDR, and then try this solution to see the behavior of different QoS, for example end-to-end delay.Trying to optimize for two or more QoS would quickly add complexity to a system which is already complex.

Using a stochastic approach with simulation to determine a weight solution that gives at least a local optimum.It might be counter-intuitive to not require the global optimum but remember that the parameter set will beoptimized for one QoS and then tested for another QoS parameter. Naturally the optimization should try to finda global optimum but this is not a requirement. A solution that performs well in all measured QoS is what isdesired.

2.5.1 Simulated Annealing

Simulated annealing is a heuristic algorithm that is a thermodynamically-inspired that simulates the controlledcooling of fluids to create crystals. The algorithm is often used in combinatorial optimization but is used in awide range of application areas.

In simulated annealing there are two main concepts, energy and temperature. Temperature can be seen as thewillingness of the system to try exploring the solution space X , even though it may in fact generate worseresults. This is done to find the global optimum or at least a local optimum that is superior to previouslyfound optimum. The second concept is the energy (E), which is the expenditure to move in the solution space∆E = Eold−Enew. Within the solution space moves can be either in the upward direction (∆E > 0) or in thedownward direction (∆E ≤ 0). As previously mentioned temperature is the willingness to explore the solutionspace, however this is only an issue in the upward direction since any downward movement will always beaccepted. The likelihood that a move in the upward direction will be accepted is x∆E where x is a controlparameter described by the temperature as x = e−1/T . The temperature is then lowered depending on time oriterations. Figure 2.7 visualizes the concepts. [34][35]

Simulated annealing is used within the field of telecommunications and specifically within information routing.Simulated annealing can be used to solve the traveling salesman problem and other similar routing problem. Infact, simulated annealing could be considered a good approach for the whole problem of information routingin VANET if one had all information of every node in the network at any time. However, due to the natureof ad-hoc networks this is rarely the case and more importantly the required bandwidth to uphold the entirenetwork would scale extremely poorly in dense traffic. Remember, the radio-spectrum is considered a naturalresource and using it to make information routing more viable is not a good trade off.

However, simulated annealing can also be used to determine parameters. [36] In which the process is considereda black-box. Rather than moving in the solution space one moves in the parameter space and feeds the black-box a set of parameters and receives the results. The differences in results is then described as ∆E. The rest ofthe algorithm is analogous to the previously described approach.

19


Figure 2.7: Visualization of the Simulated annealing [35]

2.5.2 Genetic Algorithm

Genetic algorithm is a bio-inspired heuristic optimization utilized for a large set of optimization problems. Thealgorithm is based on the biological evolution, generally described as survival of the fittest. The algorithm alsoincorporates concepts such as random mutation, selection and mating.[37]

A generic genetic algorithm works by the following five steps, step 2-5 are repeated until a criteria is met, acertain number of generations are completed or is manually aborted.

1. Initialize populations

2. Fitness

3. Selection of parents

4. Crossover between parents

5. Mutation of chromosomes

Every weight is called a chromosome, every set of chromosomes is called a node, every set of nodes beforea selection is called a generation and every node chosen in the selection is called a parent[37] . Typically thefitness function is determined by a function but it is also possible to test or simulation, as long as the results canbe evaluated.

The genetic algorithm starts by generating a population of size N. Chromosomes can differ in appearance,either they are represented by bits which in turn represent a number (this is done to save computational power)or simply by the numbers themselves. All nodes in the generation are then run through the fitness function anda result is appended to every node. The selection process then starts and the nodes with the results closest to

20


the desired results are chosen to be the parents of the next generation. The parents then mate and create thenext generation. This can be done in several ways but generally the influence of each parent affect a certainchromosome/attribute to a certain degree is randomized. After the mating is done the mutator has a chance tomutate certain chromosomes in certain nodes. The mutator typically makes the chromosomes extreme, that isto flip a bit or drastically increase the value of the chromosome. Once the mutation is completed steps 2-5 arerepeated. This continues until the results converges, until a certain number of generations are done or until thealgorithm is manually terminated.

2.6 Previous Work

In the past several years there has been increased interest in researching the implementation of swarm-inspiredrouting algorithms in VANETs.

Sergio Luis O. B. Correia et al.[38] implemented two Ant Colony Optimization (ACO) procedures in the ex-isting reactive and unicast routing protocol Dynamic MANET On-demand (DYMO). The paper resulted inMobile-aware Ant Colony Optimization Routing DYMO (MAR-DYMO) for use in VANETs. ACO is inspiredby ants’ behavior of leaving the chemical substance pheromone on paths when randomly exploring an area forfood. The pheromone is then used by other ants which strengthens the path for communication between thenest and the food source. The researchers implemented a pheromone deposit and evaporation mechanism onthe links in the routing tables so that the the actual route could be chosen based on the pheromone levels. Thealgorithms were evaluated in the event-based discrete Network Simulator 2 (NS2) in an urban environment.The protocol performed better in regard to the average end-to-end delay and average delivery ratio but had thehigher packet overhead when compared to AODV.

2.6.1 Cluster

In another research paper [39], an unicast Cluster-based Bee Colony Algorithm for QoS Routing Protocolin VANET (CBQoS-VANET) was proposed. It’s composed of a clustering algorithm and an Artificial BeeColony (ABC) algorithm, both based on the QoS criteria: available bandwidth, end-to-end delay, jitter and linkexpiration time. The clustering algorithm had two components, a cluster-heads election algorithm for route pathdetermination as well as multi-point relays selection for selection of a few nodes in a cluster to be responsiblefor relaying information between clusters. The ABC algorithm is based on the food foraging behavior observedin bee colonies where a scout returns to the beehive after exploring for food to share the information, followedby foragers who exploits the discovery to find efficient paths to the food source. The algorithm was evaluated inVeins on a 3-lane bi-directional highway. The protocol is able to select an optimal path in terms of bandwidthand delay and allow for the best PDR, delay and overhead.

Another research paper [40], proposes a Clustering algorithm based on Ant Colony Optimization (ACO) forVANETs (CACONET) which aims to solve the NP-problem of optimal selection of cluster heads, necessarywhen topology changes frequently. The algorithms finds the cluster head and its neighborhood in a mesh topol-ogy based graph connected by edges with a pheromone value and a heuristic value. A higher pheromone valuerepresents a better solution. The protocol was set up in MATLAB where it was tested in comparison to thecluster algorithms Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Par-ticle Swarm Optimization (CLPSO). The clustering is more efficient in comparison which reduced the packetrouting cost.

21


2.6.2 Geographic routing

Geographic routing protocols is efficient in large networks in that it only requires local information for routingdecisions. Kasana and Kuma propose a unicast Cat Swarm Optimization Geographical Routing (CSO-GR)protocol which aims to solve the optimization problem of choosing next forwarding vehicle (NFV) to ensurestable, efficient and reliable routing in a highly mobile network [41]. CSO is inspired from cats’ natural curios-ity towards moving things and good hunting skills. The implemented algorithm is aimed to find the optimizedlocation of the NFV with a mixed ratio of cats in seeking mode and tracing mode. In seeking mode the nextposition to move to is searched out and in tracing mode the cat moves according to its own velocity and up-dates its position (resembling PSO). To evaluate, a 4km 6-lane highway is considered. The proposed algorithmis compared with Geographic Distance Routing protocol (GEDIR) and shows encouraging results in terms ofPDR and normalized routing load.

Kaiwartya, Omprakash and Kumar, Sushil set out to solve the same NFV problem by designing a routingprotocol utilizing Geocasting through Particle Swarm Optimization (GeoPSO) [42]. PSO is based on swarmbehavior from birds, fish and more. It resembles CSO’s tracing mode in that it searches for the optimal for-warding position by adjusting vehicle of each particle of the population with the help of particle’s previousbest position and neighbor’s best position. The proposed GeoPSO algorithm was evaluated with ns2 in an ur-ban traffic environment. The results were compared with Peripheral node based Geographic Distance Routing(P-GEDIR) which showed increased PDR and decreased network load by half.

N. Hegde and S. S. Manvi implements the unicast multi-hop Ant Colony based Multipath Routing Algorithm(ACMRA) which searches multiple paths from source to destination [43]. It utilizes Search Ants (SANTs)that collects information from intermediate nodes which forwards the data packets and the path as well asReinforcement Ants (RANTs) which updates the table along the reverse path. The protocol was assessed inNS2 but implementation is under progress.

Bitam, Salim et al. implements a hybrid routing protocol which performs a topology-based procedure in densetraffic conditions and a geographic-based routing scheme when the network density [44]. The topology-basedrouting protocol is inspired by bees communication behavior when searching for food, similar to [39]. Thegeographic routing protocol is based on a genetic algorithm which finds the optimal path among several routesusing the nodes’ positions. The algorithm was compared to AODV and GPSR routing protocols and showedreduced end-to-end delay and high PDR while maintaining a similar overhead.

