volume 53, issue 2 (may - aug), 2019 issn: 0008-6452caribjsci.com/gallery/is2.125.pdf · simulation...

AN EFFICIENT LIGHT GRADIENT BOOSTING ALGORITHM FOR ROUTE

DISCOVERY AND REDUCED ENERGY CONSUMPTION SCHEME IN VANET

S.P.Sasirekha1 and Dr N.MohanaSundaram2

1Research scholar, 2 Professor, Dept of CSE,

KAHE, Cbe-21, Tamil Nadu, India;

[email protected]

Abstract- In recent times, network service providers have attempted to widen their service to newly emerging

technologies, specifically in mobile cloud computing. Most vehicles and cars with effectual storage and

computers are equipped with innovative technologies like vehicular Ad hoc network (VANET), which is a novel

wireless technology. These abilities induce network service providers to use their ability to widen their network

service, i.e. Internet on roads. It is identified that there are sufficient materials and their associated peripherals to

design an effectual network. However, every new framework and architecture leads to certain problems and

limitations, like loss of data, routing issues etc. In this investigation, an optimal design to identify an appropriate

cluster head in a set of vehicles using Efficient Light Gradient Boosting algorithm (E-LGBA) is presented. With

a mathematical methodology and physical methods, a nested clustering can diminish data loss in real time

applications and at equipped traffic light crossroads. At last, the simulation is performed using MATLAB

environment to project the efficiency of the anticipated techniques and comparison is made with the prevailing

techniques. The proposed method shows better trade off than the existing techniques.

INDEX TERMS - VANET; Routing protocol; Efficient Light Gradient Boosting algorithm; Cluster head; Data

loss; Energy efficiency.

I. INTRODUCTION

In recent times, environmental pollution, increased material and reduced energy resources and damages result in

crashes, crises in management and transportation supervision outside and inside the cities, and increase in the

demands and necessity for more roads, specifically during rush hours, is considered a serious crisis [1][2].

VANETS are measured as subset of MANETs which are utilized for launching communications in intelligent

transport systems (ITSs). VANETs do not possess specific nodes and structures for establishing networks, as the

vehicles are in moving conditions [3]. Decision of drivers, continuous movement of vehicles and high speed

have laid the path for generation of unique characteristics in these kinds of networks (VANETs) [4]. Therefore,

effective and efficient routing protocols for data distribution in VANETs are measured to essential issues.

Without efficient and effective routing protocols, vehicles will be unable to share significant safety messages.

The ultimate objective of VANETs is to improve driving safety and offer comfort and ease for the passengers. It

is identified that VANETs are lagging in fixed infrastructure and continuous connections [5]. Also, there are no

fixed routes for mobile nodes during the network attachment and detachment. Certain noticeable VANET

functions are provided below: instant view of traffic, warning road signs, identification of road line, sensing

crashes via sensors, obstacles and pedestrians and recording events, distance between vehicles and estimating

speed etc. It is mentioned that data safety is the first and foremost challenge of VANETs [6]. Subsequently,

other challenges are related to routing information and communication establishing between high-speed vehicles

Moreover, it is assumed that VANET and wireless networks are affected by co-channel interference (CCI) and

shows fast transmission time variation capabilities of used Ad hoc links. The investigator in [7] examined

channel coding, network, coding, QoS-constrained jointly optimal adaptive power control and distributed source

coding for CCI restricted wireless multiple class multicast networks. CCI was designed in the veins of

simulation environment and CCI impact on communication delay and reliability for critical distances amongst

transmitter and receiver were examined under diverse co-channel interference/load settings. Amongst all the

existing protocols which are specifically designed for VANETs, clustering-based algorithm turns out to be a

more significant approach [8]. Because, these sort of protocols attempt to the acquire nodes’ mobility in

VANET and offer extremely stable and reliable units for safe data dissemination [9]. However, most of the

prevailing clustering-based strategies will not provide routing tasks for VANETs’ applications

Volume 53, ISSUE 2 (MAY - AUG), 2019

Caribbean Journal of Science

ISSN: 0008-6452

1652

In this investigation, clustering is considered as a baseline for achieving energy efficiency; routing algorithm

anticipated for VANETs is termed as Efficient Light Gradient Boosting algorithm (E-LGBA). This algorithm is

modelled as generation of cluster-based topology for producing suitable routes to transmit information amongst

sender and the receiver nodes [10]. Significantly, clustering amongst nodes is performed based on parameters

like coverage, ability, node density, direction of vehicle movement and speed in line with available relations in

clustering. At last, based on selection of gateway nodes, CH is chosen. As illustrated by experimental outcomes,

the anticipated scheme offers higher PDR.

1. Contribution of the work

In brief, the significant contributions of this investigation are given below:

(i) Designing cluster-based routing protocol for VANETs using Efficient Light Gradient Boosting

algorithm.

(ii) Enhancing packet delivery ratio by constructing clusters using E-LGBA algorithm and choosing

appropriate CH nodes with gateway selection.

(iii) Choosing suitable route discovery nodes for transmitting safety messages amongst cluster heads.

The remainder of the work is structured as follows: a short analysis of related works is provided in Section II,

explanation of the anticipated approach in detail in Section III, discussion of the results from anticipated method

in Section IV and conclusion and final remarks in Section V.

II. RELATED WORKS

Surmukh Singh, [2014] presents a few VANET protocols that might be a promising innovation for ITS.

Surmukh et al. have introduced a few uses of VANET. Advantages and disadvantages of protocols are

portrayed in brief. By understanding different routing protocols in VANET various traffic circumstances are

analyzed to check various features of routing protocols [11] [15]. This method demonstrates relative

examination of all routing protocols. Vehicular Ad hoc Network (VANET) and its related examination are

progressive stages. The practical deployable choices are entirely based on iterations and their actual executions.

Ahmed Yasse, [2017] proposed ITS solution depended on finding most adequate routing protocol in all KPIs

from investigation point of view, at that point, in contrast it and V2V + RSU execution so as to check if the

outcomes are worthy with present real life V2V + V2I usage in Europe and USA. AODV was selected due to its

advantages are higher than its disadvantages, numerous preliminaries were done to have these outcomes, the

default settings were adjusted for AODV routing protocol, beginning from utilizing the most satisfactory range

from Vehicle to Vehicle [12], reduced delay in intra-communication delay of vehicle utilizing inserted solution

during execution.

