An Optimal Route Discovery using Biogeography based Optimization for Vehicular Ad Hoc Networks

International Journal of Computer Networks & Communications (IJCNC) Vol.13, No.1, January 2021
DOI: 10.5121/ijcnc.2021.13102 19
AN OPTIMAL ROUTE DISCOVERY USING
BIOGEOGRAPHY BASED OPTIMIZATION FOR
VEHICULAR AD HOC NETWORKS
Shaeista Begum1
, Nagaraj B. Patil2
and Vishwanath P3
1
Department of Computer Science and Engineering,
Government Engineering College, Raichur, India
2
Department of Computer Science and Engineering,
Government Engineering College, Gangavathi, India
3
Department of Electronics and Communication Engineering,
H.K.E.S’s S.L.N. College Of Engineering, Raichur, India
ABSTRACT
Vehicular ad hoc network (VANET) is a subdivision of the mobile ad hoc networks which uses the moving
vehicles as mobile nodes to form the mobile network. In conventional vehicular communications, the
restricted radio frequency bandwidth affects the network performances. Therefore, Visible Light
Communication (VLC) is integrated with the growing vehicular ad hoc network to obtain high data rate
and less energy consumption during the communication. In this paper, vehicular communication is
integrated with visible light communication to avoid the issues caused by the restricted radio frequency
bandwidth. Moreover, the Routing using Biogeography Based Optimization (RBBO) is proposed to develop
an optimal route between the source vehicles to the destination. This research performs two different
communications such as vehicle to vehicle and vehicle to the infrastructure. The performance of the RBBO-
VLC-VANET method is analyzed by means of throughput, packet delivery ratio, delay and routing
overhead as well as these performances are compared with the existing method namely ant colony
optimization based routing protocol. The throughput of the routing using the biogeography based
optimization method is 589.763 kbps for 500 nodes which is high when compared to the existing method.
KEYWORDS
Biogeography Based Optimization, Delay, Optimal Route, Throughput, Vehicular Ad Hoc Network, Visible
Light Communication.
1. INTRODUCTION
VANET is one of the special types of mobile ad hoc networks (MANETs) which allows vehicles
to generate the self-organizing mobile wireless network without any pre-existing infrastructure
[1] [2] [3]. The vehicles of the VANET are act as a node or router which is used to exchange the
data among the access points and vehicles [4]. The main goal of the VANET is to develop an
Intelligent Transport System (ITS) to provide numerous general applications such as vehicle
safety, collision avoidance, traffic regulation and protecting the blind while crossing the roads [5]
[6]. The VANET has two components such as vehicle and the roadside infrastructures. Since, the
communication between the vehicles are named as vehicle to vehicle (V2V) communication
whereas the communication among the vehicle and roadside infrastructure is called as Vehicle to
Infrastructure (V2I) communication [7] [8]. The nodes of the VANET are equipped with On-

