A Routing Technique for Enhancing the Quality of Service in Vanet

Abstract

VANET (Vehicular Ad- hoc Network) is becoming a part of a smart city in this communicating world. The major issue is frequent “Link Breakage Problem” between Source vehicle and Forwarding Vehicles (FV). If a source vehicle wants to send a set of the data packet to a destination vehicle there needs to have an FV(s) to transmit the data, given the destination vehicle is situated outside the communication range. Normally the Link Breakage problem degrades the quality of service (QoS) of Vehicular Ad hoc Network (VANET), which in turn degrades the following Parameters which determine the QoS. These are Packet Delivery Ratio, increases End to End delay, increases the Control Packet Overhead and reduces the data rate, namely bit/sec/vehicle. In our new method, we have made all the above parameters better than existing methods by a new approach, which able to make an End to End communicate for a long-time. It is done by choosing a possible FV, which will remain in the transmission zone of the source unless the complete data packet is received by the FV. This is done by defining an “Effective Region” we call it “E-region”, where FV was chosen from that region. This region is defined from a calculated parameter “E”. This parameter E is calculated from the relative velocity of the source and the chosen forwarding vehicle. We have found that our method can deliver about 14.8 kbit/sec/Vehicle data compare to other methods which were ranging from (7.87–1.21) kbit/sec/Vehicle for 300 vehicles in street.

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A Routing Technique for Enhancing the Quality of
Service in Vanet
Arindam Debnath , Habila Basumatary , Mili Dhar , Bidyut K. Bhattacharyya
& Mrinal Kanti Debbarma
To cite this article: Arindam Debnath , Habila Basumatary , Mili Dhar , Bidyut K. Bhattacharyya &
Mrinal Kanti Debbarma (2021): A Routing Technique for Enhancing the Quality of Service in Vanet,
IETE Journal of Research, DOI: 10.1080/03772063.2021.1886879
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IETE JOURNAL OF RESEARCH
https://doi.org/10.1080/03772063.2021.1886879
A Routing Technique for Enhancing the Quality of Service in Vanet
Arindam Debnath 1, Habila Basumatary 1, Mili Dhar1, Bidyut K. Bhattacharyya2and Mrinal Kanti Debbarma1
1Department of Computer Science & Engineering, National Institute of Technology, Agartala, India; 2Nano Technology Lab/PRC, Georgia
Institute of Technology, Atlanta, GA, USA
ABSTRACT
VANET (Vehicular Ad- hoc Network) is becoming a part of a smart city in this communicating world.
The major issue is frequent “Link Breakage Problem” between Source vehicle and Forwarding Vehi-
cles (FV). If a source vehicle wants to send a set of the data packet to a destination vehicle there needs
to have an FV(s) to transmit the data, given the destination vehicle is situated outside the communi-
cation range. Normally the Link Breakage problem degrades the quality of service (QoS) of Vehicular
Ad hoc Network (VANET), which in turn degrades the following Parameters which determine the QoS.
These are Packet Delivery Ratio, increases End to End delay, increases the Control Packet Overhead
and reduces the data rate, namely bit/sec/vehicle. In our new method, we have made all the above
parameters better than existing methods by a new approach, which able to make an End to End com-
municate for a long-time. It is done by choosing a possible FV, which will remain in the transmission
zone of the source unless the complete data packet is received by the FV. This is done by defin-
ing an “Effective Region” we call it “E-region”, where FV was chosen from that region. This region
is defined from a calculated parameter “E”. This parameter E is calculated from the relative veloc-
ity of the source and the chosen forwarding vehicle. We have found that our method can deliver
about 14.8 kbit/sec/Vehicle data compare to other methods which were ranging from (7.87–1.21)
kbit/sec/Vehicle for 300 vehicles in street.