Rakesh Kumar, [2011] has anticipated forwarding strategy to determine routing decision of protocol when there

are packets to be sent. Delay Bounded protocols prior to forwarding strategy are utilized in routing protocols

with multi hop technique for transmission. Digital map offers traffic statistics and street level map like vehicle

speed and traffic density on road at various times. Digital map is essential for certain Cluster Based Routing

Protocols [13]. Virtual Infrastructure are constructed through nodes cluster to offer scalability. Every group has

cluster head, which is accountable for secure communication between intra-cluster coordination and inter cluster

network. Recovery technique is utilized to recuperate in unfavourable situations. Recovery procedure is utilized

to judge execution of the protocol.

Jie Luo, [2010] has examined unique features of urban VANETs and proposed enhancing system availability by

expanding the transmission scope of transports. At that point, Jie Luo introduced novel routing protocol MIBR

which exploits the transports as mobile backbone. The proposed protocol is topographical routing utilizing map

topology and transport line data to encourage route selection [14]. Moreover, "transport first" forwarding

technique is utilized as transmission between buses is bigger. Moreover, the algorithmic complexity of MIBR is

low, and deployment is simple because no static nodes or RSUs are required in MIBR.

Sabih ur Rehman, [2013] has presented a review and instructional exercise of different issues in VANET.

Different sorts of research difficulties are featured in the context of vehicular communication. Specifically, this

paper exhibits a review of VANET design, transmission modelling, scientific parts of signal modelling, routing

protocols and security. A relative examination of various routing algorithms in field of VANET has been

displayed. It additionally features the principal issues in routing algorithms [16]. Performance metrics for

routing protocols, discussed in this paper, are PDR concerning average speed of vehicles, node density and

framework throughput. Alternate parameters of interest discussed generally in the paper are average E2E and

routing overheads. The paper presumes that some calculations perform well in urban condition while others are



ISSN: 0008-6452

1653

reasonable for a highway environment. It is additionally reasoned that proper modelling techniques are essential

for structuring consistent communication in VANET for specific situation.

Shahirah Mohamed Hatim, [2018] presents a review on IoT-based VANETs to overcome the disadvantages of

conventional VANET. IoT as option, is required to help ITS with definitive objective of driving more secure

and progressively agreeable [17]. In any case, the necessities of research are insufficient in this topic.

Forthcoming ITS based IoT can recommend real-time responses to drivers [5]. Incorporating VANET with IoT

is to guarantee greater low-latency, flexibility and high-bandwidth communication.

Zahid Khan, [2017] has proposed an enhanced mobility factor GSF giving a better stochastic network of

vehicles, as well as a measurement for best route selection. Connectivity is a component of node density,

portability and transmission range. Every parameter is specifically identified with network. An exceptionally

congested traffic scenario will be connected strongly [18]. The network will increment up-to specific threshold;

however will begin after that threshold. Stochastic network is an ideal measurement for best route selection and

optimal vehicular cloud selection. Route is said to be ideal, if network is most extreme among all available

routes. Road segment having better availability for timeframe will be an ideal decision for vehicular cloud data

storage.

Anas Abu Taleb, [2018] has anticipated that VANET is an emergent field of research because of the different

applications that can make driving on roads increasingly secure. In this investigation, a special feature which

separates VANETs from different subclasses of Ad hoc networks is discussed. From that, VANETs’ needs and

applications are introduced [19]. Moreover, the diverse criteria that can be utilized to classify VANETs’ routing

protocols are discussed. Various routing protocols and designs proposed for VANETs are examined. At last, the

primary disadvantages and advantages of the fundamental classifications of routing protocols are exhibited.

Significantly, this paper does not cover all routing protocols and uses of VANETs because of the specific

expansive number of protocols.

Kishwer Abdul Khaliq, [2017] gives an adaptability of communication among vehicles and roadside units. For

smart device communication inside the vehicle, IoT is incorporated it with VANET. With basic advancements

of applications, vehicles can communicate with one another. The planned framework is centralized. With the

assistance of electromechanical device deployed inside vehicles, accident is identified. With the assistance of

bio-medical sensors, the framework guarantees the detection of accident location and the need of medical aid,

and an alarm message is produced for vehicles nearby about the accident, while also alerting control room

through VANET [20]. On receiving the emergency messages, the server discovers the closest medical centre

and creates messages for ambulance service for accident location. A rescue vehicle at that point is dispatched to

the accident spot. The protocol design is basic and, additionally, the hardware used is not costly. At present, a

group is working on the improvement of this protocol type.

Muhammad Rizwan Ghori, [2017] investigates numerous examinations identified with routing protocols.

According to the examination finished, AODV proves to be best routing protocol in VANET condition.

Additionally, video streaming is another testing assignment to accomplish in VANET [21]. As indicated by this

investigation, Muhammed et al. have inferred that parcel of exertion is required to get a decent quality video

spilling in VANET, and AODV again turns out to be better for video applications. For previously mentioned

statement, Muhammed et al. have done simulation utilizing two situations i.e., the basic and complex. In simple,

mobile nodes and took execution results of AODV and DSR. Likewise, in complex situation, RSUs added to

have increasingly far reaching results. Both previously mentioned circumstances have demonstrated that AODV

execution is much better than DSR.

II. PROPOSED METHOD

The system design of the proposed method is based on VANET clustering. In the initial stage, cluster generation

identifies an appropriate set of vehicles as in figure below [1]. In this step, a suitable method has to be selected

depending on the circumstances and problem constraint. For instance, some of the existing clustering techniques

are appropriate for crowded paths and suitable for considering lower density vehicles. In the subsequent step, an

appropriate CH has to be selected. CH is cluster manager and manages the relationship between Road side unit

and cluster.

In the anticipated technique, it is essential to reduce data loss that occurs during an equipped traffic-light

crossroad while transferring files from RSUs to vehicular applicants. For example, while applicants enquire

about files from internet, Road side unit will reply [22] [23]. However, if the requested file is huge, there is a

chance of data loss due to the increasing distance between the applicant and the RSU. With the proposed

approach, the requested file can be partitioned. With MCC, the requested file can be sent to the applicant as an



ISSN: 0008-6452

1654

appropriate scheme to transmit its cluster to reduce the cause of increasing distance between applicant and RSU

concurrently while transferring files with higher efficiency. In case of crossroads, the scenario is different. Here,

dissemination occurs amongst members in the cluster itself. To overcome this condition, appropriate members

inside the cluster have to be selected whose behaviour is similar to that of the intended applicants.