Yet another research paper proposes a Trust dependent Ant Colony Routing (TACR) protocol which createsclusters by considering direction, position and relative speed of the vehicle to address scalability [45]. Thecluster-head selection was based on re-affiliation energy, velocity and trust value of vehicles to increase sta-bility and efficient message communication and the ant colony routing was utilized to find the route betweensource and destination. The routing algorithm was tested on highway scenarios outperforms existing routingalgorithms in terms of routing overhead when compared to AODV and MARDYMO.

22

Chapter 3

Implementation

Swarm intelligence and VANETs are evidently two different areas which are both very broad. Therefore re-searchers have previously had different approaches when implementing swarm-based algorithms on VANETs.As a result, different simulation tools have been developed to better evaluate different aspects. This chapterdescribes and motivates the implementation approach of this thesis.

3.1 System Overview

Two aspects are considered when motivating the implementation, the problem and the swarm-based solution.There are several problems associated with VANETs as a consequence of the high mobility in VANETs, but thespecific identified problem that this thesis focuses on is the selection of the NFV in geographic routing. Thereare several reasons why this focus area is chosen. As previously described, geographic routing has recentlybecome the most widely used approach in information routing in VANETs partly because of its scalability andefficiency as well as the falling prices of GPS modules. The fact that geographic routing algorithms have beenstandardized in the European ETSI C-ITS set of standards makes the statement more apparent, see Figure 3.1.However, the standardized greedy forwarding algorithm is quite trivial in that it only utilizes the geographicposition of neighboring vehicles in its forwarding process although more information is shared in the networkthrough CAM messages. CBF is more reliable than greedy but in exchange for higher delay. This means thatthere are potential performance gains in regards to PDR and end-to-end delay by utilizing more informationfrom neighboring vehicles in the forwarding process when selecting the NFV.

Figure 3.1: Geobroadcast as defined in ETSI C-ITS standards. The source vehicle(orange car to the left) sendsa message to a geographical area (yellow circle) by selecting a NFV (green vehicles) to forward the message.This process repeats at each intermediate vehicle until the message reaches the geographical area where the

message is broadcasted[46].

When considering the swarm aspect, the problem is taken into account. The selection of the NFV is basicallyan optimization problem that is local for each individual vehicle in the forwarding process. There are severaloptimization algorithms that are worth considering such as PSO, ACO and CSO. This thesis chooses to imple-ment PSO for its recognition, simplicity and ease of integrating different variables to be optimized. Althoughthe goal with PSO is to optimize the decision making on the local level for each individual vehicle, it should notbe confused with a global optimization where the most efficient route is determined. However, the aim of thisthesis is to improve the performance globally with regards to end-to-end delay and packet delivery by optimiz-

23

CHAPTER 3. IMPLEMENTATION

ing locally. The underlying technologies of VANETs are standardized and the implementation is carried out inregards to a standard to make the thesis more relevant. As mentioned in the previous chapter, there are differentstandards for different regions. The implementation of this thesis will relate to the European ETSI C-ITS set ofstandards as the thesis is executed in the EU. A ETSI C-ITS protocol suite is implemented and maintained byR. Riebl as an open-source project[47], which is appropriate to use in this thesis. Figure 3.2 shows an overviewof the implemented standard. Each vehicle in the simulation can have several services such as DEN service andCA service which handles sending and receiving DENMs and CAMs at the application level. The services areconnected to the vehicle’s middleware which implements the GeoNetworking protocol, the networking layeraccording to the OSI-model defined in the ETSI C-ITS standards.

Figure 3.2: The European Standards Architecture[48]

The GeoNetworking protocol resides in the middleware, referred to as the GeoAdhoc Router in the ETSI C-ITSstandards. The router makes routing decisions of different types of DENMs that is defined in the standards.The Geographically-Scoped Broadcast (GBC) packet is one type of DENM which is used for geographicalbroadcasts and contains additional header fields that are needed, such as geographical shape of destinationarea, geographical position of destination area and geographical position of where the GBC packet originated.A simplified overview of the routing GeoNetworking protocol for GBCs is shown in Figure 3.3.

24


A GBC packet is passed to the router either by receiving the DENM from another vehicle or as a request fromthe DEN service if the vehicle is the creator of the GBC packet. In case if the packet is received from anothervehicle, the router checks if the predefined number of hops are exceeded. The router then chooses a forwardingalgorithm based on if the vehicle is inside or outside the destination area. Forwarding algorithms outside thedestination area are greedy forwarding (GF) and non-area contention-based forwarding (CBF) which use is toroute the packet towards the destination area by relaying the packet to the NFV. Forwarding algorithms insidethe destinations area are simple area forwarding algorithm, Area CBF and area advanced forwarding which areused distribute the packet within the destination area. The routing decision is then passed down to the MACand PHY layer so that the message can be sent to its destination. [18]

The PSO is implemented as a forwarding algorithm to be used outside the destination area in place of theexisting GF and non-area CBF algorithms. To make the PSO compatible with the GeoNetworking protocol, ituses the same input as existing forwarding algorithms and returns the NFV after execution. Practically, the PSOis implemented in the router source code located in vanetza subfolder geonet where a function pso forwardingis called each time the router routes a DENM outside the destination area. The pso function creates objects of animplemented particle class and each object represents each particle containing its attributes and the calculativefunctions to update them.

Figure 3.3: Overview of GBC flow through the GeoNetworking protocol.

25


3.2 Particle Swarm Optimization

In this implementation a problem space is created in which the PSO optimizes the selection of the NFV, seeFigure 3.4. The problem space is created for each DENM. Everything in the problem space with a geodeticposition in the world, has a cartesian position (x,y) with the source vehicle (where the DENM originates) asreference. Inherently, the source vehicle is origo. This is achieved by using a local cartesian function in Vanetzawhich derives a cartesian position from geodetic World Geodetic System 1984 (WGS84) coordinates and aWGS84 reference point (source vehicle) which becomes the cartesian origin. Each vehicle has an ego positionvector with its own geodetic coordinates. The geodetic coordinates of the source vehicle and destination area isobtained from the GBC packet header while the neighbour vehicle positions are obtained from a local LocationTable (LocT). The LocT holds information about surrounding vehicles and is maintained by the receptions ofCAMs. See Appendix A for a detailed view of these data structures.

Figure 3.4: A conceptual image of the problem space in the forwarding process using PSO. The imageillustrates a snapshot of one iteration.

26


When the PSO forwarding algorithm is executed it draws a circle surrounding the sending vehicle with themaximum communication range, max range, as radius. Only neighbours inside communication range is thenincluded in the calculations, while other vehicles in the LocT which has moved away from communicationrange are excluded. The PSO is initiated with a swarm of particles uniformly distributed in the communicationrange of the sending vehicle. The velocity of the particles is also initialized randomly in a uniform distribution.Albeit the most suitable size of the swarm for this implementation can be determined experimentally, [49] isfollowed where a swarm size of 40-50 is used. The implementation therefore has a swarm size of 50.

Around each neighbouring vehicle a circle with radius of a third of the maximum communication range iscreated. This confined area is associated with that neighbour’s position vector. Particles in this area inherits theneighbours position vector which is used to calculate the particles’ fitness function. If a particle is in the circlearea of several neighbouring vehicles, then it will be associated with the closest one. How information fromneighbouring vehicles is used in the fitness function is described in-depth in section 3.3.

In each iteration, every particle which is associated with a neighbouring vehicle calculates a fitness value withthe fitness function, see Equation (3.1).

F = W1 · F1 +W2 · F2 +W3 · F3 +W4 · F4 +W5 · F5 (3.1)

Fi are the components to be optimized and Wi are the weights for each component. If the particle is not inthe vicinity of a neighbouring vehicle, the fitness function is not executed and a low fitness value F = −5 isassigned to the particle. This ensures that the particles converges towards a neighbour. The design of the fitnessfunction is further described in Section 3.3.

The velocity and position of the particles are then updated with Equation (2.6) and (2.5) until the maximumnumber of iterations, 30, or the minimum error threshold value of 0.1 meter is satisfied. The PSO solutiongbest(x,y) represents the best location in problem space for the NFV, this naturally falls within the associationrange of a certain vehicle, thus the associated vehicle closest to the solution is selected as the NFV. See Figure3.5 for an overview of the PSO implementation.

27


Figure 3.5: A flowchart of the implemented PSO algorithm.

28


3.3 Fitness Function

The fitness function in PSO is generally any function which one wants to optimize. The aim of the fitnessfunction is to increase connection reliability and end-to-end packet delivery by incorporating parameters inthe fitness function that minimizes the number of multi-hops and link expiration time(LET) by utilizing mo-bility information of neighbouring vehicles. The fitness function in (3.1) consists of five components Fi,i = 1, 2, 3, 4, 5. A higher value of F in Equation 3.1 indicates a more suitable particle for NFV.