KEYWORDS
IoV; Intelligent
Transportation System;
VANET; V2V; V2I; WBAN
1. INTRODUCTION
Intelligent Transportation System (ITS) [1]raisesaware-
ness of “public safety” [2], in this new era of wireless com-
munication. This technology allows each vehicle to work
as an information provider and as a receiver. This tech-
nologyiscalledVANET(VehicularAd-hocNetwork)
which is based on Vehicles and IoT active sensors. These
are installed on roads or they can be attached in vehi-
cles. This helps in boosting the communication between
thevehicles.So,thisgivesnewwaysofdatacommunica-
tion and data management [3]. In cities, VANET based
sensors and Road Side Unit (RSU) will aid in manag-
ing and preventing road disasters. During emergencies,
it will also anticipate events and may result in quick
necessary measures. Therefore, this technology will have
a variety of applications: the quick response of emer-
gency services [4], broadcasting of images, audio, video
data, web browsing, etc. On the other hand, when it
combines with Wireless Body Area Network (WBAN),
then a new application, called the Internet of Vehicle
(IoV) [5] shows up in the future horizon. The WBAN
[6],whichisattachedtothebodyofthepatient,broad-
casts a signal during an emergency. This helps to call
the emergency services on the road or in the vehicle.
There is an architecture which is the combination of
Cloud computing (CC), V2V (Vehicle-to-Vehicle) and
V2I(VehicletoInfrastructure)[7], which enables com-
munication to facilitate the trac management of a city
[8] and other emergency services. This emergency infor-
mation can be broadcasted through two categories of
VANET [9]. These categories are V2V and V2I Com-
munication. In recent years, various forms of researches
are going on in VANET, they are Vehicular Data Rout-
ing and Data Broadcasting. The article [10], surveys a
dierent kind of possible techniques for data routing
andbroadcastinginVANET.Indatarouting,VANET
always tends to minimize packet collision and congestion
[9,11,12] to improve data communication. These focus
on a comfortable and entertaining journey [13]forthe
passengers which include (a) weather report, restaurant
location or gas station location, movie ticket download
and other tourist information. (b) e-commerce informa-
tion like advertisements and sales information and (c)
other interactive services like Internet access, download
movies, playing online games, etc. While the data broad-
casting has an aim to enhance road safety by assisting
the drivers for any given hazardous events like accidents,
trac jams and other emergencies. VeMAC [14]proto-
colisdesignedforecientbroadcastservicestosupport
the high priority safety application. It shows a TDMA
© 2021 IETE

2 A. DEBNATH ET AL.: A ROUTING TECHNIQUE FOR ENHANCING THE QUALITY OF SERVICE IN VANET
basedMACprotocolusesamultichannelapproachto
reduce the congestion of broadcasting data. This ensures
thehigherthroughputofbroadcastingdataincaseofany
emergency scenario. Hence, the above-mentioned appli-
cations lead to the successful implementation of VANET,
therefore, this is a major step towards the realization of
ITS (Intelligent Transportation System). The car man-
ufacturing industries are installing inside the vehicles
with a GPS. This is capable of nding its global position,
speed and direction of vehicles on the road. This infor-
mation broadcasts a “Beacon message” along with their
Unique-Id (uid), within their transmission region. For
every car, the transmission region (R) is assumed to be
250 meters in radius.Factors like the number of vehicles
on the road, network scalability, vehicle speed, number of
intersections, number of curves on the road, inter-vehicle
distance, number of obstacles on road for a given conven-
tional highway scenario for smart cities are dierent. All
these factors have impacts on VANET routing. It must
be mentioned that VANET has problems dealing with
other factors like the high mobility of vehicles, frequent
link breakage, short link lifetime since vehicles can go out
of ranges and continuously can have changing topology.
So, special requirements for transmitting data to FV are
needed for routing signals, this is what we have worked
on in this paper.