20
Board Unit (OBU) at a standard position for supporting the aforementioned V2V and V2I
communications [9] [10].
The routing between the vehicular connections is difficult due to the vehicles movement and
frequent changes in the network topology. The network expansion over the huge scale causes the
higher routing overheads for the routing protocols [11] [12]. Moreover, the data transmission
through the VANET is affected by different constraints such as non-uniformity of vehicle density
and intermittent connection [13]. Conventionally, the VANET uses the Radio Frequency (RF) for
supporting the communication among the vehicular communications. In RF-based VANET, the
efficiency of the network is affected, when the interference avoidance cost is increased with
respect to the vehicle density. Additionally, the network performance is restrained based on the
restricted RF bandwidth. Therefore, the VLC is utilized in the VANET for supporting the
VANET communications due to its different advantages of high spatial reuse ratio, wide free-
licensed spectrum and higher power efficiency [14]. The VLC provides reliable and energy-
efficient data transmission compared to the RF. However, the network is affected by the unequal
load distribution through the network and data heterogeneity [15]. The major contributions of this
research paper are given as follows:
 The VANET is integrated with the VLC for avoiding the multiple distortions while
transmitting the data packets.
 The Routing using Biogeography Based Optimization (RBBO) is proposed to generate
the optimal route between the source to the destination. This route generation is
optimized by considering the multiple objectives such as residual energy, distance and
number of hops.
 The proposed RBBO-VLC-VANET is used to obtain two different communications such
as V2V and V2I and also it minimizes the delay and increases the throughput over the
VLC-VANET.
The major contributions of this paper are given as follows: The literature survey about the
existing routing methods over the VANET is given in the section 2. The problems found from the
literature survey and solution to overcome that problem is described in the section 3. The route
generation using the biogeography-based optimization over the VLC-VANET is explained in the
section 4. The results and discussion of the RBBO-VLC-VANET is given in the section 5.
Finally, the conclusion is made in section 6.
2. LITERATURE SURVEY
Chinnasamy et al. [16] presented the Wagon Next Point Routing Protocol (WNPRP) to generate a
continuous and reliable data transmission between the vehicles. The Hello Message was
frequently transmitted between the adjacent wagons (i.e., vehicles) to obtain significant
information about adjacent vehicles. There are three different information was obtained using
Hello Message transmission such as direction, location and speed of the vehicle. This collected
information was used in the WNPRP to maintain the connectivity and constant stability among
the source and destination. The developed WNPRP was analyzed only for lesser number of
nodes. For an effective VANET communication, the network area should be analyzed with a
higher number of vehicles.
Aravindhan and Dhas [17] developed the hybrid clustering and destination-aware routing
protocol to obtain an optimal cluster head and route between the vehicles. In hybrid clustering,
the clusters were formed by integrating the geographic and context-based clustering method.
Next, the inter-clustering routing was obtained using the destination-aware routing protocol. The

21
direction and destination of cluster heads were considered during the optimal cluster head
selection as well as this cluster head was used to generate the optimal route. Therefore, the
developed clustering and routing algorithm was used to minimize the traffic and delay in the
VANET communication. The amount of control message transmission was very high with
respect to increase the number of clusters.
Srivastava et al. [18] presented the Adaptive Intersection Selection Mechanism (ASIM) using Ant
Colony Optimization (ACO) to generate the routing path based on the constraints of the multiple
quality of service. This ASIM was followed two strategies: At first, the real time road evaluation
was obtained based in the prediction-based mechanism. Next, the route among the two adjacent
vehicles was identified instead of detecting the longer route between the nodes. The higher data
transmission and reduction in unwanted path identification were obtained using this ASIM. The
priority of the candidate route selection was considered only the delay and connectivity, it failed
to consider the energy in the routing process.
Masini et al. [19] developed the applications of vehicular crowd sensing, where the vehicles were
utilized to gather the urban data required by the data centers. The vehicles in the network were
considered as generating the data packets which contains the information about the vehicles.
Here, both the VLC and IEEE 802.11p were integrated and also it was used individually during
the communication. The multi-hop communication was initialized, when there is no possible
intermediate connection between the vehicles. The number of packets delivered to the Road Side
Unit (RSU) was affected due to the high directivity between the transmitter and receiver.
Gawas and Govekar [20] presented the Selective Cross-Layer design-based Ant Colony
Optimized Ad-Hoc On Demand Distance Vector (SCL-ACO-AODV) routing algorithm was
developed to generate the route through the VANET. The difficulty during the route generation
was avoided by ACO and allocation of slot was obtained using the Particle Swarm Optimization
(PSO). The AODV was used to initialize the data transmission between the source and
destination. The redundant traffic congestion and confusion were minimized using this SCL-
ACO-AODV method. Moreover, the performance of the SCL-ACO-AODV was mainly based in
the selected intermediate nodes and candidate routing paths as well as the selection of
intermediate node considers only the distance and energy of the nodes.
3. PROBLEM STATEMENT
The problems found from the existing researches and solution to solve that problem using
RBBO-VLC-VANET is described in this section.
The inappropriate fitness function selection during the route generation causes the packet loss
through the network. The route selection through the ASIM [18] considers only the delay and
connectivity for data transmission. This ASIM doesn’t consider the energy of the nodes, because
of this the node with limited battery power affects the number of packets received in the
destination. Moreover, the SCL-ACO-AODV [20] considers only the distance and energy during
the candidate path selection. The amount of packets received at the RSU is minimized due to the
reduced connectivity in the VLC communication [19].
Solution:
In this RBBO-VLC-VANET method, the Biogeography Based Optimization (BBO) is used to
generate the routing path for transmitting the data packets between the source vehicle to the RSU.
The multiple objective values considered to generate the optimal route are residual energy,