With euclidean distance defined by Equation (3.2)

dist(a, b) =√

(bx − ax)2 + (by − ay)2 (3.2)

the first component F1 is the normalized distance between the associated neighbour position and the destinationposition, as described by Equation (3.3)

F1 = −(dist(neighbourpos, destinationpos)− dist(senderpos, destinationpos)/max range

)(3.3)

senderpos is the cartesian position of the current vehicle executing the PSO, destinationpos is the cartesianposition of the destination area and neighbourpos is the cartesian position of the neighbouring vehicle whichthe particle is associated with. By minimizing the distance to destination, the number of multi-hops decreasesresulting in higher probability of successful packet delivery. Therefore F1 is negative to translate a shortdistance to a high fitness value.

The second component F2 considers the path loss which, in a network context, is the power attenuation ofthe radio signal as it propagates through space. Generally, the path loss increases with distance but may varydepending on the complexity of the path loss model. It is therefore desired to have a component which takesinto account the euclidean distance between the sender vehicle position and the neighbour vehicle which theparticle is associated with, see Equation (3.4).

F2 = −(dist(senderpos, neighbourpos)/max range

)(3.4)

F2 is defined as negative as minimizing the distance between sender and neighbour would also minimize pathloss, therefore this should be reflected in a high fitness value. It now is apparent that F1 and F2 are conflictingsince F1 want to maximize the distance from the sender vehicle and the latter want to minimize it. Throughweight determination an optimal relation between these two components is aimed to be determined.

The third and fourth component F3 and F4 aims to increase the connection reliability by decreasing the proba-bility of link disconnection utilizing mobility information from neighbouring vehicles in the LocT. F3 uses thenormalized neighboring vehicle’s relative absolute speed to the sender vehicle’s speed, see Equation 3.5.

F3 =∣∣(senderspeed − neighbourspeed)/300

∣∣ (3.5)

senderspeed is the speed of the sending vehicle executing the PSO and neighbourspeed is the speed of theneighbouring vehicle which the particle is associated with. The unit of neighbouring vehicles’ speed is cm/sin the LocT which is converted to km/h in the implementation. The speed is then normalized by assuming amaximum speed of a vehicle to be 300km/h. By minimizing the relative speed of a forwarding vehicle, the LETwill be greater.

29


F4 utilizes the neighboring vehicle’s relative absolute heading to the sender vehicle heading, see equation 3.6.senderheading is the heading of sending vehicle and neighbourheading is the heading of the neighbour vehiclewhich the particle is associated with. In the LocT, the heading is given as unsigned units of 0.1 degree fromnorth which is converted to full degrees before use. F4 is also normalized by dividing by 180 degrees.

F4 =∣∣(senderheading − neighbourheading)/180

∣∣ (3.6)

F5 aims to add a gradient to the fitness value in the area of neighbour association by using the distance betweenthe particle position and the neighbour which it is associated with, see Equation 3.7. particlepos is the cartesianposition of the particle and neighbourpos is the cartesian position of the neighbour. F5 is normalized bydividing with a third of the max range and is negative in order to give a high fitness value when the distance issmall. F5 helps the PSO to converge to the neighbouring vehicle within the association area.

F5 = −(dist(particlepos, neighourpos)/(max range/3)

)(3.7)

30


3.4 Weight Determination

Now that the PSO can be described and the fitness function is explained the task remains to determine thenumerical value of the weights in the PSO. To determine these weights one can utilize a stochastic approachbased on simulation and use genetic algorithms to ensure that the solution converges over time.

The genetic algorithm implemented for this project works similar to the generic one described in Section 2.5.2but instead of a fitness function an OMNeT++ simulation is run with the current node. The flow chart of asingle scenario is described in Figure 3.6. The weights can be described by

0 ≤Wi ≤ 1, i = {1, 2, 5} (3.8)

and− 1 ≤Wi ≤ 1, i = {3, 4} (3.9)

as well as Equation (3.1). The weights are described as doubles in the code and storage files. F1 and F2 arealready defined as negative in their respective function definition and since the objective is to find an optimalrelation between them, it is not desired to have negative weights since that would only amplify their effect overthe other variables. However if it would turn out that one of them is of less importance it would converge tozero in the GA process. F5 is also defined as negative and its weight should not be negative since it should bebeneficial to be near the neighbour a particle is associated with. It is not logically motivated to have it the otherway around since the particles then would not converge towards a neighbouring vehicle. It is decided in thisimplementation that F3 and F4 should be able to have either negative or positive sign to investigate if a fasterspeed or a vehicle in the opposite direction would be beneficial for the routing results.

Figure 3.6: Implementation of Genetic Algorithm

At the initialization of the population a generation size is chosen, hereby described by N. Each node in thegeneration is simulated and a result in form of the Package Delivery Ratio (PDR) is appended to the node.After the entire generation has been tested the selection process starts, at which point the two nodes withthe highest PDR are chosen as parents. To ensure that there is no degradation between generations the two

31


parents are cloned into the new generation, utilizing the concept of elitism. [37]. The remaining nodes in thegenerations are then chosen depending on the parents, with the influence of each parent being described as

Wn,i = (RNG)Wparent1,i + (1−RNG)Wparent2,i (3.10)

where RNG is a randomly generated number between 0 and 1 and n is the index of N . RNG can be seen as theinfluence one parent has on a certain weight. This is done for every weight and node until an entire generation,N, is filled.

The final step is the mutation of the chromosomes. For every child node there is an initial chance of 10% thata certain weights value is increased by 50%, limited to the maximum values of weights. This is done to avoidlocal maxima. [37] The mutation chance decreases after each simulation and converges to 0. The idea of thedecreasing likelihood of mutation is similar to the concept of temperature in the simulated annealing system.

The implementation was written in C++ as well as a Makefile making it easy to specify size of generation andwhich scenario should be run. Every new generation generates a current.txt-file. In every run the PSO parsesthe current node for the weight values. After each simulation the node has a result appended to it and is poppedfrom the current.txt and appended to the history.txt. Once a generation has completed the results of the lastN elements in history.txt are compared to one another and the selection of parents for the next generation isdone. The mating and mutation is executed and a new current.txt (that is the new, or rather the new currentgeneration) is populated with the size N. The entire history of nodes, results, generation and node-number ofall generations are stored in history.txt.

32


3.5 Simulation Environment

Due to the features described in the previous chapters the decision was made to use the Artery and Vanetzaframeworks in the OMNeT++ simulation environment. Customized scenarios or maps are built to triggerDENMs using the existing DEN Service in the vehicles such that the PSO algorithm can be evaluated.

Artery has the ability to trigger the DEN service on different conditions. Using conditions it is possible tocheck the OMNeT++ simulation dynamically and generate response effects for the vehicles. Conditions can bespeed, position, time-to-collision or a specific vehicle and they can also be limited to one or multiple vehicles.Vehicle effects are speed changes, lane changes, route changes and signal-triggers. Each set of conditionsand effect is called a story which is specified in a python file. The stories are then registered to a storyboardwith a certain string name. Each time a condition is satisfied, the storyboard is triggered and the story stringname is handled by the DEN service which links the story string trigger to a specific use-case that runs ahandleStoryboardTrigger function. A use-case creates the DENM by setting the DENM header fields suchas destination area and location. The message is then handled by the DEN service and sent to its destinationarea. A receiving vehicle handles the DENM in the DEN Service and links the DENM to its use-case where ahandleMessageReception function is run to take action for the received message. This enables the developersto customize their simulation scenarios by creating stories and use-cases, see Figure 3.7. In this thesis a custom

Figure 3.7: Architecture of storyboard, stories, Services and Use-cases.

use-case is created which is triggered by customized story. The story and use-case is designed to be sufficient totrigger a DENM outside of the destination area such that it requires several multi-hops in order for the messageto reach its destination. This is achieved by creating a minimalistic Emergency use-case which is triggered bya story with an ”emergency” string. Upon triggering this use-case a DENM is created with a destination areawith the shape of a circle and radius 10m. The destination position is set to a stationary vehicle which acts asa fixed point to receive the message without any defined reception response. The destination position dependson which scenario is run in the OMNeT++ simulation, these are explained more in Section 3.6. Other aspectsto the DENM such as a hop limit of 10 and the duration of 10s of which a DENM is valid is also set in theuse-case. The created story is designed such that a moving vehicle triggers the storyboard with the ”emergency”string every 10 seconds. This enables a continuous stream of DENMs to be created which can be sent from amoving vehicle to a fixed point that might necessarily not be in communication range.

The use-case and story emulate a scenario where a moving emergency vehicle continuously sends DENM to

33


a point of interest which could be a hospital or a crucial intersection. Although the use-case and story can bemade much more detailed and sophisticated by adjusting the behavior of the surrounding vehicles based on theDENMs, the described design is sufficient to evaluate the PSO design and to compare it with other algorithms.

An important aspect of OMNeT++ is the configuration file omnetpp.ini which specifies simulation parameters,modules to be used in the simulation as well as other settings associated with the simulation itself. The createdstory that previously was described is specified in the omnetpp.ini such that it can be used in the simulation.A main part of the omnetpp.ini file is the network settings which specifies the underlying linklayer used inthe simulation. As described in section 2.3.2, Artery supports either the veins link layer or a modified INETversion. In this thesis INET is used for the sole reason that it functioned expectantly at the time of implemention.Although there are differences between veins and INET which is beyond the scope of this thesis, the aim toevaluate PSO can be fulfilled with either of the different link layer models. If the use of linklayer is consistentfor all experiments, the differences between the routing algorithms should become apparent.