Various routing protocols have been designed to over-
come the above-mentioned problems in VANET. For
example, Global State Routing (GSR) [15], Greedy
Perimeter Stateless Routing (GPSR) [16] and Greedy
Perimeter Coordinator Routing (GPCR) [17]selectthe
minimum distance path between source vehicle to des-
tination vehicle. But all these protocols suer from the
frequent link failure problem and these results in a
short time End to End communication. This failure has
occurred very frequently between a source and a data
forwarding vehicle, due to the problem of the selec-
tion of neighbor forwarding vehicle. On the other hand,
Greedy Trac-Aware Routing (GyTAR) [18], Anchor-
based Street and Trac-Aware Routing (A-STAR) [19],
Stable CDS-Based Routing Protocol (SCRP) [20]always
forward the data packets through the well-connected
road. A backbone/guard node is used in the road junc-
tion, which is responsible for directing the data packet
to the actual destination. In the case of multiple pairs
of source and destination, the intermediate vehicles
are sending their data using this backbone node on
theroadjunction.Thisresultsindatacongestionand
queuing delay. As a result, huge numbers of packets
are dropped in between the communication. Recently,
Named Data Networking (NDN) technology [21]is
used for addressing the above-mentioned problem in
vehicular environments. In NDN, the content store mod-
ulecacheswhicharesentorreceivedcontentswhich
can improve the network performance by eliminating the
redundancy of IP based network. But our infrastructure-
less proposed routing method is mainly focusing on min-
imizing the link breakage problem between the source
and intermediate vehicles and maintaining the long-time
End to End communication during the data communica-
tion in TCP/IP-based vehicular network.
This will result in enhancing the Quality of Service (QoS).
The QoS is dened by high Packet delivery ratio (PDR),
minimum End to End delay (E2E delay) and minimum
Control Packet Overhead. To achieve this goal, this pro-
posed protocol has considered the number of vehicles on
the road, their position, speed and transmission region.
There are three folds of direct contributions that we made
in this paper, these are described below.
(1) A method is developed, where the forwarding vehi-
cle (say ith vehicles) has the lowest value of (Pi+Ci).
Where, Piand Ciare dened in (4) and (5) in
the paper, and these parameters are described in
Figure 1.
(2) An Eective Region (E-Region) was dened, which
depends on Critical Distance (E). This Critical Dis-
tance “E” is dened in term of the relative velocity
ofthesourceandotherneighboringcars,whichwill
allowthesourcetohavethelongesttimeforEndto
End Communications between ith neighboring vehi-
cle and the source vehicles. The Critical Distance E
is dened in (6) and in Figure 2.
(3) The ith selected Vehicle is sitting in a white color E-
Region showed in Figure 2.Pickingtheith selected
Vehicle from the E-Region causes the moving car to
stay longer in the transmission region of the source
vehicle. This is why we have observed the minimum
link failure between the forwarding and the source
vehicle, compare to other methods.
TheOutcomeofourproposedprotocol,asdescribed
above, is given below.
(1) The QoS (Quality of service) is also enhanced due to
theincreaseinPacketDeliveryRatio(PDR),reduced
End to End Delay, and fewer numbers of Control
Packet Overhead. What that means, one needs to
sendlessnumberofrequestorcontroldatapacket
which is essential for sending the actual data, hence
to determine the path required for routing the actual
data.
(2) The transmitted data rate was maximized is our
proposed method. It was dened in terms of

A. DEBNATH ET AL.: A ROUTING TECHNIQUE FOR ENHANCING THE QUALITY OF SERVICE IN VANET 3
bit/sec/vehicle. What that means is the following: –
If the number of vehicles on the road is higher, then
one can transmit a larger number of data per second.
The paper is divided into the following sections.
Section 2, refers to “RELATED WORK”. In section 2
we will describe the existing methods on data routing in
VANET. We add als o a Tab l e 1, called “SUMMARY OF
NOTATION”. This Table 1has all the symbols. Those
symbols are used throughout this paper. After that, in
Section 3 (“PROPOSED MODEL”), describes the func-
tionality of this proposed protocol and dierent compo-
nents. In Section 5, refers to “PERFORMANCE EVOLU-
TION”. This section describes all the simulation results
and shows a comparison of the performance of our pro-
posed model with other existing methods. Finally, in
Section 6 we will have a conclusion.