Fig 1: VANET architecture

In accordance to the proposed scheme, pseudo time division algorithm such as TDMA to generate cluster on

street .When a vehicle between t0 and t1 goes to a crossroad and travels to a connected street, Road side is

partitioned as cluster, one Initiator is selected and the cluster member list is transmitted to it with information

such as number of lanes. Initiator captures the position of every member and computes the plurality of lane

connected [24]. After that, Initiator chooses appropriate lane and the average speed of cluster members; it

chooses CH amongst vehicles that travel in selected lane. Subsequently, Initiator considers member position list

and cluster member list to select CH. With respect to crossroad conditions and street lanes quantity, CH

produces SCs and chooses member for every SC as SCH. After that, it initiates SCHs to Road side Unit and

transmits them to own SC and offers SC member list to SCH. Also, CH is SCH of its lane. In the subsequent

step, clusters are configured hierarchically. Henceforth, when member X1 requires file from RSU, it has to

enquire about its corresponding SCH initially. At that time, SCH will ask for file from RSU and leave its

member list from RSU. RSU partition data with pre-defined size and transmits it to all SC members. While

cluster arrives for intersection, during inevitable dissemination, there is no data loss theoretically as cluster

members contain file segments which follows data applicant.

Some general problems in the existing approaches are found and adequate solutions are provided to them as

given below:

– Traffic lights at crossroad

– Changing lane

Even though, applicants care about operating time by decreasing the amount of calculations, bringing the

overhead of RSU and CH down along with diminishing data loss on cloud computing during intersections is the

most significant aspect of the proposal [25]. In a similar state, these purposes have to be satisfied considering

the operating time.

a. Clustering and cluster head selection

Clusters are produced using RSUs in accordance with the proposed scheme. In this scenario, the vehicles

entering the street are clustered with time interval like (t0, t1). To identify entering vehicles, RSU has to compute

coordinates of every vehicle [26] [27]. There are certain popular techniques to describe co-ordinates of every

vehicle such as Received Signal Strength Indicator (RSSI). However, based on experimental outcomes, RSSI is

not considered as a reliable way for multipath environment. Henceforth, Ou, Chia-Ho schemes and the related

assumptions are used. It is free from GPS settings, and most current techniques uses coordinates of vehicle to

calculate reliability and precision.

Fig 2: Uniformly distributed VANET Fig 3: Clustered VANET

In RSU based localization strategy, all vehicles are equipped with digital odometer, VANET compass and

transceiver [28]. RSU1 and RSU2 with corresponding co-ordinates (x1, y1) and (xn, yn) respectively are installed

in middle position. RSUs’ coordinates are described using Global Positioning System (GPS). Also, RSUs cover

radio range with width of road. Henceforth Equation [1] shows the representation,



ISSN: 0008-6452

1655

𝑫 ≥ √(𝑳𝒔

𝟐)

𝟐

+ 𝑾𝒔𝟐 (1)

With this, strip lx is lane of the street. As well, if RSUs identifies vehicle coordinates in E, then add it to entrance

list as in equation [2],

𝒙𝒗𝒆 = 𝒙𝒓

𝟐− 𝒙𝟏𝟐+ 𝒅𝟏𝒗𝒆

𝟐 − 𝒅𝒓𝒗𝒆𝟐

𝟐𝒙𝒓− 𝟐𝒙𝒍

(2)

Based on the above equations, co-ordinates of vehicles are calculated as trails as in Equation [3] [4] & [5]:

= 𝒚𝟏 − √−𝒙𝒗𝒆𝟐 − 𝒙𝒍

𝟐 + 𝟐𝒙𝒍𝒙𝒗𝒆 + 𝒅𝟏𝒗𝒆𝟐 (3)

Where,

(𝒙 − 𝒙𝒍)𝟐 + (𝒚 − 𝒚𝒍

)𝟐 = 𝒅𝟏𝒗𝒆

𝟐 (4) (𝒙 − 𝒙𝒓)𝟐 + (𝒚 − 𝒚𝒓

)𝟐 = 𝒅𝒓𝒗𝒆

𝟐

(5)

As given in above equations, two hypothetical circles which originate from vehicle and beacon messages are

attained; Ve is preciously on intersection with subsequent conditions as in Equation [6] [7] & [8]:

𝒚𝒍 = 𝒚𝒓 (6)

𝒙 = 𝒙𝒗𝒆 (7)

𝒚 = 𝒚𝒗𝒆 (8)

Even though, the vehicle direction Ve is recognized using interior product to attain the angle amongst the road

direction ab and current vector movements Ve in constrast with angle amongst Ve and ba. Some set of points

are stored in road side units to identify vehicle directions Ve, as the interior product needs more computations

on RSUs [29]. Also, more recognition is needed to identify which lane vehicle Ve is on. Henceforth, it is finer

for the proposed scheme to utilize set of points indeed of using certain methods like interior product.

The Road side Unit responsibility is to generate clusters and to acquire members [30] for it in certain interval of

time. Whilst Vehicle Ve moves to its region R, RSUs gas to check whether vehicles are free to merge with this

cluster, and to change this as cluster member.

