natural computing for vehicular networks

Doctoral Thesis

Natural Computing for Vehicular Networks

Programa Oficial de Postgrado de Tecnologías Informáticas

Departamento de Lenguajes y Ciencias de la Computación

Universidad de Málaga

January 15th, 2016

Author: Jamal [email protected]

Supervisor:Dr. Enrique Alba

[email protected]

Vehicular Networks Natural Computing and VANET Optimization

2. Fundamentals

Jamal Toutouh Natural Computing for Vehicular Networks

Outline

Outline Motivation

1. Introduction

4. Conclusions and Future Work

3.4 Real World VANET Experiments

3. Methodology: Optimization and Experimentation in VANETs

Introduction Fundamentals Methodology Conclusions & Future WorkOutline

3.2 On-line VANET Optimization

3.3 Smart Roadside Unit Placement

3.1 Off-line VANET Optimization

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Vehicular Networks Natural Computing and VANETs Optimization

2. Fundamentals


Outline

Outline Motivation

1. Introduction


Introduction Fundamentals Methodology Conclusions & Future WorkMotivation






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VEHICLE-TO-VEHICLE COMMUNICATION COULD CUT DOWN ON THE CRASH RATE BY 80 PERCENT NATIONWIDE


Vehicular networks represent an emerging technology that allow the development of advanced road transport applications that could significantly improve our daily life and the global economy

MotivationIntroduction Fundamentals Methodology Conclusions & Future Work

Motivation

U.S. Department of TransportationHitachi Social Innovation (2016)

There are relevant problems that limit their deployment

We propose the application of Natural Computing to address such problems because it comprises a set of versatile, flexible, and efficient methods

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2. Fundamentals


Outline

Outline Motivation

1. Introduction

Introduction Fundamentals Methodology Conclusions & Future WorkVehicular Networks







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Road transport is capital for the quality of life in our modern society ...but it offers important challenges

IntroductionVehicular Networks

More than 25,500 people were killed last year + human and property damages

Greenhouse gas emissions (20% of the total CO2)

Population discomfort + costs about 1% of GDP

SAFETY ENVIRONMENT EFFICIENCY

Intelligent Transportation Systems are being developed to mitigate such issues. Could Natural Computing help here?

Introduction Fundamentals Methodology Conclusions & Future Work

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Vehicular ad hoc networks (VANETs) interconnect vehicles and infrastructure elements with each other

Communication technologies: DSRC (IEEE 802.11p), LTE, …

Vehicular NetworksVehicular Networks

Connectivity will provide more precise knowledge of the traffic situation across the entire road system- Optimize traffic flows - Reduce hazardous situations (accidents)- Minimize pollution - Make more comfortable road trips


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File transferring and data routing dynamism in topology changes connection loss hard QoS constraints

Beacon broadcasting (safety applications) network congestion hard QoS constraints (reliability)

Hardware platform deployment limited budget

Network performance evaluation complexity of achieving realistic simulations resources limitation to define outdoor testbeds

Open Challenges Analyzed Vehicular Networks


Our goal: address them as optimization problems

Our means: Intelligent systems, simulation and real tests

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2. Fundamentals


Outline

Outline Motivation

1. Introduction


Introduction Fundamentals Methodology Conclusions & Future WorkNatural Computing and VANETs Optimization







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An Optimization Problem can be defined as a pair (S,f):S is called solution/search spacef is a quality criterion known as objective/fitness function

s* is the global optimum element s*S iff f(s*) ≤ f(s) sS

Single-objective Optimization ProblemIntroduction Fundamentals Methodology Conclusions & Future Work

Natural Computing and VANETs Optimization

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Multi-objective Optimization: Find a set of solutions which satisfy constraints and optimize

a vector of objective functions in conflict with each other

Multi-objective Optimization ProblemIntroduction Fundamentals Methodology Conclusions & Future Work


w dominates u and v Non-dominated solutions

The goal is to find a set of non-dominated solutions (Pareto optimal set) whose projection in the objectives domain is called Pareto optimal front

e.g., minimizing both, f1(x) and f2(x)

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Natural Computing (NC) takes the inspiration from nature to design algorithms for the solution of complex problems Flexible (wide application) and versatile for many problems Non-deterministic and approximate methods (statistics needed!)