INET consists of different models for the physical radio components as well as a radio-medium module whichkeeps track of radio waves, noise sources, ongoing transmissions, background noise, thermal noise, obstacleloss among other aspects related to wireless communication. When choosing the modules for the simulation, asimplified approach has been taken. It is deemed unnecessary for the purpose of this thesis to use the most com-plex radio modules. This is done to reduce overall computation time which is already considerable. However,if more complex modules exists and can be used if so desired.

First of all, Artery includes a modified wireless area network (WLAN) with a customized Vanet NetworkInterface Card (NIC) for the vehicles. The NIC uses a carrier frequency of 5.9Ghz with 10MHz bandwidth andbitrate of 6 Mbps which is according to the European standards. The radio transmitter power and sensitivity isthen set to 7 mW and -89 dBm respectively which enables a communication range of approximately 300m.

The radio medium is based on IEEE 802.11 physical layer and uses scalar transmission in the analog repre-sentation which means that the transmission power is set by a given parameter as previously set and does notchange over time/frequency. The propagation is set to have a constant speed and the distance between Tx andRx is therefore relevant for the transmission. The path loss model used is free space path loss which does notconsider any fading or ground reflection into account. An isotropic scalar model for the background noise isused which does not change over space, time and space. The background noise power and minimum inter-ference power is set to -110 dBm. The receiver is modelled after NistErrorRateModel [50] which is basedon orthogonal frequency division multiplexing (OFDM) and calculates the received signal to noise ratio andconsults a look-up table based on the mode of operation to determine the probability of a successful framereception. Obstacle loss is disabled as it would add another level of complexity to the model.

34


3.6 Scenarios

Different scenarios are set up in OMNeT++ on which the forwarding algorithms can be evaluated. All thescenarios run on the same network settings, story and use-case as previously described. What differs betweenthe scenarios are the maps, which are the SUMO specific files. SUMO maps are defined with several xmlfiles where the sumocfg file links all the different xmls together. This is also the file that is specified in theomnetpp.ini configuration file. The net.xml file consists of nodes or intersection which are connected by edgesor roads. The net file also specifies what type of road an edge is. The bbox.xml file specifies the WGS84coordinates of the nodes. A rou.xml specifies the vehicle and all its properties such as length, acceleration andmax speed. Finally, a poly.xml file could also be used if obstacle is to be included in the simulation.

These file are generated in the SUMO tool OSM Web Wizard which fetches real world map data, such asWGS84 coordinates and lane speed limits, and converts it into the sumo file formats. Three different scenariosare created for this thesis: an suburban scenario Taby, a city scenario Berlin and a highway scenario Autobahn.Due to constraints, a maximum limit of 2000x2000m were set on the size of the maps. The interface for settingup the scenarios are shown in Figures 3.8 and 3.9

Figure 3.8: GUI of OSM webwizard

Figure 3.9: GUI of OSM webwizard sidebar traffic alternatives

35


3.6.1 Scenario 1: Taby

Taby is a suburban area north of Stockholm and one of the scenarios, see Figure 3.10. Suburban roads are notas dense as in a city but not as spare as on the country side. This means that vehicles are further apart and willhave less neighbouring vehicles if any at all. The emergency vehicle triggering the DENM in the storyboard isarbitrarily moving around the map in a square-like manner.

Figure 3.10: The Taby SUMO map generated from OSM Web Wizard

36


3.6.2 Scenario 2: Berlin

The Berlin map is a section of the city Berlin where the roads are denser. Vehicles are more likely to havemore neighbours to choose from when forwarding a message. The emergency vehicle is also in this scenarioarbitrarily moving around the map in a square-like manner.

Figure 3.11: The Berlin SUMO map generated from OSM Web Wizar

37


3.6.3 Scenario 3: Autobahn

The Autobahn is a fairly straight highway strip in Germany characterized by its high density and high speedswith lane speed limit of 300km/h. Unlike Taby and Berlin where the routes are generated from the OSMWeb-Wizard, a customized rou.xml file is created. The rou.xml file is created with four type of vehicles derivedfrom the SUMO website[51], see Table 3.1. Speed factor is the vehicle’s expected multiplicator for the lanespeed limits, speed deviation is the deviation of the speed factor and sigma is a car-following factor where 0.5represents a normal car’s behavior to follow another vehicle. These vehicles uses the flow function to createa repeated stream of vehicles with the same parameters over the highway. The flows in both directions areidentical. The normal car is created with periods specified in the last column of the table below. The emergencyvehicles moves from west to east with a period of 150s.

Table 3.1: Vehicle types in the Berlin scenario.Vehicle Type Max Speed (km/h) Speed Factor Speed Deviation Sigma Period (s)Normal Car 144 0.9 0.2 0.5 2.0Sports Car 216 1.3 0.1 0.1 30.0Trailer 108 1.1 0.1 0.5 30.0Coach 108 1 0.1 0.5 15.0

Figure 3.12: The Autobahn SUMO map generated from OSM Web Wizar

38


3.7 Measurements

The PSO algorithm for NFV selection is used solely for DENM and is therefore the focus for measurements.The QoS variables used for the genetic algorithm and the results are end-to-end delay and PDR.

To measure both end-to-end delay and PDR for DENMs, a signal has to be registered in OMNeT++ after theDENM is created and when it is received by the intended destination vehicle. The time of the registration and theoriginating vehicle station ID has to be recorded for the measurements, these are available in the GBC header.The DENM is created by the use-case in the DEN service before a transmission request to the networking layerwhich is where the transmission signal is recorded. The forwarding process is then in action and when the DENService of a vehicle indicates a received message, the DENM has reached its destination area. This is where areceived signal is recorded. The number of hops between transmission and reception is calculated in the DENservice upon message reception. The GBC header includes a variable for remaining hops from the hop limit of10 which is used to register a signal of the hops taken by the DENM.

The recorded signals are enabled in the omnetpp.ini and can be saved in various formats specified in the om-netpp.ini file. The results can then be post-processed in different ways depending on the format. Riebl has previ-ously implemented measurement recording for CAMs which is saved in the sqlite3 format and post-processedin the R programming language, therefore this thesis follows the same method for DENMs. R implementspackages to easily interact between R and the database format such as DataBase Interface (DBI), RSQLite anddplyr. These are used to connect to the sqlite3 result file, read its’ values and store them in R.

The end-to-end delay is calculated in an R script by matching a received signal to a transmitted signal byoriginating vehicle station ID. The end-to-end delay is then the time difference between the matched transmittedand received signal. The PDR is calculated by dividing the received signals over the transmitted signals. Thehop count is calculated in the DEN service as previously described, thus no post-processing is required.

R has the capabilities to plot the results using packages such as reshape2 and ggplot2. However, in this case theresults are used in the GA and writes the compiled results into a new txt file. Therefore python is used alongwith the matplotlib to visualize the results.

39

Chapter 4

Results

Three sets of PSO weights are determined through the GA from each of the scenarios by optimizing the PDRreliability. The sets are then tested on each scenario 20 times and compared to GF and CBF which are also ran20 times.

4.1 Weight Determination


The weight determination process for 5 generations and 50 nodes is shown in Figure 4.1. In the upper plot itcan be seen that weight 1, 2 and 5 are uniformly distributed between 0 and 1 whereas weights 3-4 are uniformlydistributed between -1 and 1. The weights converge rapidly and by generation 4 there is small deviation ofthe weights within the generation. The lower plot shows that the PDR increases with the generations but doesnot converge to a single value as the weights converge. The PDR varies between approximately 20-27% inthe last generation. The lower plot also shows that there are a high amount of nodes with 0% PDR in the firstgeneration. As the PDR increases with the generations, the 0% PDR behavior persists albeit in a lower quantity.

Figure 4.1: Weight determination results for the Taby scenario using 50 nodes over 5 generations. The upperplot shows the weight values and lower plot shows the PDR.

The weights from the last generation with the highest PDR are shown in Table 4.1. w1 and w2 shows that thefitness function values vehicles further away rather than close by a factor of approximately 4. w3 shows thatvehicles with low relative speed are favored and w4 that the relative heading barely impacts the NFV selection.

41

CHAPTER 4. RESULTS

The value of w5 indicates that minimizing the distance between the particle and the neighbouring vehicle issignificant for the fitness function.

Table 4.1: Taby WeightsWeight Valuew1 0.847451w2 0.212588w3 -0.579942w4 -0.0050747w5 0.50161


The weight determination process of the city Berlin scenario is shown in Figure 4.2. The upper plot of thefigure shows that all weights are initialized correctly and has converged by the fifth generation. The lower plotshows that the PDR converges to a mean value of approximately 60% by the end of second generation butshows deviation between 40-80% in the extreme cases. The behavior of 0% PDR throughout all generationsappears in this scenario as well.