Algorithm_Node synchronization 1

a. Synchronize ()

b. {

c. Synchronize 𝑉𝑒 (𝑋𝑣𝑒, 𝑌𝑣𝑒); d. For (r=1; r=n; r++) //road loanes

e. { if 𝑉𝑒(𝑋𝑣𝑒, 𝑌𝑣𝑒) ∈ 𝑙𝑎𝑛𝑒𝑟)

f. {

g. return (𝑣𝑒 (𝑥𝑣𝑒, 𝑦𝑣𝑒), 𝑙𝑎𝑛𝑒𝑟); h. break;

i. }

j. }

k. Return 0;

l. }

Algorithm_Node clustering 2



ISSN: 0008-6452

1656

1. Clustering ()

2. {

3. J=0;

4. For ( t= 0; t = t+1; t++) //time period

5. If (synchronize (𝑉𝑒) ∈ R [ ])

6. If (update (Ve) = merge)

7. {

8. ClustermenberList[][][] ß Ve, synchronize

(Ve);

9. R ++;

10. }

11. }

12. I = rand ()% R +1;

13. Vinit = 𝑉𝑒𝑖; 14. Transmit 𝑣𝑒𝑖𝑛𝑖𝑡, 𝑐𝑙𝑢𝑠𝑡𝑒𝑟𝑚𝑒𝑚𝑏𝑒𝑟𝑙𝑖𝑠𝑡); 15. Transmit (Ve_init, totallanes);

16. Return 0;

}

Algorithm_cluster head election 3:

2. Electing_clusterhead()

3. {

4. Elect(nodeCH) = 0;

5. If (i=1; i=n;i++)

6. If (elect(nodeCH) < elect (nodei))

7. nodeCH = lanei;

8. elseif (elect(nodeCH) < elect (nodei))

9. nodeCH = rand (nodeCH, nodei);

10. }

11. CH = nodeCH.node1;

12. For (j=2; j=m; j++)

13. {

14. If(add(CH = nodeCH.node1) < add (CH =

nodeCH.node1))

15. CH = nodeCH.node1;

16. Elseif (add (CH = nodeCH.node1) = add(CH =

nodeCH.node1))

17. CH=rand(CH, CH = nodeCH.node1);

18. }

19. Return 0;

20. }

Only three velocity samples are needed by every vehicle to compute average speed, without more computation

and calculation like acceleration as, the general assumption is that vehicles are in urban region with constant

velocity. 𝐴𝑣𝑒𝑟𝑎𝑔𝑒𝑐 specifies average cluster speed, 𝜎𝑐 specifies standard deviation of Ve average speed. The

correlation amongst the network will be eliminated as every cluster member is included in the network [31]

[32]. In order to choose the appropriate CH, 𝑉𝑖𝑛𝑖𝑡 evaluates the number of nodes in every lane in a street. With the

attained node value and adjacency speed, Ch chooses the most appropriate cluster head in a cluster. When the

CH is elected, Vinit transmits the cluster member list and the associated information initially. Hence, it transmits

CH information to all cluster members and introduces them to RSUs. The equations below [9] [10] & [11]

depict the average cluster speed:

𝑨𝒗𝒆𝒓𝒂𝒈𝒆𝒗𝒆 = 𝟏

𝟑 ∑ 𝑽𝒊

𝟑𝒊=𝟎 (9)

𝑨𝒗𝒆𝒓𝒂𝒈𝒆𝒄 = 𝟏

𝟐 ∑ 𝑽𝒋

𝒏𝒋=𝟏 (10)



ISSN: 0008-6452

1657

𝝈𝒄 = √𝟏

𝒏 ∑ (𝑨𝒗𝒆𝒓𝒂𝒈𝒆𝒗𝒆 − 𝑽𝒋)

𝟐𝒏𝒋=𝟏 (11)

II. Node Clustering

The next section of the anticipated work is clustering. Cluster head (CH) is associated with cluster members and

Crossroad condition, and constructs clusters as sub cluster (SC) [33] [34]. After that, it chooses one amongst SC

member as Cluster head_sub. Hence, cluster head is equivalent to cluster head_sub.

For instance, consider a cross road condition CR. CR comprises four subsets. They are CR1, CR2, CR3 and CR4. Every

subset possesses its own condition. For example, CR2 is provided with two ways, and each way possesses two

lanes, i.e., CR2 has one way and four subsets of lanes. When the vehicle reaches the crossroad CR from CR3 it has

two choices: turn to right for intersection of CR1 to CR2. In addition, next vehicle possesses two choices: turn

towards CR3 and straight of CR2. At the same time, there is no choice for CR1 at cross road CR.

Fig 4: a) Cross road b) Cluster formation c) Data transmission amongst nodes

In some urban crossroads, there are some traffic lights, and henceforth, road should have threshold value near

the crossroad. In thresholding, vehicle should not change its lane. This is performed during the computation of

CR saturation.

Another functionality of crossroad is the estimation of weight in every region. Based on the information

gathered, the total number of vehicles in crossroad has been recognized using road side unit in certain time

interval. If road side unit identifies V1, V2, V3 and V4, with the corresponding lanes L1, L2, L3 and L4, then the

weights of LN can be computed as follows as in Equation 12 & 13:

𝑳𝑵 = 𝟏𝟎𝟎 (𝑽𝟏

𝑽𝑵

) , 𝒘𝒉𝒆𝒓𝒆 𝑵 = 𝟏, 𝟐, 𝟑, 𝟒 (12)

Where,

𝑽𝑵 = ∑ 𝑽𝒏𝟒𝑵=𝟏 (13)

Algorithm_cluster subset 4:

1. Generating cluster_subset()

2. {

3. For{ j=1; j=road.lane; j++)

4. {

5. If (source (lanei. Node) = source(lanei-1. Node))

6. Cluster_subset(j.1) [ ] ß lanej. Node;

7. Else

8. {

9. Cluster_subsetj [ ] ß lanej. Node;

10. Count+ =;

11. }

12. }

13. For (i=1; i=count; i++)

14. Id(clusterhead ≠ cluster_subseti [ ]

15. }

16. R =rand ( ) % cluster_subseti [ ]. Quantity +1

17. Clusterhead_subseti = cluster_subseti . node;

18. }



ISSN: 0008-6452

1658

19. Elseif (clusterhead 𝜖 cluster_subset [ ] )

20. Cluster_subseti = clusterhead;

21. }

22. Return 0;

23. }

The weighting condition and the associated proportion of every lane at crossroad are appropriate for the purpose

of distinguishing and to make probable computation of some similar choices.

Cluster head associated with cluster group and number of lanes generate cluster_subset. Fig. 5 cluster head

generates cluster_subset based on sum of lanes initially. Also, function source ( ) verifies the destination of

vehicles over lanej based on cluster group [35]. If the destination is identified to be in same lane, then both the

nodes of the lane belong to the same cluster subset. The source ( ) as well identifies the purpose of unequal state.