Efficiently explore the search space to find (near-)optimal solutions


Natural Computing for Optimization ProblemsIntroduction Fundamentals Methodology Conclusions & Future Work


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NC Algorithms Applied in this ThesisIntroduction Fundamentals Methodology Conclusions & Future Work


Evolutionary Algorithms

Swarm Intelligence

Population based

Genetic Algorithm (GA)Differential Evolution (DE)Evolution Strategies (ES)

Non-dominated Sorting Genetic Algorithm-II (NSGA-II)

Particle Swarm Optimization (PSO)

Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO)

Simulated Annealing (SA)

Multi-objectiveoptimization

Single-objectiveoptimization

Trajectory based

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File transferring and data routing (before use)Four off-line optimization problems were defined to compute efficient protocol configurations that improve the QoS and reduce resources consumption

PSO, DE, GA, ES, SA, NSGA-II, and SMPSO

Beacon broadcasting (during use)An on-line optimization problem was defined to keep the network load under a given threshold while maximizing the data exchange

Heuristic Greedy Methods

Hardware platform deployment (before use)A multi-objective optimization problem was proposed to compute RSU platform designs that maximize the service provided and minimize deployment the costs

NSGA-II

The Problems Solved in this PhD ThesisNatural Computing and VANETs Optimization


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1. Formulating the optimization problem

2. Defining realistic VANET instances to evaluate tentative solutions (ns-2, ns-3, SUMO, …)

3. Selecting/building the NC algorithms (operators) to address the defined optimization problem (MALLBA, jMetal, MATLAB, EJB, …)

4. Performing (at least) 30 independent runs of each experiment and analyzing the statistical meaning of the results:

quality/performance indicators + statistical tests (SPSS, R, …)

5. Validating the solutions against state of the artQoS/resources consumption metrics + statistical tests

Global MethodologyNatural Computing and VANETs Optimization


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Optimization and Experimentation in VANETs

Outline


2. Fundamentals Outline Motivation

1. Introduction







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Off-line VANET Optimization

Outline




1. Introduction






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Off-line VANET Optimization – Introduction

Introduction

Goal: Find efficient protocol configurations that optimize their performance and resources consumption

Search algorithm

new solution

fitness value metrics

Solution evaluation

protocol conf.

fitness(s)

s

output

Improved protocol configuration

NCALGORITHM ns-2/ns-3

VANET simulationcommunication metrics

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Optimized protocols (cases of use)VDTP (file transfer protocol); OLSR and AODV (routing protocols)

Metrics

Realistic VANET instances using real information of Málaga SUMO + OpenStreetMap road traffic generation ns-2/ns-3 network performance evaluation

Urban VANET scenarios Highway VANET scenarios

One VANET simulation needs several minutes



Off-line VANET Optimization – Introduction

Introduction

File transfer protocol: Transmission time Number of lost packets Total data transferred Transmission data rate

Routing protocol: Packet delivery ratio (PDR) Normalized routing load (NRL) End-to-end delay (E2ED) Energy consumption

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QoS targets:transmission timelost packetstotal data transferred



Off-line VANET Optimization – Data Transfer Between Vehicles with Improved QoS

VDTP File Transfer Off-line Optimization

PROBLEM 1: Optimize the QoS of VDTP file transfers

VDTP is a file transfer protocol based on stop and wait operation

VDTP parameters:chunk size [128 … 524,288] in bytesretransmission time [1.0 … 10.0] in seconds max attempts [1 … 250]

Problem encoding:

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Experimental Analysis

PSO DE GA ES SA0.0

1.0

2.0

3.0Urban instance

Fina

l com

mun

icati

on

cost

val

ues

PSO DE GA ES SA0.0

2.0

4.0

6.0Highway instance

Fina

l com

mun

icati

on

cost

val

ues

Algorithm Friedman Rank

PSO (best) 1.27DE 1.83GA 3.07ES 4.33SA 4.50


SA 1.87GA 1.97PSO 2.63DE 3.57ES 4.97

Scalability analysis: PSO and GA present the best results

Wilc

oxon

p-v

alue

> 0

.01

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Validation

Urban scenario: PSO achieves the best performance Highway scenario:

PSO provides the highest transmission data ratesSA and GA lose the lowest number of packets

NC provides more efficient file transfers than human experts

PSO DE GA ES SA

EXPERTS0

50100150200250300

Urban scenario

Tran

smiss

ion

Data

Rat

e (k

B/s)

PSO DE GA ES SA

EXPER

TS0

10

20

30

40

Highway scenario

Tran

smiss

ion

Data

Rat

e (k

B/s)


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OLSR QoS Off-line Optimization

PROBLEM 2: OLSR QoS optimization Find the parameter configuration of OLSR that provides the best QoS