Figure 4.2: Weight determination results for the Berlin scenario using 50 nodes over 5 generations. The upperplot shows the weight values and lower plot shows the PDR.

The weights from the last generation with highest PDR are shown in 4.2. w1 and w2 shows a relation ofapproximately 3 to 1 in favor of the vehicle being further away opposed to close from the sending vehicle. w3

shows favor for high relative speed but with little to no impact on the fitness function. w4 shows that the fitnessfunction favors a vehicle with high difference in heading and w5 that a node close to the associated neighbouris favored.

42

CHAPTER 4. RESULTS

Table 4.2: Berlin WeightsWeight Valuew1 0.624269w2 0.199313w3 0.0461836w4 0.165527w5 0.452979


The weight determination for the highway scenario Autobahn is shown in Figure 4.3. In the upper plot itis shown that the nodes are initialized correctly and that the weights has converged by the third generation.The lower plot shows that the PDR converges quickly by the second to third generation to a mean value ofapproximately 40%. This scenario also has some cases of 0% PDR, albeit fewer than the other scenarios.

Figure 4.3: Weight determination results for the Autobahn scenario using 50 nodes over 5 generations. Theupper plot shows the weight values and lower plot shows the PDR.

The weights from the last generation with highest PDR are shown in Figure 4.3. w1 and w2 shows that acandidate vehicle further away has twice the impact than a vehicle being closer to the sending vehicle. w3

shows that a vehicle with low relative speed is favored and w4 that a low relative heading is favored, but thelatter having less importance. w5 shows that the node being close to its associated neighbour is significant forthe fitness value.

Table 4.3: Autobahn WeightsWeight Valuew1 0.781108w2 0.421437w3 -0.248689w4 -0.0738247w5 0.590401

43

CHAPTER 4. RESULTS

4.2 Test runs

The PSO weights are tested on the three different scenarios and compared to GF and CBF with regards toend-to-end delay, PDR and hop count. All three set of weights are tested on each scenario and it is importantto note that results between scenarios might not be directly comparable as each scenario is different. Addi-tionally, the results when testing the GF, CBF and PSO with determined weights filters out results which has0% PDR as these are deemed as faulty. PSOi denotes the PSO with weights from the ith scenario wherei = taby, berlin, autobahn.


The PDR for GF, CBF and PSO with weights from all scenarios run on Taby is shown in Figure 4.4. Onthe Taby scenario the figure shows that CBF has the highest PDR of 36% followed by PSOtaby with 22%,PSOberlin with 20%, PSOautobahn with 19% and lastly GF with 9%. Additionally, the figure shows that GFis deterministic with only one PDR value whereas the PDR of the other test cases has a deviation. All PSOversions spans within 10% whereas CBF has a lower variation within 4%.

Figure 4.4: Packet delivery date results for the weights from the Taby scenario. The bars represents the meanvalue, the black marker represents the spread between maximum and minimum value and the gray circles arethe data points.

The same test results in terms of end-to-end delay is shown in Figure 4.5. The delay is the highest for CBF with295ms followed by PSOautobahn with 53ms, PSOtaby with 50ms, PSOberlin with 44ms and GF with 31ms.In terms of variation, GF is deterministic whereas the CBF PSOtaby and PSOautobahn spans within 4% andPSOberlin within 2%.

The tests results in terms of hops is shown in Figure 4.6. It can be seen that CBF has the highest number ofhops of 4.4 followed by PSOautobahn with 4.1 hops, PSOtaby with 4.0 hops PSOberlin with 3.9 hops and GFwith 3.0 hops. PSOautobahn shows the highest result variation of approximately 1 hop followed by PSOtaby

and PSOberlin of around 0.5 hop and lastly CBC of 0.3 hops.

44

CHAPTER 4. RESULTS

Figure 4.5: End-to-end delay results for the weights from the Taby Scenario. The bars represents the meanvalue, the black marker represents the spread between maximum and minimum value and the gray circles arethe data points.

Figure 4.6: Hop count results for the weights from the Taby Scenario. The bars represents the mean value, theblack marker represents the spread between maximum and minimum value and the gray circles are the datapoints.

45

CHAPTER 4. RESULTS


The PDR run on the Berlin scenario is shown in Figure 4.7. The figure shows that CBF has the highest PDR of79% followed by PSOberlin with 62%, PSOautobahn with 54%, PSOtaby with 53% and lastly GF with 35%.The variation of PDR is only present in the PSO where PSOtaby has the highest variation of approximately20% followed by PSOautobahn with 12% and PSOberlin with 10%

Figure 4.7: Packet delivery date results for the weights from the Berlin scenario. The bars represents the meanvalue, the black marker represents the spread between maximum and minimum value and the gray circles arethe data points.

The results in terms of end-to-end delay is shown in Figure 4.8. CBF has the highest end-to-end delay with128ms followed by PSOtaby with 59%, GF with 53ms, PSOautobahn with 48ms and lastly PSOberlin with43ms. GF and CBF do not have any result variation. PSOtaby has a variation of 10ms which is affected by onehigh value followed by PSOautobahn that spans between 15ms and PSOberlin within 10ms.

The results in terms of hop count is shown in Figure 4.9. CBF has the highest hop count of 3 followed byPSOberlin with 2.5 hops, PSOautobahn and PSOtaby with 2.4 hops and lastly GF with 2.1 hops. There is aslight deviation that exist for the PSO versions, see Figure 4.9.

46

CHAPTER 4. RESULTS

Figure 4.8: End-to-end delay results for the weights from the Berlin Scenario. The bars represents the meanvalue, the black marker represents the spread between maximum and minimum value and the gray circles arethe data points.

Figure 4.9: Hop count results for the weights from the Berlin Scenario. The bars represents the mean value,the black marker represents the spread between maximum and minimum value and the gray circles are the datapoints.

47

CHAPTER 4. RESULTS


The results for the Autobahn scenario in terms of PDR is shown in Figure 4.10. CBF has the highest PDR of64% followed by PSOautobahn with 41%, PSOberlin and PSOtaby with 39% and lastly GF with 16%. Allscenarios shows a spread in the PDR whereas the PSO algorithm has a much larger spread than the GF andCBF of approximately 12%.

Figure 4.10: Packet delivery date results for the weights from the Autobahn scenario. The bars represents themean value, the black marker represents the spread between maximum and minimum value and the gray circlesare the data points.

The results in terms of end-to-end delay is shown in Figure 4.11. The delay of 259ms is achieved with CBF,followed by PSOberlin with 37ms, PSOautobahn with 36ms, PSOtaby with 35ms and lastly GF with 14ms.The variation in the results persists in the delay but with the PSO having the highest deviations and CBF isbarely existent.

The results in terms of hop count is shown in Figure 4.12. The hop count is the highest when using CBF with4.9 hops, followed by PSOautobahn with 4.1 hops, PSOberlin with 4.0 hops, PSOtaby with 3.8 hops and GFwith 1.9 hops. The spread is similar to previous results where PSO has greater variation in the results than GFfollowed by CBF which is marginal.

48

CHAPTER 4. RESULTS

Figure 4.11: End-to-end delay results for the weights from the Autobahn Scenario. The bars represents themean value, the black marker represents the spread between maximum and minimum value and the gray circlesare the data points.

Figure 4.12: Hop count results for the weights from the Autobahn Scenario. The bars represents the meanvalue, the black marker represents the spread between maximum and minimum value and the gray circles arethe data points.

4.3 Conclusion

This section summarizes the results from all scenarios. When looking at the weight determination process,all scenarios converges in terms of PDR by the 2nd or 3rd generation which is before the weights has fullyconverged to their final values. While training the PDR the weights converge after the third or fourth generation.The PDR varies in all scenarios when using the same parameters and simulations with 0% PDR exists in allscenarios throughout all generations.

When testing the final weights PSO generally has a lower PDR and end-to-end delay as compared to CBF. PSO

49

CHAPTER 4. RESULTS

also has a higher PDR and higher end-to-end delay as compared to GF. CBF has the highest hop count, followedby PSO and lastly GF in all scenarios. A trained PSO, that is a set of weight trained to a specific scenario, hasbetter performance than non-trained in terms of PDR and end-to-end latency.

50

Chapter 5

Discussion & Conclusion

This chapter presents a discussion of the results and the conclusions that are drawn from the results.

5.1 Discussion

The results show that CBF is the most reliable forwarding algorithm compared to both GF and PSO with thehighest PDR. This is achieved at the cost of a high end-to-end delay due to its timer-based nature and increasedprocessing. PSO manages to increase the PDR compared to GF while keeping the latency low, only slightlyhigher than GF and dramatically lower than CBF. However, the PSO experiences a significantly higher resultvariation, in terms of all measured variables, when compared to both GF and CBF. The PSO with the sameweight values give a high variation of results and this same behavior is also demonstrated through the GAprocess.