If vehicle Ve alters its lane after clustering, it should broadcast its related clusterhead_subset to eradicate it from

the cluster_subset list and from the cluster itself. After that, cluster head communicates with the road side unit

and the RSU has to respond to it with newer node to the cluster by function Generation ( ). After generation,

newer coordinates are introduced to the associated cluster_subset.

For example, if vehicle needs any file, it requests some information with the connected clusterhead_subset.

Clusterhead_subset requests its file from road side unit and provides the list of cluster_subset to road side unit

concurrently. RSU partitions file if needed. Subsequently, it broadcasts the file to cluster_subset node

comprising the applicant name. After that, cluster_subset nodes transfer the associated segments to vehicles.

Fig 5: Cluster head selection

When cluster head reaches threshold region, as illustrated in figure 5, it requests traffic light state from road side

unit (clr,T). Clr is traffic light colour; T is remaining time of colour in traffic light [36]. The algorithm shows, if

cluster head acquires green light, then it transmits to clusterhead_subset.

Based on figure, TCH is the time needed for cluster head to reach crossroad and it is computed as follows as in

Equation 14:

𝑻𝑪𝑯 = 𝒀

𝒂𝒗𝒆𝒓𝒂𝒈𝒆𝒄𝒍𝒖𝒔𝒕𝒆𝒓𝒉𝒆𝒂𝒅

(14)

As in equation [14] cluster_subset of cluster A, works in time duration T. If clusterhead_subset distribution in

cluster_subset at crossroad happens, then it generates clusterhead_subset and determines the member of

clusterhead_subset.



ISSN: 0008-6452

1659

Algorithm_traffic light monitoring 5:

17. Trafficlight ( )

18. {

19. Generate (VCH);

20. If (VCH ( xCH, yCH) ∈ Thresholding [ ])

21. Request_Trafficlight (clr, T);

22. If (clr = green)

23. If (TCH < T)

24. Transmit (T);

25. Return 0;

26. }

Algorithm_cluster subset 6:

1. Generate cluster_subset ( )

2. {

3. Cluster_subset1 [ ] = 0;

4. For ( i=1; i = cluster_subset.quantity; m++)

5. {

6. If (ti ≥ T)

7. Clusterhead_subset [ ] + = memberi;

8. }

9. If (clusterhead_subset1 [ ] ≠ 0)

10. {

11. clusterhead_subset2 [ ] = cluster_subset [ ] - clusterhead_subset1 [ ];

12. if (clusterhead_subset 𝜖 clusterhead_subset1 [ ] )

13. {

14. clusterhead_subset1 = clusterhead_subset;

15. r = rand ( ) % clusterhead_subset2.

16. clusterhead_subset1 = clusterhead_subset1 [ ]. Member;

17. }

18. Transmit (clusterhead_subset1 and clusterhead_subset2);

19. }

20. Return 0;

21. }

Vehicle in clusterhead_subset1 and the data format of clusterhead_subset2 nodes are carrying should be

transferred to clusterhead_subset1nodes [37].

The pseudo-code of generating and electing clusterhead_subset1 and clusterhead_subset2 is given above:

Using the number of cluster nodes in the cluster helps in transferring huge files to applicants and reduce the data

loss at crossroads and identifies the cause of data loss and provides certain solutions to have consistent data

transfer.

b. Efficient Light Gradient Boosting algorithm

In general, Gradient boosting decision tree (GBDT) is an extensively utilized machine learning algorithm, due to

its efficiency, interpretability and accuracy. Gradient boosting decision tree (GBDT) attains the state-of-the art

performance in numerous machine learning tasks like multi-class classification, learning to rank and click

prediction. In recent times, due to the emergence of big data (i.e. both number of features and number of

instances), GBDT is facing more challenges in terms of efficiency, accuracy and data transfer. Traditional

execution of GBDT has to scan all data instances (every feature) to evaluate information gain of all probable

points. Henceforth, the computational complexities will be proportional to both number of features and number

of instances. This leads the execution of GBDT a very time consuming process while handling big data.

To resolve this issue, in this work, an [Efficient Light Gradient Boosting algorithm] is designed, as a simple idea

to diminish the number of features and number of instances. However, this is a highly trivial process. For

instance, performing data sampling is clear while using E-LGBA. Here, this work concentrates on sampling the

data in accordance with weight of data and transmission speed during the process of boosting. This can be



ISSN: 0008-6452

1660

directly applied amongst clusters, which is not possible in traditional and state of the art techniques like GBDT

at all.

The principal focus of E-LGBA relies on the learning of weights in decision tree, and the time consumption part

in learning decision tree. Thus, optimal split points are acquired. The effectual method of finding the split points

is sorting the weights of decision tree, which describes the split points of weights associated with nodes on pre-

sorted feature values. This algorithm is easier and attains optimal split point values with respect to weights of

decision tree. Moreover, it is also effectual in terms of memory consumption and training speed.

Algorithm_ Efficient Light Gradient Boosting algorithm 7:

Algorithm: Efficient Light Gradient Boosting

algorithm

Input: D à training data; I à number of iterations; L à

sampling of large gradient data; S à sampling of small

Gradient data; data loss; training features

22. Modelling ß { }, computation ß 1−𝑎

𝑏

23. N ß L * length of (D), random number ß S *

length (D)

24. For i = 1 to d do

25. Prediction ß modelling large gradient.

Prediction (D)

26. L ß loss due to small gradient (D, prediction),

27. W ß { 1, 1, ...}

28. Perform large gradient sorting ß attain sorted

index (LS(g))

29. Topsort ß sorting [I:top N]

30. Randomset ß randompick of instances

(sorting [I:top N], random)

31. Computation ß topset and randomset;

32. Weight[randomset] = computation of weight

assigning to small gradient data

33. Newcomputation ß Features(D[topset] –

L[usedset], weight[usedset])

34. Models.attain(newmodel)

In general, large scale datasets are used for real time applications which are quite sparse in nature. E-LGBA uses

pre-sorted algorithm and reduces training cost by eliminating features with zero values. Moreover, E-LGBA

with histogram computation does not possess any influence on sparse optimization solutions. The reason behind

this is histogram-based computation has to retrieve features of bin value for the data instance, and does not care

about the feature value (i.e. zero or not). Hence, it is preferable to deal with the histogram based computation for

effectual leverage of such spare property.