PDR NRL E2ED

Problem encoding:

Objective function or communication cost ( minimization ) :𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑐𝑎𝑡𝑖𝑜𝑛𝐶𝑜𝑠𝑡 (𝑠 )=𝑤2 ∙𝑁𝑅𝐿 (𝑠 )+𝑤3 ∙𝐸2𝐸𝐷 (𝑠 )−𝑤1 ∙𝑃𝐷𝑅(𝑠)


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Off-line VANET Optimization – Optimization of the QoS of Proactive Routing


The most competitive algorithm in optimization SA


SA (best) 1.40DE 2.10PSO 2.50GA 4.33RAND 4.50

SA DE PSO GA RAND

-0.50 -0.45 -0.40 -0.35 -0.30 -0.25 -0.20 -0.15 -0.10 -0.05 0.00

Fina

l com

mun

icati

on

cost

val

ues

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Performance Evaluation

The evolution of the best solution during the median run During the last iterations all the algorithms improve their solutions

Execution time per single run is between 12.11 and 32.66 hours PSO and DE spend shorter computation times

0 6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 96

-0.47

-0.42

-0.37

-0.32DE

PSO

GA

SA

RAND

Number of ( ) evaluations (x 10)𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑐𝑎𝑡𝑖𝑜𝑛𝐶𝑜𝑠𝑡 𝑠

Com

mun

icati

on c

ost (

fitne

ss)

DE

GA

SA

PSORAND

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Validation Validation over U1 urban VANET scenario

Computed configurations: NC (SA, DE, PSO, and GA) and RANDState-of-the-art OLSR configurations

PDR: NC 100% NRL: SA, DE, and PSO <5% E2ED: SA the shortest delays

SA: The best trade-off among QoS metrics

Validation over 54 urban VANET scenarios NC configurations offer a competitive trade-off among the QoS metrics

High PDR (>84%) Low NRL (<16.5%) Short E2ED <10.3 ms(reliability) (scalability) (delays)

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Off-line VANET Optimization – Power Aware Proactive Routing for VANETs

Power Aware OLSR Off-line Optimization

PROBLEM 3: Energy-Efficient OLSR (EE-OLSR) optimization Find the OLSR configuration that reduces the power consumption

(keeping the QoS PDREE-OLSR degradation lower than 15%)

Objective function ( minimization ) :𝑓 (𝑠 )=∆+(𝜔1∙𝑒𝑛𝑒𝑟𝑔𝑦𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 (𝑠 )−𝜔2 ∙𝑃𝐷𝑅(𝑠))

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Problem encoding:




Experimental Analysis Master-slave parallel GA: pGA-8, pGA-16, and pGA-24

Diagonal Uniform Initialization and OLSR µ-Mutation

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GA-8 pGA-8 pGA-16 pGA-240.65

0.67

0.69

0.71

0.73

0.75

0.77

Fina

l f(s

) val

ues

Kruskal-Wallis test pGA-24 is significantly the best (highest energy savings)

PDREE-OLSR degradation < 13%

Parallel GAs show an almost-linear speedup behavior

GA-8 pGA-8 pGA-16 pGA-2422%

24%

26%

28%

30%

32%

Ener

gy G

AP

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Validation Validation experiments over 36 VANET scenarios (U2 and U3)

pGA-8 pGA-16 pGA-2420%

25%

30%

35%

40%

45%

33.3% 34.3% 35.2%

42.6% 41.9%44.4%

38.3% 38.3%40.2%

U2 U3 Overall

Ener

gy G

AP


pGA-24 (best) 1.92pGA-16 1.94pGA-8 2.94RFC 3.94

pGA-24: average energy GAP of 40.2% pGAs outperformed SoA (GAP<32%)

NC EE-OLSR provides high energy savings while keeping PDR and improving E2ED

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Off-line VANET Optimization – Efficient QoS for Reactive Data Routing for VANETs

Multi-objective AODV Off-line Optimization

PROBLEM 4: Multi-objective QoS optimization of AODV Define a multi-objective formulation in order to find a set of

efficient AODV configurations

PDR E2ED Conflicting objectives

Problem encoding:

Objective functions (minimization)

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Off-line VANET Optimization – Efficient QoS for Reactive Data Routing for VANETs

Experimental Analysis NC algorithms: pNSGA-II and pSMPSO with ad hoc operators The pNSGA-II solutions are better distributed along the Pareto front

(statistically better spread) Most of pNSGA-II solutions dominate the pSMPSO ones (statistically

better epsilon)