22
distance and number of hops. This helps to increase the number of packets received by the
destination. Moreover, the shortest path generation using the RBBO minimizes the delay while
transmitting the data packets.
4. RBBO-VLC-VANET METHOD
In this RBBO-VLC-VANET, an optimal path between the source vehicle and the RSU is
obtained by using the BBO algorithm. This BBO is optimized by using the residual energy,
distance and number of hops. Here, the data transmission between the vehicles is carried out
through the VLC channel. This VLC data transmission helps to avoid the multiple distortions
during the data transmission. This RBBO-VLC-VANET transmits the data between the vehicles
and vehicle to infrastructure namely V2V and V2I. Moreover, this RBBO-VLC-VANET is used
to transmit the high amount of data packets with less packet loss. The flowchart of the RBBO-
VLC-VANET method is shown in the Figure 1.
Figure 1. Flowchart of the RBBO-VLC-VANET method
4.1. VLC-VANET system model
The VLC-VANET system model is comprised of vehicles as mobile nodes and lighting sources
as transmitting/receiving sources. In this VLC-VANET, both the lighting infrastructure and
mobile nodes are equipped with the transmitter and receiver to transmit and receive the
information from the adjacent vehicles. For example, the VLC transmitter and receiver are
equipped with the headlights and brake lights of a vehicle. The VLC transmitter and VLC
receiver are connected through the free space optical communication channel. The main
requirement of the VLC is the Line-of-Sight (LoS) especially in outdoor vehicle communications.

23
The light emitted from the transmitter contains the data packets which are transferred through the
wireless medium. Here, the direct LoS is used as communication technology between the
vehicles which used to minimize the multipath distortion during data transmission. The channel
model of this VLC-VANET is expressed in the equation (1).
 
       
2
1
cos cos 0
2
0
b
c
Los
c
B A
T g
H Dist
     

 
 
 

 
 

(1)
Where, b is the order of Lambertian emission; A is the detector area; distance between one
vehicle to other vehicle is Dist ; optical band-pass filter of transmission is  
T  ; concentrator
gain is  
g  , receiver’s field of view is c
 ; angle of impedance is  and angle of irradiance
is .
4.2. Identification of routing path using BBO
The location of nodes deployed in the VLC-VANET is given as input to the BBO algorithm for
generating the route between the source vehicle to the destination vehicle. The multiple
objectives considered in this RBBO-VLC-VANET method are residual energy, distance and
number of BS. This section provides an overview of BBO and route generation using BBO.
4.2.1. Overview of biogeography based optimization
Initially, the BBO is developed by Dan Simon in 2008 [21]. BBO is generally inspired by the
geographical assignment of biological species and the Habitat Suitability Index (HSI) is used to
specify each geographical zone of BBO. An extra index utilized to mention the habitat area and
conditions of livelihood is specified as Suitability Index Variable (SIV). The HSI value and
amount of species is equal to the habitat’s fitness value. The features from the solution of higher
HSI are accepted for enhancing lower HSI solution. The immigration and emigration rate of the
habitat are and
  are used to specify the single species model. This immigration and emigration
rate is shown in the equation (2) and (3) respectively.
1
s
k
I
n

 
 
 
 
(2)
Where, the maximum immigration rate is represented as I ; number of species in the habitat is
represented as k and maximum amount of species in the habitat is s
n .
s
E k
n