In conventional Adaboost, sample weights provide a good indicator for importance of data instances. Moreover,

in GBDT, there is no sampling of weights, and therefore techniques proposed for Adaboost is not applied

directly. Providently, it is identified that gradient of data instance in GBDT provides useful data sampling

information. That is, if instance is related with small gradient, training error for those instances is smaller and it

can be trained well. An effectual idea is to eliminate samples with smaller gradients. But still, data distribution

will be modified during the process of instance elimination, which influences the learning model accuracy. To

overcome this issue, an Efficient Light Gradient Boosting algorithm [E-LGBA] is proposed with sampling the

one side instances.

The design of Efficient Light Gradient Boosting algorithm maintains all the instances with large gradients and

carries out random sampling on instances with small gradients. To deal with the data distribution influence,

information gain will be computed, in which a constant multiplier with small gradients are introduced for data

instances. Initially, this method sorts all data instances in accordance with absolute value of gradients from the

data that remains and selects the top instances. After performing sorting, sample data with small gradients with

constant (1-ab) is amplified during the computation of information gain. By performing this, the focus is

generally on under-trained instances without modifying original data distribution.



ISSN: 0008-6452

1661

The proposed Efficient Light Gradient Boosting algorithm comprises two novel approaches: Large gradient

based one side sampling of data instances and Effectual feature clustering to deal with a huge amount of data

instances and a huge number of features correspondingly. Experimental analysis is made on the above

mentioned approaches of the proposed techniques. The experimental outcomes are consistent, and projects that

with large gradient based one side sampling. The proposed E-LGBA outperforms existing SGB and XGBoost in

terms of memory consumption and computation speed. In future, the optimal selection process of features for

computing the constants using this one side sampling helps in enhancing the performance of Exclusive feature

clustering to access large number of features by eliminating the sparse property.

IV. RESULTS AND DISCUSSION

In this investigation, MATLAB software was utilized for simulating the anticipated technique. The parameters

related to simulation are given in the table I. In the current scenario, consider there are 150 vehicles that are

equal to the number of nodes available in a simulation environment. Maximum speed of every node is

considered to be 80 km/h. Here, maximum speed refers to original speed of vehicles, which can be any values

between 0 and 80 in case of simulation. Simulated outcomes are attained as an outcome of executing program

for 10 time’s average. The table below specifies the nodes that are distributed in simulation environment.

Table I: Simulation Parameter and associated values

S.No Parameter Value

1 Length of road 100 km

2 Lanes 4

3 Vehicles 150, 200, 250, 300

4 Maximum velocity 80, 90, 100

5 Strength for transmission 5 packets

6 Radio range 1000 m

7 Size of packet 1000 bytes

8 Execution time 100 s

c. Performance metrics

The following are the performance metrics utilized for efficient evaluation of proposed method.

d. Rout discover ratio (RDR)

Route discover ratio refers to fraction of number of route request packets received (RRi) to total number of

packets sent (RSi) by source vehicle. Average RDR value is attained with equation [15] given below:

𝑹𝑫𝑹 = 𝟏

𝑵 ∑ 𝑹𝑹𝒊

𝒏𝒊=𝟏

∑ 𝑹𝑺𝒊𝒏𝒊=𝟏

∗ 𝟏𝟎𝟎% (15)

e. Network throughput

Network throughput refers to fraction of total number of packets received at the receiver side during the

execution time. Here, Xi and Ps specifies number of packets received and packet size correspondingly. The

equation [16] given below shows the average network throughput for number of iterations.

𝑨𝒗𝒆𝒓𝒂𝒈𝒆 𝒕𝒉𝒓𝒐𝒖𝒈𝒉𝒑𝒖𝒕 = 𝟏

𝑵 (∑ 𝑿𝒊)∗𝑷𝒔

𝒏𝒊=𝟏

𝑺𝒕

∗ 𝟖

𝟏𝟎𝟎𝟎 ( 16)



ISSN: 0008-6452

1662

Fig 6: Graphical representation of Throughput computation

Table I: Tabular representation of Throughput values

f. Packet delivery rate (PDR)

This metric refers to the ratio of the total number of data packets (Xi) in the destination vehicle to the total

number of data packets (Yi) sent by the source vehicle. The average PDR for N experiments is obtained

𝑷𝑫𝑹 = 𝟏

𝑵 𝑿

∑ 𝑿𝒊𝒏𝒊=𝟏

∑ 𝒀𝒊𝒏𝒊=𝟏

∗ 𝟏𝟎𝟎% (17)

as clustering is carried out between vehicles using the finest evolutionary algorithms, and optimal clustering is

carried out with the proposed technique. Therefore, generating an appropriate routing cluster in the vehicular

network is optimized correspondingly. It is specified that clusters generation with a few members or one

member is eliminated. In this manner, suitable and relatively accurate routes are given by the anticipated

algorithm.

Fig7: Graphical representation of Packet Delivery ratio (PDR)

0

0.2

0.4

0.6

0.8

1

1.2

100 200 300 400 500

Th

rou

gh

pu

t (%

)

Number of nodes

Throughput computation

MOSIC

ACR

CBL

EGBCA

E-LGBA

0

20

40

60

80

100

100 200 300 400 500

Packet

Deliv

ery

rati

o

(%)

No of nodes

Packet delivery ratio

MOSIC

ACR

CBL

EGBCA

E-LGBA

MOSIC

0.55 0.57 0.62 0.67 0.7

ACR 0.64 0.68 0.72 0.78 0.8

CBL 0.74 0.77 0.79 0.82 0.85

EGBCA 0.82 0.84 0.86 0.88 0.9

E-

LGBA

0.9 0.9 0.95 0.96 0.95



ISSN: 0008-6452

1663

Table II: Tabular representation of PDR

MOSIC 51 55 65 72 81

ACR 42 51 58 68 80

CBL 38 43 52 62 76

EGBCA 24 29 38 49 59

E-LGBA 18 20 34 45 55

g. End to End delay

Estimating the throughput is a more precise approach in the anticipated method, as clustering is prone to

continuous changes with the increased vehicle speed. Therefore, such cluster head has to be chosen which works

for a longer time for network survival. Therefore, speed is specified using negative symbol in CH selection by

Efficient light gradient boosting algorithm while describing the output specification. In this case, the lower the

nodes’ speed the higher its priority of selecting the cluster head.