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Introduction Fundamentals Methodology Conclusions & Future WorkOff-line VANET Optimization – Summary

Off-line VANET Optimization Summary

File transfer optimization: NC improved the file transferring service regarding the

experts’ configurations

Routing optimization: NC offered high PDRs, while highly reducing the routing load

and the message delivery times NC improved the energy efficiency

New VANET services are possible thanks to better design achieved by using automatic NC techniques

This methodology can be directly applied over any VANET protocol

Jamal Toutouh Natural Computing for Vehicular Networks 33 / 58


On-line Optimization of Vehicular Communications

Outline





1. Introduction






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On-line Optimization of VANETS – Introduction

Broadcasting On-line Optimization

Fair (Balanced) Beacon Rate (FBR) broadcasting:a) keeping the VANET load under a given threshold (α)b) avoiding starvation of the nodes c) balancing beacon rates

Beacon broadcasting suffers from network congestion Congestion control by adapting the beacon rates of each node

according to the current network status

Time t Time t + Δ

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On-line Optimization of VANETS – Fair Beacon Rate Optimization Problem

Fair Beacon Rate Optimization Problem

PROBLEM: FBR optimization Goal: Adapt beacon rates of each node v to allow FBR

Maximizing the channel occupancy which represents the quantity of the beacons traveling through the shared medium

Minimizing the difference between the beacon rates in the neighborhood

max η( v )=max( ∑

𝑗

𝑗∈𝑁𝑁 (𝑣 )

𝑏𝑟 𝑗)+𝑏𝑟𝑣𝑀𝑎𝑥𝑄

min σ ( v )=min( ∑

𝑗

𝑗 ∈𝑁𝑁 (𝑣 )

(𝑏𝑟 𝑗−𝑏𝑟 𝑣 )2)+(𝑏𝑟 𝑣−𝑏𝑟𝑣 )2

|𝑁𝑁 (𝑣)|+1

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On-line Optimization of VANETS – FREEDY algorithms

FREEDY algorithms

We have devised FREEDY (Fair Rate grEEDY) algorithms that dynamically compute accurate beacon rates to address FBR optimization problem:- fully distributed no central manager entity- low-cost computations (fast)

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On-line Optimization of VANETS – FREEDY algorithms

FREEDY algorithms

‹ network overload‹ neighborhood size

Self FREEDYSelf o-FREEDYSelf n-FREEDY

Swarm FREEDYSwarm o-FREEDYSwarm n-FREEDY

Swarm o-FREEDY-modSwarm n-FREEDY-mod

Swarm o-FREEDY-medSwarm n-FREEDY-med

mode medianstatistic metric ›

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On-line Optimization of VANETS – Experimental Analysis

Experimental Analysis 9 Highway VANET scenarios: beacon rates from 1 to 10 Hz and α = 0.8

0.2

0.4

0.6

0.8

1.0

0

0.1

0.2

0.3

0.4

0.5Channel occupancy Network balance

High density traffic scenarios

α

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On-line Optimization of VANETS – Sumamary

Summary

Swarm n-FREEDY-mod

Self o-FREEDY

Simulation output (MATLAB):

high and balanced beacon rates

high beacon rates, but low balance

0 Hz

10 Hz

Beaconrates

Swarm n-FREEDY-mod is significantly (Friedman + Wilcoxon) the most competitive in terms of both analyzed metrics

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No congestion control

unbalanced beacon rates and starvation


Smart Roadside Unit Placement

Outline





1. Introduction






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Smart Roadside Unit Placement – Introduction

Smart Roadside Unit Placement

PROBLEM: RSU deployment problem (RSU-DP) Goal: Decide the type and the location of the RSUs to maximize

the service provided and minimize the deployment costs

RSUs placed over any location within the road segment

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Smart Roadside Unit Placement – Implementation Details

Implementation Details

Problem encoding: Vector of real numbers with n elements (n = number of segments) RSU type: integer part of real number (0 stands for no RSU and

from 1 to k represent types from 1 to tk, respectively.)