Before analyzing the results, it is important to discuss the result variation of the PSO. There are many causesthat might be the root to the variation might not be explainable within the scope of this thesis due to the large andintertwined code framework that has been used for the simulation. With that in mind, a brief inspection of thesimulation was done to investigate this. It has previously been described that the Tx signal strength attenuatesas it propagates through air and if the vehicle is too far away, near the communication range limit, the signalwould be too weak to be detectable for the Rx. The Rx also has a stochastic error model that calculates theprobability of faulty packets which then are not received. CBF results in the Taby scenario shows a relativelyhigh result variation which gives some credibility to this explanation. However, PSO shows a higher variationmore frequently which indicates that there are additional causes. The fact that this is the case gives rise tosuspect that the variation is a consequence of the PSO algorithm itself. Unlike GF and CBF, the PSO containsstochastic elements in the particle initialization and velocity function which might affect which neighbouringvehicle the PSO converges to in each forwarding step. With the weight determination one would expect the GAto make the PSO converge to an optimal solution. However, with the designed fitness function being relativelysimple there might exist several neighbouring vehicles with small differences in fitness value. The PSO wouldthen be more dependent on the stochastic effects. Ponder on a situation with a neighbouring vehicle closer to thedestination with high relative speed and another vehicle further away from the destination but with low relativespeed. The fitness value might be similar for both neighbouring vehicles, but in fact one of these vehicles mightresult in a better PDR and delay over the other. Then the randomness of the particle initialization and particlemovement would reflect in randomness in the result.

In case of the variation being a consequence of the stochastic simulation models then it is not obvious whethera solution would be to remove it. The used simulation models from INET are developed from a scientificstandpoint and are supported by studies. They are a representation of how the hardware and radio wavesbehave in a real-life situation. By removing the stochastic behaviors from the simulation the results may beless random but they would also be less representative of results from tests run on real hardware. On the otherhand, with the stochastic models present the GA has difficulties converging to a set of weights with the optimalsolution, in this case the highest PDR. In the GA implementation used in this thesis there is not a mechanism tohandle different results from the same set of weights over generations. This can clearly be seen in the weightdetermination results where the GA improves the PDR drastically by the second generation, but then has a hard

51

CHAPTER 5. DISCUSSION & CONCLUSION

time to converge to a single value. Therefore a possible solution would be to add such a mechanism to handlenon-deterministic results. Another solution could be to temporarily disable the stochastic elements only duringthe GA and then use them during testing.

Looking at the PSO as the cause of the varying results instead, a more complex fitness function could bedesigned in order to make the PSO deterministic. There are then several factors that has to be taken intoaccount. This thesis has used relative simple vehicle data of neighbours: position, speed and heading. It isnot apparent at first glance what information would be beneficial to use in the fitness function such that PDR,latency and other QoS variables are improved. Therefore a proper study to determine this has to be carried outin order to design a refined fitness function. Then a problem of how many fitness variables would still persists.The fitness function might require far more variables than what is proposed in this thesis’ fitness function. ThePSO may then become much more complex resulting in the forwarding algorithm becoming a black box withless insight into how the PSO choses a NFV. Clearly this is already a problem with the relatively simple fitnessfunction proposed in this thesis.

Despite the varying results, by looking at the mean value the performance of the PSO outperforms GF inall measured aspects and CBF in terms of end-to-end delay. Additionally, weights obtained with GA from aspecific scenario also performs better on that specific scenario, in most cases, compared to the other scenarios.It is therefore of interest to evaluate the PSO by its fitness function and the obtained weights. As can be seenin the weights from all scenarios, F1 is clearly the most important variable. It is logical to move the packetcloser to the destination in the forwarding process. F5 is another variable which importance seems to be fairlyconsistent across the scenarios with a value of around 0.5. This is also logical as particles otherwise wouldnot converge to a certain vehicle and probably oscillate between several vehicles with high traffic density. F2

is also significant throughout the scenarios but might not be the most dominant variable, especially comparedto F1. As described before there is a trade-off between Tx signal attenuation and packet progress towardsdestination. Through the GA a ratio between them has been determined. Now looking at F3 shows that its’weight in the Berlin scenario is only 0.046 and looking at F4 shows that its’ weight in Taby and Autobahn isonly -0.005 and -0.07 respectively. A weight close to 0 means that these variables barely had any impact onthe fitness value in these scenarios. Furthermore, the sign of F3 and F4 are inconsistent across the scenarioswith the first-named being negative in Taby and Autobahn but positive in Berlin and the latter being negativein Taby and Autobahn but positive in Berlin. The inconsistency of the weights for both F3 and F4 raisessuspicion that rather than the speed and heading information of F3 and F4 is being useful, the weights mightbe manipulated in the different scenarios to converge to the best solution or PDR. To strengthen the argumentsfor this suspicion, the delay results are looked at. Across all scenarios the delay varies between 40-80ms forthe PSO, except for the 150ms case in the Berlin scenario. This means that it would take up to 150ms fora DENM to traverse from the source vehicle to the destination vehicle using PSO. This means that a vehicleof a top speed, on the Autobahn, of 300km/h would have moved 12.5m. With the hop count results fromBerlin of approximately 4 hops, the vehicles would have moved around 3m per forwarding step. Even withthe scaled down communication range of 300m, this is quite insignificant. Since the vehicles barely have anymovement during the actual transmission, the information contained in F3 and F4 most likely does not haveany actual effect on the results. This raises several interesting thoughts. First of all, this might be the cause ofthe inconsistency experienced with the PSO results. Furthermore, good performance with PSO is achieved interms of mean value. The fact that the information of F3 and F4 does not seem to be used might suggest thateven better performance can be achieved with a refined version of the fitness function and PSO. Lastly, with F3

and F4 being inconsistent their weights are adapted for each scenario which would explain that weights trainedon different scenarios does not perform as well on other scenarios. However, they still perform well comparedto GF since three of the variables actually contains valuable information for improving the performance. Thismight suggest that the performance might be more consistent across scenarios with a refined fitness function.

It is apparent that the design of the fitness function is crucial for the performance of the PSO. This thesis decidedto use motion information from neighbouring vehicles shared through CAMs which is also why the particle areassociated to a certain neighbouring vehicle. The problem space can be seen as a gravitational field where

52

CHAPTER 5. DISCUSSION & CONCLUSION

the strongest pull is the optimal position of the NFV. The fitness function solely uses vehicle information andtherefore only the neighbouring vehicles contribute to the gravitational field where the strongest pull representsthe optimal forwarding vehicle. This raises the question whether just comparing the fitness function betweenthe neighbouring vehicles would result in better results. The stochastic elements from the PSO would thennot play into account which is speculated to impact the result variation and the forwarding algorithm mighthave marginally lower end-to-end delay due to less computation. However, the fitness function is not limited tosolely include neighbouring vehicle information but can also take other surrounding environmental informationsuch as buildings into account. This would add other contributing factors to the gravitational field apart fromthe vehicles meaning that it would then not be as simple to just compare the fitness function between theneighbouring vehicles. Additionally, it is more efficient to calculate the fitness function only for 40-50 particlesover a few iterations compared to calculating the fitness function for all neighbouring vehicles in a denselypopulated environment. With the existing communication range of 300m there could exist thousands, if not tenthousands, of neighbouring vehicles in a city environment. Furthermore, if this communication range wouldincrease with technology advancements the neighbouring vehicles could rapidly grow.

Finally, what remains is to discuss the forwarding algorithms connection to the results. First of all, GF is avery simple algorithm where forwarders only uses position data from its LocT containing surrounding neigh-bours. The standardized GF algorithm does not have a mechanism for determining which neighbours still are incommunication range and therefore the LocT quickly becomes outdated with vehicles that the GF can chooseas NFV but not be able to send to. The European standards attempts to deal with this issue with a lifetimeexpiration of 20 seconds in the LocT. GF is simply not efficient in terms of PDR because of its simplicity andthe highly dynamic nature of VANETs. PSO on the other hand improves on this and uses the vehicle’s commu-nication range in order to filter out the outdated neighbouring vehicles. Furthermore, the PSO algorithm itselfis more complex and also uses more data from neighbouring vehicles to determine the NFV. This is reflectedin the increased PDR but slightly higher end-to-end delay than GF. Lastly, CBF has the highest PDR thanksto its reliability mechanism that ensures forwarding by an alternative vehicle if the optimal forwarder does notreceive the packet. This is advantageous when the NFV is near the communication range limit where the radiosignal is heavily attenuated. The PSO does not have any mechanism to handle this and therefore explains whythe PSO is less reliable than CBF in terms of PDR. However, the reliability mechanism comes at a cost oflarger forwarding delay and additional processing which is why the CBF has the highest latency compared tothe other forwarding algorithms.