 (3)
Where, E represents the maximum emigration rate. The processes of the BBO algorithm are
given as follows:
4.2.1.1. Migration
Consider the candidate solutions and the optimization problem for the BBO initialization, where
each solution specified using the dimension vector  
n is called as habitat. Let, the habitat’s

24
dimension in each dimension is SIV as well as the habitat’s goodness is equal to the amount of
species and HSI value. The low HSI solution shares the information with higher HSI solution to
improve the solution obtained during the searching process. The transmission of HSI solution
depends on the immigration and emigration rate and the two different habitats are selected from
the BBO population. Initially, one habitat  
i
H is selected by considering the immigration rate
 
i
 and one more habitat  
j
H is selected by considering the emigration rate  
j
 .
Subsequently, the random selection of SIVs are transferred from  
j
H and appears in  
i
H .
4.2.1.2. Mutation
Generally, the rapid changes in habitat and deviation from the equilibrium location are occurred
due to natural disasters caused in the geographical region. Consequently, the same effect is
illustrated in the BBO using the operation of mutation. This mutation operation is accomplished
using the number of species in each habitat, which is shown in equation (4) and (5). The value of
probability is allocated for each habitat to perform the mutation operation. The possibility for
mutation is less and a solution is closer to the optimal solution, when the probability value is high
in the BBO. However, the possibility of mutation is high and a solution is far from the optimal
solution, when the probability value is low.
 
 
 
1 1
1 1 1 1 max
1 1 max
, 0
1 1
s s s s s
h
S s s s s s s s
s s s s s
P P s
P P P P s s
P P s s
  
   
  
 
   
 
    

       

    

(4)
  max
max
1 s
P
m s m
P
 

  
 
(5)
Where, the higher amount of species in the BBO is max
s ; mutation rate of s species is max
s ;
maximum mutation rate and probability are represented as max
m and max
P respectively.
4.2.2. Optimal path generation using BBO
This BBO is used to generate the optimal route to achieve a high amount of packets received at
the destination RSU. The issues related to frequent changes in network topology and high density
of vehicles are overcome by generating the optimal path and transmitting the data through the
VLC channel.
4.2.2.1. Representation and initialization of BBO
The habitats of the BBO are specified by the possible paths from the source vehicle to the RSU.
Moreover, the dimension of the habitat is identical to its number of intermediate vehicles in the
routing path. Consider, the habitat i is      
 
,1 ,2 ,
, ,...
i i i i m
H H t H t H t
 , whereas the next hop
vehicle is specified as , ,1
i v
H v m
  .
4.2.2.2. Fitness function formulation for BBO
In this RBBO-VLC-VANET method, three different objective values such as residual energy,
distance between one vehicle node to another vehicle node and number of hops connected to each
vehicle node.

25
a. Residual energy
The residual energy  
RE of each node is considered as a significant fitness value, because it
shows the amount of energy exist in each node. This used to select only the node which has
higher residual energy while transmitting the data packets. Because, the node with limited energy
creates node/link failure during data transmission which causes the packet loss. The following
equation (6) shows the residual energy.
1
i
m
H
i
RE E

  (6)
Where, mrepresents the number of vehicles in the routing path and i
H
E represents the residual
energy of the th
i vehicle in the habitat H .
b. Distance
The distance  
Dist between the one vehicle to another vehicle is considered to select the shortest
path between the source vehicle to destination RSU. Moreover, this shortest path identification is
used to minimize the delay during communication. Equation (7) expresses the distance between
the vehicles.
 
1
,
m
i
i
Dist dis H DV

  (7)
Where, destination vehicle is represented as DV as well as distance between the th
i vehicle in
the habitat H and DV is represented as  
,
i
dis H DV .
c. Number of hops
The amount of Next Hop Vehicle (NHV) to the respective node defines the number of hops
 
NH . The number of hops is required to be less, because it causes higher energy consumption
in VANET.
1
i
m
H
i
NH M

  (8)
Where, i
H
M represents the amount of nodes connected in the path.
In this fitness function derivation, the multiple objectives are converted into single objective by
assigning weighted value to each objective value. Equation (9) shows the HSI/fitness value of the
BBO (5).
1 2 3
HSI RE Dist NH
  
      (9)
Where, the 1 2 3
, and
   represents the weighted values used in the fitness function. The
calculated values of HSI are used to update the immigration and emigration rate of each habitat.