2. RELATED WORK
VANET, there are two routing strategies are adopted,
whicharePositionandTopologybased.Intopology-
based routing [22], each vehicle is sharing the routing
table with other vehicles. This increases the packet over-
head as well as E2E delay. Therefore, it performs well for
a small and static network like WSN (Wireless Sensor
Network). However, the Position-based routing protocol
[23] is appropriate for a dynamic and mobile network like
VANET. In the Position-based routing protocol, there is
no need for sharing the routing table with other vehi-
cles. The major issues of this protocol are to maintain
the link reliability, network scalability, an appropriate
selectionofanFVtoforwardthedataandstillhaving
the maximum speed for the data routing. At the same
time, one needs to improve the performance of the data
routing by increasing the Packet. Delivery Ratio (PDR),
decreasing the E2E Delay and Control Packet Overhead.
However, it always suers from load balancing prob-
lems and the broadcasting of extra control packets in a
given network. Here, the position-based routing protocol
is further divided into three main categories: Vehicular
routing without junction node, Vehicular routing with
the junction node and Vehicular routing using RSU and
SDN-based network. One of the popular position-based
benchmark protocols, GPSR (Greedy Perimeter Stateless
Routing) always selects a neighbor node to forward the
data, which is very near to the destination node. But, that
should be within the range of the source node. How-
ever, there is a high chance of link failure during the
data communication, as GPSR routing protocol always
selectsavehiclethatisveryneartotransmissionrange,
with E∼0,seeFigure2. GPSR is suitable for that sce-
nario where there are no obstacles in the road. A newly
developed routing protocol MM-GPSR [24]eliminates
the problem of GPSR by selecting a neighboring vehicle
that can maintain long-time data communication with
the source node. This depends on the minimum angle
oftheneighboringvehiclewiththesourcenode.Atthe
same time, it eliminates the problem of path redundancy
in perimeter forwarding in GPSR protocol. But the prob-
lem is if the selected vehicle (which has the minimum
angle) is very near to the boundary of the transmission
region of the source, it may be the case of frequent link
failure. So it can degrade the performance of the QoS.
In [25], the DGF-ETX measures the communication link
quality between source and receiver, which is an impor-
tant parameter in vanet routing. This quality factor is
measured based on the number of sending hello packet
and receiving hello packet measures the QoS. But, they
didn’t care about the link stability and long communica-
tion time with the receiver. In [26], a reliable and stable
data forwarding scheme is shown to quantitatively mea-
sure the link stability for each neighbor’s vehicle. The
source vehicle always select a link that has the highest
connectivity time with the source vehicle. Next, it uses the
greedy algorithm to forward the data to the next vehicle.
Ifthenextforwardingvehicleisveryclosetothesource,it
canincreasethedelaytosendthedatatothedestination.
The routing method [27] uses a multi valued DPSO (Dis-
crete particle swarm optimization) approach to select an
optimal path for ecient data diusion in VANETs. This
procedure determines quantitatively the link reliability of
the path and nds the probability of the occurrence of
obstacles on the path. Based on these two metrics DPSO
nds out the optimum path for data communication. In
[28],theauthorhasaddressedsomechallengesandissues
about the junction based routing protocols. Normally the
junction based routing protocols has advantahes, since
that helps to move the data, if there are obstructus while
sending data in a starigh path. But the junction node can
be static or dynamic and helps to select the right path
to forward a data packet to the actual destination. One
of the junction based routing protocols GPCR (Greedy
Perimeter Coordinator Routing) also follows the GPSR
protocol. But they use the backbone node on the road
junction, which makes a decision where to send the
data packet. While the data packet can’t be routed in a
road junction, a repair strategy has been taken using the
right-hand rule. But this method is showing the improve-
ment while one compares with GPSR, in terms of Packet
Delivery Ratio (PDR). In improved Greedy Trac-Aware
Routing (GyTAR), the static backbone node in the road
junction assigns a weight to connected road segments,
for the selection of the shortest path, to direct the data
packet. The shortest path will get always the highest
weight. However, this method always suers from the

4 A. DEBNATH ET AL.: A ROUTING TECHNIQUE FOR ENHANCING THE QUALITY OF SERVICE IN VANET
load balancing problem. This increases the E2E delay
during communication. iCAR (Intersection-based Con-