5.2 Conclusions

The purpose of this thesis was to explore a bio-inspired approach, specifically swarm intelligence, for informa-tion routing. The idea was to utilize the self-organizing nature of swarms to increase robustness. It is evidentthat there lies potential in PSO as a forwarding algorithm since the results show an increased PDR compared toGF at the cost of a slight delay increase. The PSO is not as reliable as CBF but manages to keep a low delay.The weight results and result variation indicates that the PSO can be improved upon by carefully redesigningthe fitness function to use more relevant information. Mobility information did not seem to have a consistentimpact on the results of this thesis. Furthermore, the trained weights of the PSO are scenario dependent. ThePSO performed best on the scenario it was trained on compared to the weights from the other scenarios.

It is important to note that the results are limited to the scope of this thesis. The results are limited to the modeledbehavior of SUMO, Artery, Vanetza, OMNeT++ and INET. This includes the simplified radio propagationnot affected by diffraction losses or ground reflections. This means that the results and conclusions are nottransferable to real-life scenarios with hardware. Additionally, the results are also only represented in end-to-end delay and PDR which means that other QoS metrics such as throughput and overhead are unknown andshould be investigated to fully determine the performance of PSO.

53

Chapter 6

Future Work

Refinement of the PSO fitness function is required in order to lower the result variation. Including more relevantinformation in the fitness function could potentially increase the PDR reliability further while keeping a lowforwarding delay. A thorough study to determine what information to use should be conducted in order todetermine this.

Add an element to the fitness function that evaluates obstacles imported from maps. Objects obstructs radiosignals but the reflections might increase its reach through a non-line of sight path. In a real scenario this wouldbe provided by GPS-maps and in the scenario by the poly.net.xml-file. Naturally such an element should beanalyzed with a proper obstacle loss model. It is also important to consider what information is available inmaps. Buildings are usually marked, but information of billboards and parked trucks etc. might not.

The simulation uses the simplest radio models and the PSO should be tested on more complex simulation mod-els and hardware in order to obtain more accurate results of how PSO performs in a real vehicular environment.

This thesis analyses the reliability and time aspect of information routing algorithms. Other QoS metrics suchas throughput and overhead should be measured in order to observe other aspects that this thesis does notconsider.

55

Bibliography

[1] H. Hartenstein and L. P. Laberteaux, “A tutorial survey on vehicular ad hoc networks,” IEEE Communi-cations Magazine, vol. 46, no. 6, pp. 164–171, June 2008.

[2] K. Palm, “Technology for autonomous cars,” 08 2018. [Online]. Available: https://www.cybercom.com/Cases-and-clients/technology-for-autonomous-cars/

[3] Innovation and N. E. Agency, “Scoop@f part 2,” 08 2018. [Online]. Available: https://ec.europa.eu/inea/en/connecting-europe-facility/cef-transport/2014-eu-ta-0669-s

[4] R. Kumar and M. Dave, “A comparative study of various routing protocols in vanet,” International Journalof Computer Science Issues, vol. 8, 08 2011.

[5] S. Bitam and A. Mellouk, Bio-Inspired Routing Protocols for Vehicular Ad Hoc Networks. John Wiley& Sons, Inc., 2014, ch. Bio-Inspired Routing Protocols for VANETs, pp. 79–119. [Online]. Available:http://dx.doi.org/10.1002/9781119004967.ch4

[6] E. C. Eze, S. Zhang, and E. Liu, “Vehicular ad hoc networks (vanets): Current state, challenges, potentialsand way forward,” in 2014 20th International Conference on Automation and Computing, Sept 2014, pp.176–181.

[7] M. Dixit, R. Kumar, and A. K. Sagar, “Vanet: Architectures, research issues, routing protocols, and its ap-plications,” in 2016 International Conference on Computing, Communication and Automation (ICCCA),April 2016, pp. 555–561.

[8] S. Hamato, S. H. Syed Ariffin, and N. Fisal, “Overview of wireless access in vehicular environment (wave)protocols and standards,” Indian Journal of Science and Technology, vol. 7, 07 2013.

[9] W. Liang, Z. Li, H. Zhang, S. Wang, and R. Bie, “Vehicular ad hoc networks: Architectures, researchissues, methodologies, challenges, and trends,” International Journal of Distributed Sensor Networks,vol. 2015, pp. 1–11, 08 2015.

[10] K. R. Sturley, “Radio technology,” 07 2018. [Online]. Available: https://www.britannica.com/technology/radio-technology

[11] “Radio propagation models,” Tech. Rep., 07 2018. [Online]. Available: http://people.seas.harvard.edu/∼jones/es151/prop models/propagation.html#fsl

[12] V. Beal, “The 7 layers of the osi model,” 07 2018. [Online]. Available: https://www.webopedia.com/quick ref/OSI Layers.asp

[13] M. Pinola, “Understanding wi-fi and how it works,” 07 2018. [Online]. Available: https://www.lifewire.com/what-is-wi-fi-2377430

[14] B. Mitchell, “802.11 standards explained: 802.11ac, 802.11b/g/n, 802.11a,” 07 2018. [Online]. Available:https://www.lifewire.com/wireless-standards-802-11a-802-11b-g-n-and-802-11ac-816553

[15] C. Sommer, “Ieee 802.11p and ieee 1609.4 dsrc/wave,” Jun 2017. [Online]. Available: http://veins.car2x.org/documentation/modules/

57

https://www.cybercom.com/Cases-and-clients/technology-for-autonomous-cars/

https://www.cybercom.com/Cases-and-clients/technology-for-autonomous-cars/

https://ec.europa.eu/inea/en/connecting-europe-facility/cef-transport/2014-eu-ta-0669-s

https://ec.europa.eu/inea/en/connecting-europe-facility/cef-transport/2014-eu-ta-0669-s

http://dx.doi.org/10.1002/9781119004967.ch4

https://www.britannica.com/technology/radio-technology

https://www.britannica.com/technology/radio-technology

http://people.seas.harvard.edu/~jones/es151/prop_models/propagation.html#fsl

http://people.seas.harvard.edu/~jones/es151/prop_models/propagation.html#fsl

https://www.webopedia.com/quick_ref/OSI_Layers.asp

https://www.webopedia.com/quick_ref/OSI_Layers.asp

https://www.lifewire.com/what-is-wi-fi-2377430

https://www.lifewire.com/what-is-wi-fi-2377430

https://www.lifewire.com/wireless-standards-802-11a-802-11b-g-n-and-802-11ac-816553

http://veins.car2x.org/documentation/modules/

http://veins.car2x.org/documentation/modules/

BIBLIOGRAPHY

[16] “Intelligent Transport Systems (ITS); Access layer specification for Intelligent Transport Systems op-erating in the 5 GHz frequency band ,” European Telecommunications Standards Institution, Sophia-Antipolis, FR, Standard, Nov. 2012.

[17] A. Fitah, A. Badri, M. Moughit, and A. Sahel, “Performance of dsrc and wifi for intelligent transportsystems in vanet,” Procedia Computer Science, vol. 127, pp. 360–368, 2018.

[18] “Intelligent Transport Systems (ITS); Vehicular Communications; GeoNetworking; Part 4: Geograph-ical addressing and forwarding for point-to-point and point-to-multipoint communications; Sub-part 1:Media-Independent Functionality,” International Organization for Standardization, Sophia Antipolis, FR,Standard, Aug. 2018.

[19] A. Awang, K. Husain, N. Kamel, and S. Aıssa, “Routing in vehicular ad-hoc networks: A survey onsingle- and cross-layer design techniques, and perspectives,” IEEE Access, vol. 5, pp. 9497–9517, 2017.

[20] IEEE, “Ieee draft guide for wireless access in vehicular environments (wave) - architecture,” IEEEP1609.0/D10, January 2018, pp. 1–104, Jan 2018.

[21] A. Festag, “Cooperative intelligent transport systems standards in europe,” Communications Magazine,IEEE, vol. 52, pp. 166–172, 12 2014.

[22] G. C. Congress, “Volkswagen group brands to use wlanp for v2x communication as standard equipmenton new models from 2019,” 07 2018. [Online]. Available: http://www.greencarcongress.com/2018/02/20180219-vw.html

[23] B. Paul, M. Ibrahim, and M. A. N. Bikas, “VANET routing protocols: Pros and cons,” CoRR, vol.abs/1204.1201, 2012. [Online]. Available: http://arxiv.org/abs/1204.1201

[24] S. Bitam and A. Mellouk, Bio-Inspired Routing Protocols for Vehicular Ad Hoc Networks. John Wiley& Sons, Inc., 2014, ch. Routing for Vehicular Ad Hoc Networks, pp. 29–50. [Online]. Available:http://dx.doi.org/10.1002/9781119004967.ch2

[25] O. Kaiwartya, S. Kumar, D. Lobiyal, A. Abdullah, and A. Hassan, “Perfor-mance improvement in geographic routing for vehicular ad hoc networks,” Sen-sors (Switzerland), vol. 14, no. 12, pp. 22 342–22 371, 2014, cited By 22. [On-line]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84912120804&doi=10.3390%2fs141222342&partnerID=40&md5=bd75a3cacaf42e506f47ee809df00ce7

[26] “Prescan product datasheet,” Datasheet, 07 2018. [Online]. Available: https://tass.plm.automation.siemens.com/prescan

[27] “Veins publication,” 12 2017. [Online]. Available: http://veins.car2x.org/publications/

[28] C. Sommer, “Veins over all documentation,” Jun 2017. [Online]. Available: https://veins.car2x.org/documentation/

[29] R. Riebl, “Raphael riebl github repository,” https://github.com/riebl, 2018.