26
4.2.2.3. Migration and mutation
The immigration and emigration rate are used to select two different habitats during the migration
process. Next, the NHV from the higher HSI solution has appeared in lower HSI solution. Here,
one location is randomly created among the 1 and th
m dimension to accomplish the migration
process. From the generated location, all NHV from the j
H appears in i
H . Therefore, the habitats
are updated until the optimal solution is obtained in the BBO. The habitat i
H considered using
the mutation probability. The selection of habitat is identified using the emigration and
immigration rate. The probability of selecting the habitat is less, when the mutation probability is
high during mutation process. Otherwise, the probability of selecting the habitat is high, when the
mutation probability is less during mutation process. In habitat i
H , the randomly selected
location changes its NHV by selecting the random NHV within its transmission range.
5. RESULTS AND DISCUSSION
The results and discussion of the proposed RBBO-VLC-VANET are described in this section.
The implementation and simulation of the RBBO-VLC-VANET is carried out in the MATLAB
R2018a which is operated in the in a Windows 8 operating system with an Intel core i3 processor
and 4GB RAM. In this RBBO-VLC-VANET, the VLC is used as a communication channel for
supporting the data transmission between the vehicles. The routing path between the source
vehicle to the RSU is generated by using the BBO. The number of vehicles deployed in the
network area are varied as 50, 100, 200 and 500 which are deployed in the area of 2
500 500m
 .
The specification parameters of this RBBO-VLC-VANET method are given in Table 1.
Table 1. Specification parameters
Parameter Value
Area 2
500 500m

Number of vehicles 50, 100, 200 and 500
RSU location Center of the network area
Transmission range 250m
Communication channel VLC
Packet size 4000 bits
Routing protocol BBO
5.1. Performance analysis
The performance of the RBBO-VLC-VANET is analyzed in terms of the Throughput, Packet
Delivery Ratio (PDR), delay and routing overhead. Next, the RBBO-VLC-VANET is evaluated
with SCL-ACO-AODV [20] to show the efficiency of RBBO-VLC-VANET. The performance
analysis is described as follows:
5.1.1. Throughput
Throughput is defined as a number of packets successfully received at the RSU during the
simulation rounds. Generally, throughput is measured as kilobits per second or Megabits per
second.

27
Figure 2. Comparison of throughput for varying nodes
The throughput comparison of the RBBO-VLC-VANET with SCL-ACO-AODV [20] is shown in
the Figure 2. Figure 2 shows that the RBBO-VLC-VANET method obtains higher throughput
than the SCL-ACO-AODV [20]. For example, the throughput of the RBBO-VLC-VANET
method is 589.763 kbps for 500 nodes which is high when compared to the SCL-ACO-AODV
[20] method’s throughput i.e., 540 kbps. The elimination of multiple distortions using VLC and
optimal path generation using BBO are used to achieve the higher throughput in the RBBO-VLC-
VANET method.
5.1.2. Packet Delivery Ratio
PDR is the ratio between the Number of packets successfully received at the RSU to the total
packets generate at the source vehicle. Expression for PDR is shown in the equation (10).
Amountof Packetssucessfullyreceived attheRSU
PDR
Total packetstransmitted
 (10)
Figure 3 shows the comparison of the PDR for the RBBO-VLC-VANET with SCL-ACO-AODV
[20]. This comparison shows that the RBBO-VLC-VANET method has higher PDR than the
SCL-ACO-AODV [20]. For example, the PDR of the RBBO-VLC-VANET method is 98.998%
which is high when compared to the SCL-ACO-AODV [20]. The integration of VLC into the
VANET is used to increase the number of packets received by the RSU. Moreover, the SCL-
ACO-AODV [20] doesn’t consider the appropriate fitness function value which leads to cause the
packet loss during route generation.