nectivity Aware Routing in Vehicular Ad hoc Networks)
[29] protocol gathers real-time information about the
number of vehicles on the road segments, road intersec-
tion and packet delivery delay among the vehicles. But
it always prefers to avoid the high number of vehicles
on the road to minimize the delivery delay. However,
this method improves in terms of minimizing the aver-
age delay of the packet, due to the consideration of the
lifetime of communication links. A-STAR (Anchor-based
Street and Trac-Aware Routing) uses the city bus route
to forward the data packet. It always looks for getting the
higher connectivity of vehicles. But this approach can’t
improve the delivery delays, it is always looking for good
connectivity of vehicles and this path may be the longest
path to send the data to the actual destination. CAR [30]
(Connectivity Aware Routing) relies on the carry and for-
ward algorithm in V2I communication. It combines the
method of geographic routing and Ad-hoc routing. In
thisapproach,itusesanRSU(Road-SideUnit)asaguard
node at each road junction, which is a static node. This
helps to direct the data packet at the road junction. Due
tothereasonofthesameguardnodeselectionfordata
communication, there are possibilities of data congestion
anditwillresultinpacketdeliverydelay.BAHG[31]
(Back-Bone-Assisted Hop Greedy Routing) is a dynamic
and reactive routing protocol. It always tries to select that
shortest path that has the minimum number of vehicles.
But in that approach, it doesn’t consider the strong com-
munication connectivity of neighboring vehicles with a
source node within its transmission region. As a result,
frequent link failures will occur. In JBR [32](Junction
Based Routing Protocol), the source vehicle measures an
angle for each neighboring vehicle within its transmis-
sion region. The approach is, a neighboring vehicle which
has the lowest angle in the list of angles of neighboring
vehicles will be selected for forwarding the data packet.
This improves the link stability between two vehicles. The
problem of the right-hand rule used in GPCR and GPSR
is eliminated in this method. Another benchmark rout-
ing protocol Stable CDS-Based Routing Protocol (SCRP)
also follows the way of GyTAR. But, this protocol does
not suer from load balancing problem, as it uses a back-
bone node in road junction which is dynamic. It assigns
aweightontheroadsegmentandthehighestassigned
weight path is always selected to route the packet. While
multiple numbers of source and destination pairs are
sending the data, then data congestion has occurred on
thebackbonenode.Thisisareasonforalargenumber
of packet drops. In the paper [33], routers were commu-
nicating between the vehicle and the clouds, which does
all the master computations, store the data and reroute
accordingly. Under this provision, one needs equipment
that has a larger power source to transmit the data into
the clouds. And then the cloud will secure the informa-
tion and route the data appropriately to the destination.
Inourcase,allthevehiclesinthenetworkwillknowthe
information, which is dierent than routing data through
thecloud.Butonecanstillworktosecuretheactualdata,
to insert one extra security level while data are still routed
through other vehicles. In such a case, only the receiver
can unlock the data. All these may reduce the package
deliver ratio or quality of service for that extra security
level. SVPS [34]isaninfrastructurebasedprotocolto
nd out the parking slot on the roadside. This method
usestheParkingSideUnits(PSUs)aswellasRoad-
side unit to help the cloud-based communication. From
the cloud system, vehicles gather information like vacant
parkingslots,parkingfees,andthetracconditions,sur-
rounding the parking slots. So, this method depends on
the roadside infrastructure. Recently, the SDN (Software-
dened Network) based routing method [35]hasgot
great attention to design an infrastructure-based routing
protocol in VANET. It helps to switch the data communi-
cation between wired and wireless networks and it is very
helpful for a very large network. Normally these are done
using links which are some kind of connectivity between
the SDN switches which are much less in number that
point to point. One of the SDN based routing method
[36], minimizes the link failure by using the roadside
units (RSU), which are connected with the SDN-based
network. This road side unit acts as a controller together
with some selected special switches, which actually mini-
mized the latency. In [37], the author shows a 5G technol-
ogy enabled VANET (hierarchical architecture) that inte-
grates SDN’s centralized feature. For eective resource
allocation, this architecture incorporates the 5G enabled
C-RAN (Cloud Radio Access Network). Moreover, the
method has used the clustering techniques to minimize
the number of hand-overs between RSUs and vehicles at
the network-edge.