Fig 8: Graphical representation of End to End delay computation (E2E delay)

Table III: Tabular representation of End to End Delay

h. Routing overhead

It is obvious that successful packet delivery is achieved better in the E-LGBA method than in the prevailing

techniques. It is be noted that suitable clusters have been generated amongst the nodes in the efficient gradient

light boosting algorithm and cluster member nodes transmit their data only to cluster head. Moreover, in

primary data processing, cluster head eliminates broadcasting of repetitious data and redundant data. For

clustering purpose, anticipated scheme utilizes E-LBGA algorithm and measures significant parameters like

ability, node degree, speed and coverage of vehicle difference. On the other hand, with E-LBGA for selection of

CH node can also influence the cluster stability. Therefore, PDR of anticipated scheme is greater than the

existing methods.

0

2

4

6

8

100 200 300 400 500En

d t

o E

nd

dela

y

No of nodes

E2E computation

MOSIC

ACR

CBL

EGBCA

E-LGBA

MOSIC 3.3 4.2 5.3 6.4 7.2

ACR 2.4 3.2 4.1 5.3 6.5

CBL 1.2 2.5 3.6 4.8 6.2

EGBCA 0.9 1.8 2.7 3.9 5.4

E-LGBA 0.6 1.5 2.4 3.5 5



ISSN: 0008-6452

1664

Fig 9: Graphical representation of Routing overhead computation

Table IV: Tabular representation of RO computation

i. Average cluster head nodes

Consider a one-way road scenario where vehicles are uniformly distributed in a fixed simulation area. Figure 10 specifies

that when the number of vehicles increases in simulation environment it leads to decrease in cluster size with the use of E-

LGBA, and more nodes are placed in clusters. Network coverage and node distance from each other are considered in

clustering using the anticipated scheme. These parameters lead to the generation of denser clusters.

Fig 10: Graphical representation of Energy consumption

0

5

10

15

20

100 200 300 400 500

Ro

uti

ng

ov

erh

ead

No of nodes

Routing overhead

MOSIC

ACR

CBL

EGBCA

E-LGBA

0

10

20

30

40

100 200 300 400 500En

erg

y c

on

su

mp

tio

n

No of nodes

Energy consumption

MOSIC

ACR

CBL

EGBCA

E-LGBA

MOSIC 11.34 12.43 13.23 14.12 15.21

ACR 10.34 11.21 12.12 13.12 14.23

CBL 9.21 10.23 11.12 12.21 13.23

EGBCA 8.1 9 10.2 11.2 12.3

E-

LGBA

7.5 7 8.3 9.8 10.5



ISSN: 0008-6452

1665

Table V: Tabular representation of Energy computation

MOSIC 23.5 24 27.5 33.4 34.5

ACR 21 21.4 24.9 30 31.2

CBL 18.2 18.8 21.2 24 25.2

EGBCA 16.3 17.4 19.6 22.12 23.05

E-LGBA 14.5 15.8 18.5 20.14 21.5

Fig 11: Graphical representation of Route Discovery Time

Table VI: Tabular representation of Route discovery time

AODV 1.4 1.2 0.9 0.9 0.8 0.78 0.6

E-LGBA

without

RSU 1.2 1 0.8 0.6 0.4 0.2 0.1

E-LGBA

with

RSU 1 0.8 0.8 0.4 0.3 0.1 0.1

Fig 12: Graphical representation of weight Vs time computation

0

0.5

1

1.5

1 2 3 4 5 6 7

Ro

ute

Dis

co

very

tim

e

Time (secs)

Route Discovery Time

AODV

E-LGBA withoutRSU

E-LGBA with RSU

0

5000

10000

15000

20000

25000

30000

35000

1 2 3 4 5 6 7 8 9

Weig

ht

Time

Weight Vs Time

AODV

E-LGBA

without

RSU

E-

LGBA

with

RSU



ISSN: 0008-6452

1666

V.CONCLUSION

Routing based on information exchange is measured as a major challenge in vehicular network environment.

Therefore, modelling an effectual routing protocol for reliable and stable communication for vehicular network

is mandatory. In this investigation, the anticipated routing algorithm is based on clustering through the proposed

E-LGBA, and cluster heads are selected based on gateway node selection process. It is noted that the anticipated

approach was one amongst the machine learning algorithms. Therefore, it provides reasonable outcomes based

on clustering in various fields. The attained results specify that the anticipated algorithm offers higher PDR,

higher network throughput and route delivery ratio. The proposed method shows better trade off than the

existing techniques. As a future direction, the anticipated strategy can be tested under hybrid networks to offer

Internet access along with cloud computing connections and also for data controlling congestion and

aggregation for VANET applications.

REFERENCES

[1] M. Ren, L. Khoukhi, H. Labiod, J. Zhang, and V. V`eque, “A mobility-based scheme for dynamic

clustering in vehicular ad-hoc networks (VANETs),” Vehicular Communications, vol. 9, pp. 233–241,

2016.

[2] G. Wolny, “Modified DMAC clustering algorithm for VANETs,” in Proceedings of the International

Conference on Systems and Networks Communications, pp. 268–273, Sliema, Malta, October 2008.

[3] Z. Zhang, A. Boukerche, and R. Pazzi, “A novel multi-hop clustering scheme for vehicular ad-hoc

networks,” in Proceedings of the ACM International Workshop on Mobility Management & Wireless

Access (MobiWac), pp. 19–26, Miami Beach, Fl, USA, October-November 2011.

[4] A. Ziagham and M. R. Noorimehr, “MOSIC: mobility-aware single-hop clustering scheme for vehicular Ad

hoc networks on highways,” International Journal of Advanced Computer Science & Applications, vol. 7,

no. 9, 2016.

[5] X. M. Zhang, K. H. Chen, X. L. Cao, and K. S. Dan, “A streetcentric routing protocol based on

microtopology in vehicular Ad hoc networks,” IEEE Transactions on Vehicular Technology, vol. 65, no. 7,

pp. 5680–5694, 2016.

[6] M. A. Togou, A. Hafid, and L. Khoukhi, “SCRP: stable CDSbased routing protocol for urban vehicular Ad

hoc networks,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 5, pp. 1298–1307,

2016.