28
Figure 3. Comparison of PDR for varying nodes
5.1.3. Packets’ delay
Delay is the total required to transmit the data packet from the source to the destination which is
expressed in the equation (11).
’
Totaltimerequiredtotransmitthedata packets
Delay
Amountof packetssucessfullyreceived attheRSU
Packets  (11)
Figure 4. Comparison of delay for varying nodes
The delay comparison of the RBBO-VLC-VANET with SCL-ACO-AODV [20] is shown in the
Figure 4. Figure 4 shows that the RBBO-VLC-VANET method achieves lesser delay than the
SCL-ACO-AODV [20]. For example, the delay of the RBBO-VLC-VANET method is 0.0783s
for 500 nodes which is less when compared to the SCL-ACO-AODV [20] method’s delay i.e.,
0.75s. The delay during the data transmission is less by identifying the shortest path between the

29
source vehicle to the destination. But, the SCL-ACO-AODV [20] has a higher delay because of
the traffic that occurred in the VANET.
5.1.4. Routing overhead
Routing overhead is defined as the ratio between the number of control packets and amount of
packets successfully received at the RSU. The routing overhead is expressed in the following
equation (12).
Amountof control packets
Routingoverhead
Amountof packetssucessfullyreceived attheRSU
 (12)
Figure 5. Comparison of routing overhead for varying nodes
Figure 5 shows the comparison of the routing overhead for the RBBO-VLC-VANET with SCL-
ACO-AODV [20]. This comparison shows that the RBBO-VLC-VANET method has lesser
routing overhead than the SCL-ACO-AODV [20]. For example, the routing overhead of the
RBBO-VLC-VANET method is 220.2752 which is less when compared to the routing overhead
of SCL-ACO-AODV [20] i.e., 490. The optimal route generation using BBO through the VLC-
VANET is used to minimize the routing overhead while transmitting the data packets.
Table 2. Comparative analysis of the RBBO-VLC-VANET with SCL-ACO-AODV
Number
of nodes
Throughput (kbps) PDR (%) Delay (s) Routing overhead
SCL-
ACO-
AODV
[20]
RBBO-
VLC-
VANET
SCL-
ACO-
AODV
[20]
RBBO-
VLC-
VANET
SCL-
ACO-
AODV
[20]
RBBO-
VLC-
VANET
SCL-
ACO-
AODV
[20]
RBBO-
VLC-
VANET
50 590 598.939 88 99.821 0.5 0.075 430 22.423
100 550 598.38 95 99.797 0.85 0.064 390 45.087
200 520 595.335 90 99.594 0.7 0.074 380 88.208
500 540 589.763 98 98.998 0.75 0.078 490 220.275