[30] R. Riebl and C. Facchi, “Regain control of growing dependencies in omnet++ simulations,” CoRR, vol.abs/1509.03561, 2015. [Online]. Available: http://arxiv.org/abs/1509.03561

[31] “Inet framework documentation,” Documentation, 07 2018. [Online]. Available: https://inet.omnetpp.org/Introduction.html

[32] S. J. Wright, “Optimization,” 08 2018. [Online]. Available: https://www.britannica.com/science/optimization

58

http://www.greencarcongress.com/2018/02/20180219-vw.html

http://www.greencarcongress.com/2018/02/20180219-vw.html

http://arxiv.org/abs/1204.1201

http://dx.doi.org/10.1002/9781119004967.ch2

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84912120804&doi=10.3390%2fs141222342&partnerID=40&md5=bd75a3cacaf42e506f47ee809df00ce7

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84912120804&doi=10.3390%2fs141222342&partnerID=40&md5=bd75a3cacaf42e506f47ee809df00ce7

https://tass.plm.automation.siemens.com/prescan

https://tass.plm.automation.siemens.com/prescan

http://veins.car2x.org/publications/

https://veins.car2x.org/documentation/

https://veins.car2x.org/documentation/

https://github.com/riebl

http://arxiv.org/abs/1509.03561

https://inet.omnetpp.org/Introduction.html

https://inet.omnetpp.org/Introduction.html

https://www.britannica.com/science/optimization

https://www.britannica.com/science/optimization

BIBLIOGRAPHY

[33] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Neural Networks, 1995. Proceedings.,IEEE International Conference on, vol. 4, Nov 1995, pp. 1942–1948 vol.4.

[34] K. A. Dowsland and J. M. Thompson, Handbook of Natural Computing. Springer Berlin Heidelberg,2012. [Online]. Available: https://doi.org/10.1007/978-3-540-92910-9 49

[35] S. Moins, “Implementation of simulated annealing algorithm for matlab,” Master’s thesis, LinkopingInstitute of Technology, Institutionen for Systemteknik, 581 83 LINKOPING, 08 2002.

[36] J. Haddock and J. Mittenthal, “Simulation optimization using simulated annealing,” Computers & Indus-trial Engineering, vol. 22, no. 4, pp. 387–395, 1992.

[37] O. Kramer, Genetic Algorithm Essentials, ser. Studies in Computational Intelligence, 679, 2017.

[38] S. L. O. B. Correia, J. Celestino, and O. Cherkaoui, “Mobility-aware ant colony optimization routing forvehicular ad hoc networks,” in 2011 IEEE Wireless Communications and Networking Conference, March2011, pp. 1125–1130.

[39] M. E. A. Fekair, A. Lakas, and A. Korichi, “Cbqos-vanet: Cluster-based artificial bee colony algorithmfor qos routing protocol in vanet,” in 2016 International Conference on Selected Topics in Mobile WirelessNetworking (MoWNeT), April 2016, pp. 1–8.

[40] F. Aadil, K. B. Bajwa, S. Khan, N. M. Chaudary, and A. Akram, “Caconet: Ant colony optimization(aco) based clustering algorithm for vanet,” PLOS ONE, vol. 11, no. 5, pp. 1–21, 05 2016. [Online].Available: https://doi.org/10.1371/journal.pone.0154080

[41] R. Kasana and S. Kumar, “A geographic routing algorithm based on cat swarm optimization for vehicularad-hoc networks,” in 2017 4th International Conference on Signal Processing and Integrated Networks(SPIN), Feb 2017, pp. 86–90.

[42] O. Kaiwartya and S. Kumar, “Geocasting in vehicular adhoc networks using particle swarm optimization,”ACM International Conference Proceeding Series, 05 2014.

[43] N. Hegde and S. S. Manvi, “Implementation of vanet routing using computational intelligence,” in 2016International Conference on Communication and Signal Processing (ICCSP), April 2016, pp. 0971–0975.

[44] S. Bitam, A. Mellouk, and S. Zeadally, “Hybr: A hybrid bio-inspired bee swarm routing protocol forsafety applications in vehicular ad hoc networks (vanets),” J. Syst. Archit., vol. 59, no. 10, pp. 953–967,Nov. 2013. [Online]. Available: http://dx.doi.org/10.1016/j.sysarc.2013.04.004

[45] R. R. Sahoo, R. Panda, D. K. Behera, and M. K. Naskar, “A trust based clustering with ant colony routingin vanet,” in Computing Communication Networking Technologies (ICCCNT), 2012 Third InternationalConference on, July 2012, pp. 1–8.

[46] “Intelligent Transport Systems (ITS); Vehicular Communications; GeoNetworking; Part 1: Require-ments,” International Organization for Standardization, Sophia Antipolis, FR, Standard, Mar. 2010.

[47] R. Riebl, “Open-source implementation of the etsi c-its protocol stack,” https://github.com/riebl/vanetza,2018.

[48] R. Riebl, H. J. Gunther, C. Facchi, and L. Wolf, “Artery: Extending veins for vanet applications,” in 2015International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS),June 2015, pp. 450–456.

[49] A. Bolufe-Rohler and S. Chen, “An analysis of sub-swarms in multi-swarm systems,” Lecture Notes inComputer Science, pp. 271–280, 12 2011.

59

https://doi.org/10.1007/978-3-540-92910-9_49

https://doi.org/10.1371/journal.pone.0154080

http://dx.doi.org/10.1016/j.sysarc.2013.04.004

https://github.com/riebl/vanetza

BIBLIOGRAPHY

[50] “Validation of OFDM error rate model in ns-3,” Boeing Research Technology, P.O. Box 3707, MC 7L-49,Seattle, WA 98124-2207, Report.

[51] “Tutorials/autobahn,” Jul 2017. [Online]. Available: http://sumo.dlr.de/wiki/Tutorials/Autobahn

[52] “Post och telestyrelsens homepage,” 01 2018. [Online]. Available: http://www.pts.se/sv/om-pts/

[53] “Cept – the european conference of postal and telecommunications administrations,” 07 2017. [Online].Available: https://cept.org/files/1047/CEPT%20Leaflet June%202017.pdf

[54] “Pts,” 02 2015. [Online]. Available: https://www.pts.se/globalassets/startpage/dokument/icke-legala-dokument/faktablad/radio/faktablad-undantag-tillstandsplikt-pts-f-2015 2 uppd1.pdf

[55] “Pts,” Apr 2015. [Online]. Available: https://www.pts.se/globalassets/startpage/dokument/legala-dokument/foreskrifter/radio/ptsfs-2015 4-undantag.pdf

[56] J. F. Pastrana, “802.11p standard and v2x applications on commercial wi-fi cards,” Master’s thesis, Uni-versidad De Valladolid, https://uvadoc.uva.es/handle/10324/23064, 2017.

[57] J. Carr, “An introduction to genetic algorithms,” 04 2014. [Online]. Available: https://www.whitman.edu/Documents/Academics/Mathematics/2014/carrjk.pdf

[58] M. X. Cheng, Combinatorial Optimization in Communication Networks, ser. Combinatorial Optimization,18, 2006.

60

http://sumo.dlr.de/wiki/Tutorials/Autobahn

http://www.pts.se/sv/om-pts/

https://cept.org/files/1047/CEPT%20Leaflet_June%202017.pdf

https://www.pts.se/globalassets/startpage/dokument/icke-legala-dokument/faktablad/radio/faktablad-undantag-tillstandsplikt-pts-f-2015_2_uppd1.pdf

https://www.pts.se/globalassets/startpage/dokument/icke-legala-dokument/faktablad/radio/faktablad-undantag-tillstandsplikt-pts-f-2015_2_uppd1.pdf

https://www.pts.se/globalassets/startpage/dokument/legala-dokument/foreskrifter/radio/ptsfs-2015_4-undantag.pdf

https://www.pts.se/globalassets/startpage/dokument/legala-dokument/foreskrifter/radio/ptsfs-2015_4-undantag.pdf

https://www.whitman.edu/Documents/Academics/Mathematics/2014/carrjk.pdf

https://www.whitman.edu/Documents/Academics/Mathematics/2014/carrjk.pdf

Appendices

61

Appendix A

ETSI GeoNetworking Data Structures

Figure A.1: Fields of Long Position Vector as specified in ETSI C-ITS EN 302 636-4-1 standards[18].

62

APPENDIX A. ETSI GEONETWORKING DATA STRUCTURES

Figure A.2: Fields of the GBC packet header as specified in ETSI C-ITS EN 302 636-4-1 standards[?]

63

www.kth.se