[7] D. Lin, J. Kang, A. Squicciarini, Y. Wu, S. Gurung, and O. Tonguz, “MoZo: a moving zone based routing

protocol using pure V2V communication in VANETs,” IEEE Transactions on Mobile Computing, vol. 16,

no. 5, pp. 1357–1370,2017.

[8] L. Rivoirard, M. Wahl, P. Sondi, M. Berbineau, and D. Gruyer, “Chain-branch-leaf: a clustering scheme for

vehicular networks using only V2V communications,” Ad hoc Networks, vol. 68, pp. 70–84, 2018.

[9] Z. He, D. Zhang, and J. Liang, “Cost-efficient sensory data transmission in heterogeneous software-defined

vehicular networks,” IEEE Sensors Journal, vol. 16, no. 20, pp. 7342–7354, 2016.

[10] F. Zeng, R. Zhang, X. Cheng, and L. Yang, “Channel prediction based scheduling for data dissemination in

VANETs,” IEEE Communications Letters, vol. 21, no. 6, pp. 1409–1412, 2017.

[11] Surmukh Singh, “VANET Routing Protocols: Issues and Challenges”, IEEE March, 2014.

[12] Ahmed Yasser, “Yasser et al., Cogent Engineering (2017), 4: 1362802”.

[13] 1Rakesh Kumar, “ A Comparative Study of Various Routing Protocols in VANET”, International Journal

of Computer Science Issues, Vol. 8, Issue 4, No 1, July 2011

[14] Jie Luo, “A Mobile Infrastructure Based VANET Routing Protocol in the Urban Environment”,

International Conference on Communications and Mobile Computing, 2010.

[15] Surmukh Singh, “VANET Routing Protocols: Issues and Challenges”, IEEE 2014.

[16] Sabih ur Rehman, “ Vehicular Ad-Hoc Networks (VANETs) - An Overview and Challenges”, Journal of

Wireless Networking and Communications 2013, 3(3): 29-38 DOI: 10.5923/j.jwnc.20130303.02

[17] Shahirah Mohamed Hatim, “ VANETs and Internet of Things (IoT): A Discussion”, Indonesian Journal of

Electrical Engineering and Computer Science Vol. 12, No. 1, October 2018, pp. 218~224 ISSN: 2502-4752,

DOI: 10.11591/ ijeecs.v12.i1.pp218-224

[18] Zahid Khan, “On the Connectivity of Vehicular Ad hoc Network

[19] Under Various Mobility Scenarios”, IEEE Access, November 7, 2017.

[20] Kishwer Abdul Khaliq, “Prototype of Automatic Accident Detection and

[21] Management in Vehicular Environment UsingVANET and IoT”, International Conference on Software,

Knowledge, Information Management and Applications (SKIMA), 2017.

[22] Lin D, Kang J, Squicciarini A,Wu Y, Gurung S, Tonguz O (2017) MoZo: a moving zone based routing

protocol using pure V2V communication in VANETs. IEEE Trans Mob Comput 16:1357–1370



ISSN: 0008-6452

1667

[23] M. Fathian and A. R. Jafarian-Moghaddam, “New clustering algorithms for vehicular ad-hoc network in a

highway communication environment,” Wireless Networks, vol. 21, no. 8, pp. 2765–2780, 2015.

[24] G. V. Rossi, F. Zhong, W. H. Chin, and K. K. Leung, “Stable clustering for ad-hoc vehicle networking,” in

Proceedings of the Wireless Communications and Networking Conference, pp. 1–6, Edinburgh, UK,

December 2017.

[25] S. Ucar, S. C. Ergen, and O. Ozkasap, “Multihop-cluster-based IEEE 802.11p and LTE hybrid architecture

for VANET safety message dissemination,” IEEE Transactions on Vehicular Technology, vol. 65, no. 4,

pp. 2621–2636, 2016.

[26] B. Liu, D. Jia, J. Wang, K. Lu, and L. Wu, “Cloud-assisted safety message dissemination in VANET-

cellular heterogeneous wireless network,” IEEE Systems Journal, vol. 99, pp. 1–12, 2017.

[27] P. Salvo, M. D. Felice, F. Cuomo, and A. Baiocchi, “Infotainment traffic flow dissemination in an urban

VANET,” in Proceedings of the Global Communications Conference, pp. 67–72, Atlanta, GA, USA,

December 2013.

[28] M. Oche, R. Md Noor, and A. Jalooli, “Quality of service management for IPTV services support in

VANETs: a performance evaluation study,” Wireless Networks, vol. 21, no. 1,pp. 315–328, 2015.

[29] J. Chen, C. Lai, X. Meng, J. Xu, and H. Hu, “Clustering moving objects in spatial networks,” in

Proceedings of the International Conference on Database Systems for Advanced Applications, pp. 611–623,

Bangkok, thailand, April 2007.

[30] C. Shea, B. Hassanabadi, and S. Valaee, “Mobility-based clustering in VANETs using affinity

propagation,” in Proceedings of the Global Telecommunications Conference, pp. 1–6, FL, USA, December

2010.

[31] S. Almalag Mohammad and C. Weigle Michele, “Using traffic flow for cluster formation in vehicular ad-

hoc networks,” in Proceedings of the Local Computer Networks, pp. 631–636, Denver, CO, USA, October

2010.

[32] M. M. C. Morales, C. S. Hong, and Y. C. Bang, “An adaptable mobility-aware clustering algorithm in

vehicular networks,” in Proceedings of the Network Operations and Management Symposium, pp. 1–6,

Maui, MI, USA, April 201.

[33] Ayaida M, Barhoumi M, Fouchal H, Ghamri-Doudane Y, Afilal L (2014) Joint routing and locationbased

service in VANETs. J Parallel Distrib Comput 74:2077–2087

[34] Ardakani, S. P. (2018). ACR: a cluster-based routing protocol for VANET. arXiv preprint

arXiv:1805.07565.

[35] Ghasemnezhad S, Ghaffari A (2018) Fuzzy logic based reliable and real-time routing protocol for mobile

Ad hoc networks. Wireless Pers Commun 98(1):593–611



ISSN: 0008-6452

1668

volume 53, issue 2 (may - aug), 2019 issn: 0008-6452caribjsci.com/gallery/is2.125.pdf · simulation...

Documents