30
Table 2 shows the comparative analysis of the RBBO-VLC-VANET with SCL-ACO-AODV [20]
for a different numbers of nodes such as 50, 100, 200 and 500. From the Table 2, it shows that
the performance of the RBBO-VLC-VANET is improved than the SCL-ACO-AODV [20]. The
SCL-ACO-AODV [20] has lesser performance due to its inappropriate fitness function
formulation during route selection. The integration of VLC with VANET is used to increase the
number of packets received at the RSU. The multiple objectives such as residual energy, distance
and number of hops are considered in the BBO to identify the optimal route between source
vehicle to RSU. The packet loss during the data communication is minimized by avoiding the
node failure in the routing path. Therefore, the number of packets received at the RSU is
increased through the VANET. The distance considered in the fitness function helps to detect the
shortest path which used to minimize the delay in VANET communication.
6. CONCLUSION
In this RBBO-VLC-VANET, the VLC is integrated with the VANET to achieve the higher
throughput during communication. The BBO based route generation is proposed to obtain the
optimal path between the source vehicle to the destination RSU. Here, the BBO is optimized by
using the three different objective values such as residual energy, distance and number of hops.
Therefore, this optimal path generation is used to overcome the issues related to frequent changes
in network topology and node failure. This RBBO-VLC-VANET method is used to obtain the
higher throughput and lesser delay by identifying the optimal path. Therefore, the proposed
RBBO-VLC-VANET is operated in both the 4G and 5G networks. From the performance
analysis, it knows that the RBBO-VLC-VANET method provides better performance than the
existing SCL-ACO-AODV method. The throughput of the RBBO-VLC-VANET is 589.763 kbps
for 500 nodes which is high when compared to the throughput of SCL-ACO-AODV i.e.,
540kbps.
CONFLICTS OF INTEREST
The authors declare no conflict of interest.
REFERENCES
[1] Liu, H., Yang, L. & Zhang, Y. (2015) “Improved AODV routing protocol based on restricted
broadcasting by communication zones in large-scale VANET”, Arabian Journal for Science and
Engineering, Vol. 40, No. 3, pp857-872.
[2] Li, N., Martínez-Ortega, J.F., Díaz, V.H. & Fernandez, J.A.S. (2018) “Probability prediction-based
reliable and efficient opportunistic routing algorithm for VANETs”, IEEE/ACM Transactions on
Networking, Vol. 26, No. 4, pp.1933-1947.
[3] Al-Kharasani, N.M., Zukarnain, Z.A., Subramaniam, S.K. & Hanapi, Z.M. (2020) “An Adaptive
Relay Selection Scheme for Enhancing Network Stability in VANETs”, IEEE Access, Vol. 8,
pp128757-128765.
[4] Suganthi, B. & Ramamoorthy, P. (2020) “An Advanced Fitness Based Routing Protocol for
Improving QoS in VANET”, Wireless Personal Communications, pp1-23.
[5] Purkait, R. & Tripathi, S. (2020) “Fuzzy logic based multi-criteria intelligent forward routing in
VANET”, Wireless Personal Communications, Vol. 111, No. 3, pp1871-1897.
[6] Wagh, M.B. & Gomathi, N. (2018) “Route discovery for vehicular ad hoc networks using modified
lion algorithm”, Alexandria engineering journal, Vol. 57, No. 4, pp3075-3087.
[7] Bello-Salau, H., Aibinu, A.M., Wang, Z., Onumanyi, A.J., Onwuka, E.N. & Dukiya, J.J. (2019) “An
optimized routing algorithm for vehicle ad-hoc networks”, Engineering Science and Technology, an
International Journal, Vol. 22, No. 3, pp754-766.
[8] Zhang, D., Zhang, T. & Liu, X. (2019) “Novel self-adaptive routing service algorithm for application
in VANET”, Applied Intelligence, Vol. 49, No. 5, pp1866-1879.