Position within the segment: fractional part of the real number maps the interval [0,1) to points in segment [pj, pk)

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Objective functions: f1 Service time (QoS) considers the number of vehicles and

the speed in the covered segments

f2 Total cost sums the costs of the selected RSUs (according to RSU type)

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Ad hoc mutation operator:a) remove RSU (if any)

b) change RSU type

c) changing RSU position (Gaussian mutation)

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Smart Roadside Unit Placement – Experimental Analysis

Experimental Analysis Master-slave parallel NSGA-II Comparison against two randomized heuristic greedy algorithms:

Greedy Qos (GQoS) and Greedy cost (GCost) Road traffic information:

Málaga with three road traffic densities: low, normal, and high

type radio range costt1 243.12m 121.7$

t2 338.70m 139.2$

t3 503.93m 227.5$

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Smart Roadside Unit Placement – Experimental Analysis

Numerical Results

The NSGA-II Pareto front dominates the greedy solutions

normal traffic

best solutions

Improvements of QoS up to 6.0% over GQoS (with the same cost)

Reductions of cost up to 37.1% over GCost (with the same QoS)

NC provides RSU designs with higher QoS and lower deployment costs

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Real World VANET Experiments

Outline





1. Introduction






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Real World VANET Experiments – Performance Analysis of Improved VDTP

Exp1: Performance Analysis of Improved VDTP

Experiments to compare the real performance of the VDTP by using the CARLINK human experts’ configurations with the ones computed by using NC

VDTP Testbed definition: Málaga open roads 2 VANET nodes Different file types/multiple file transfers

Configuration Friedman RankPSO (best) 4.26ES 3.60GA 3.54DE 3.51SA 3.18EXPERTS 2.92

Multimedia content sharing and on-line gaming are possible (we did some!)

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Real World VANET Experiments – Lightweight Personal Devices for VANETs

Exp2: Lightweight Personal Devices for VANETs The idea is to prove the feasibility of VANET Wi-Fi short range

communications when using smartphones, tablets, and laptops

Testbed definition: Málaga open roads 2 VANET nodes Multiple data streams

IEEE 802.11g IEEE 802.11a

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Real World VANET Experiments – Performance Analysis of Improved VDTP

Experimental Results

Smartphones allow information to be exchanged with nearby nodes (at distances up to 75 m): yes, they are actually useful!

Tablets exchange more information than smartphones (at distances up to 125 m): common sense, but now proved with data

Laptops (IEEE 802.11g) are able to exchange multimedia information at high data rates (at distances up to 150 m)

Laptops (IEEE 802.11a) obtain the least competitive results

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Conclusions

Outline





1. Introduction






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Conclusions

ConclusionsIntroduction Fundamentals Methodology Conclusions & Future Work

File transferring and data routing Off-line optimization Coupling NC and VANET simulation represents an efficient and

flexible methodology to optimize (actually improve) protocols for vehicular communications. Using NC leads to: File transfers at higher data rates allowing efficient data

exchange Packet routing with improved performance (higher delivery

ratio and lower communication delays) and reduced resources consumption (network load and energy)

Using together multi-objective and parallel algorithms is necessary to deal with complex real problems

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Conclusions


Hardware platform deployment NC improved the service provided by the RSUs installed

throughout the city scenario (Málaga), while minimizing the required budget

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Beacon broadcasting On-line optimization Fair Beacon Rate broadcasting improved the

accuracy of beaconing based VANET (safety) applications: increasing and balancing the channel usage while avoiding network congestion at the same time


Network performance evaluation The realism of the simulations performed in our experimentation

allowed us to perform accurate computations Real world outdoor experiments confirmed the efficiency of

VDTP when using NC-computed configurations Personal devices can be used to exchange information with

VANET nodes depending on the application requirements

Conclusions


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Future Work

Future WorkIntroduction Fundamentals Methodology Conclusions & Future Work

File transferring and data routing Off-line optimization Generalizing the use of multi-objective and parallel algorithms to

address VANET optimization problems Confirming in vitro (real world experiments) the results obtained

in silico (the laboratory)

Beacon broadcasting On-line optimization Extending FREEDY algorithms by including:

advanced swarm intelligence operators different beacon based applications (priorities)

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Future Work

Future WorkIntroduction Fundamentals Methodology Conclusions & Future Work

Hardware platform deployment Extending the experimental analysis of RSU-DP to other

geographical areas and considering additional traffic relevant information (POIs, dangerous areas, …)

Network performance evaluation Including devices with IEEE 802.11p wireless interfaces Evaluating routing and broadcasting approaches with more than

two VANET nodes

Making actual transference of our findings to industry and foster vehicular communications in European cities

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Doctoral Thesis


Universidad de Málaga

January 15th, 2016



[email protected]

CommentsIntroduction Fundamentals Methodology Conclusions & Future Work


Comments and Questions

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Doctoral Thesis




[email protected]



Thank you very much for your time!

natural computing for vehicular networks

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