31
[9] Patil, S.D. & Ragha, L. (2020) “Adaptive fuzzy-based message dissemination and micro-artificial bee
colony algorithm optimised routing scheme for vehicular ad hoc network”, IET Communications,
Vol. 14, No. 6, pp.994-1004.
[10] Abuashour, A. & Kadoch, M. (2017) “Performance improvement of cluster-based routing protocol in
VANET”, IEEE Access, Vol. 5, pp15354-15371.
[11] Moridi, E. & Barati, H. (2017) “RMRPTS: a reliable multi-level routing protocol with tabu search in
VANET”, Telecommunication Systems, Vol. 65, No. 1, pp127-137.
[12] Chen, C., Liu, L., Qiu, T., Yang, K., Gong, F. & Song, H. (2018) “Asgr: An artificial spider-web-
based geographic routing in heterogeneous vehicular networks”, IEEE Transactions on Intelligent
Transportation Systems, Vol. 20, No. 5, pp1604-1620.
[13] Wu, J., Fang, M., Li, H. & Li, X. (2020) “RSU-Assisted Traffic-Aware Routing Based on
Reinforcement Learning for Urban Vanets”, IEEE Access, Vol. 8, pp5733-5748.
[14] Chen, J. & Wang, Z. (2019) “Topology Control in Hybrid VLC/RF Vehicular Ad-Hoc Network”,
IEEE Transactions on Wireless Communications, Vol. 19, No. 3, pp1965-1976.
[15] Msongaleli, D.L. & Kucuk, K. (2019) “Optimal resource utilisation algorithm for visible light
communication-based vehicular ad-hoc networks”, IET Intelligent Transport Systems, Vol. 14, No. 2,
pp65-72.
[16] Chinnasamy, A., Prakash, S. & Selvakumari, P. (2016) “Wagon next point routing protocol (wnprp)
in vanet “, Wireless Personal Communications, Vol. 90, No. 2, pp875-887.
[17] Aravindhan, K. & Dhas, C.S.G. (2019) “Destination-aware context-based routing protocol with
hybrid soft computing cluster algorithm for VANET”, Soft Computing, Vol. 23, No. 8, pp2499-
2507.
[18] Srivastava, A., Prakash, A. & Tripathi, R. (2020) “An adaptive intersection selection mechanism
using ant Colony optimization for efficient data dissemination in urban VANET”, Peer-to-Peer
Networking and Applications, pp1-19.
[19] Masini, B.M., Bazzi, A. & Zanella, A. (2018) “Vehicular visible light networks for urban mobile
crowd sensing”, Sensors, Vol. 18, No. 4, p1177.
[20] Gawas, M.A. & Govekar, S.S. (2019) “A novel selective cross layer based routing scheme using
ACO method for vehicular networks”, Journal of Network and Computer Applications, Vol. 143,
pp34-46.
[21] Verma, J. and Kesswani, N. (2018) “BIGM: A Biogeography Inspired Group Mobility Model for
Mobile Ad Hoc Networks”, International Journal of Wireless Information Networks, Vol. 25, No. 4,
pp.488-505.

32
AUTHORS
Shaeista Begum perceived her B.E from Bapuji Institute of Engineering and
Technology, Davangere, Karnataka in 2005 and M.Tech from University of BDT
College of Engineering, Davangere, Karnataka in 2007. From 2007 to 2008 she worked
as a lecturer in GM Institute of Technology, Davangere. From 2008 to 2010 worked as
a lecturer in BKIT, Bhalki, Karnataka. From 2010 to 2011 worked as a lecturer in
Women’s Government Polytechnic, Gulbarga. Presently working as an Assistant
Professor in Government Engineering College, Raichur since 2011. Her research area
is Computer Network.
Nagaraj B. Patil received his B.E. degree from the Gulbarga University Gulbarga
Karnataka in the 1993, M.Tech. degree from the AAIDU Allahabad in 2005, and the
Ph.D. degree from the University of Singhania, Rajasthan India in 2012. From 1993 to
2010 he worked as a Lecturer, Senior Lecturer and Assistant professor and HOD Dept.
of CSE & ISE at SLN College of Engineering, Raichur Karnataka. From 2010 to June
2019 he worked as a an Associate Professor and HOD in the Department of Computer
Science and Engineering at Government Engineering College Raichur, Karnataka. He
is currently Working as Principal Government Engineering College, Gangavathi, Karnataka from July
2019. His research interests are in Image Processing and Computer Network.
Vishwanath P completed his M.E from PDA college of Engineering, Gulbarga
University, Gulbarga (Karnataka) in the year 2003 and completed his PhD in the year
2017 from NIMS University, Jaipur (Rajasthan). Presently working as Associate
Professor in the department of “Electronics and Communication Engineering” since
1998.His area of interest are Image Processing and Embedded System. He has
publishes four papers in international journals and presented one paper in international
conference.

An Optimal Route Discovery using Biogeography based Optimization for Vehicular Ad Hoc Networks

More Related Content

What's hot (20)

Similar to An Optimal Route Discovery using Biogeography based Optimization for Vehicular Ad Hoc Networks (20)

Recently uploaded (20)

An Optimal Route Discovery using Biogeography based Optimization for Vehicular Ad Hoc Networks