But our proposed protocol is independent of RSU for
any V2 V communication system. Most of the aforemen-
tioned protocols don’t take care of the frequent “Link
Breakage Problem” between the vehicles and long-time
End to End communication between the vehicles. This
is a reason for receiving a lower rate of data packets for
other protocols. Moreover, information like “shortest dis-
tance” from source to destination and their position is
not sucient in a dynamic environment like VANET. To
some extent, we can overcome this problem by know-
ingtherealtopologyontheroad.Apartfromthis,it
is important to guarantee a stable and reliable link for
designing a routing protocol. In Table 1, we show the

A. DEBNATH ET AL.: A ROUTING TECHNIQUE FOR ENHANCING THE QUALITY OF SERVICE IN VANET 5
Table 1: Comparison Table for Referred Protocols
Protocol with Reference Number Advantage Disadvantage
[16] Without Junction Node Minimizes the looping problem and always
select a forwarding node which is very
near to the destination.
This leads to frequent link failure due to the high mobility of the
vehicles.
[25] Without Junction Node Determine quantitatively the commu-
nication link quality between source
and receiver before the start of the
data communications which leads to
minimizes the link failure.
Don’t care about the link stability for a long period of time.
[26] Without Junction Node Always select a node, which has the highest
weight for link stability for longer data
communication time.
If the next forwarding node is very near to the source it leads to
the data communication delay.
[27] Without Junction Node It measures the link reliability of the
path and finds the probability of the
occurrence of obstacles on the path.
This method is suitable for a small network.
[30] WithJunction Node Uses a static backbone node at the
junction to direct the data to the actual
destination. This improves the End to
End delay over GPSR protocol.
At the junction, data packet congestion has occurred due to the
use of a static backbone node. This reduces the QoS.
[31] With Junction Node 1. Routing path requires minimum num-
ber of hops.
2. Reduced End to End Delay.
Frequent Link Failure problem occurs, which leads to smallest PDR.
[20] With Junction Node 1. Forwarding vehicles are selected very
efficiently to maintain link stability.
2. Any vehicle which is in the junction
region (Defined to be less than 40m)
and has the shortest distance from the
destination is used as a junction node.
Due to the use of a dynamic junction node at each junction, it
broadcasts a message about the junction. Which increases
the control packet overhead and hence the link failure at the
junction.
[36] Uses RSU and SDN based network Uses the SDN technology to minimizes
the link failure and maintain the high
scalability of the network.
Due to the centralized mechanism, it increases the end to end
delay.
[37] 5G enabled SDNbased network To discover the route it uses vehicle’s
information, V2V data offloading.
Identification problems of vehicles and issues of inaccurate vehicle
information.
[38] 5G enabled SDN based network Uses the 5-G enabled SDN network to
minimize the latency and management
of cooperative message distribution.
There are integration issues due to the use of differenttechnologies,
evaluation problem in a high dynamic environment.
advantages and disadvantages of some selected important
protocols.
By addressing this aforementioned problem, we propose
a new routing protocol that minimizes the link failure and
ensures enough time for two communicating vehicles.
ThisresultsinenhancingtheQoS(QualityofService)
intermsofPDR,E2EDelayandAverageControlPacket
Overhead. We present a detailed description of our “Pro-
posed Model” and also present the result and analysis
which we have compared with other existing protocols
inthenextfewsections.InTable2,wehaveshownthe
symbolsthatwehaveusedthroughoutthewholepaper
and their corresponding meanings.
3. PROPOSED MODEL
3.1 Acquiring the Position of Destination and
Intermediate Vehicle
Our proposed method is a reactive and multi-hop rout-
ing method. At rst, a source vehicle broadcasts a route
request(RREQ)packettoknowthepositionoftheDes-
tination vehicle. If any intermediate vehicle knows about
Table 2: Summary of notation
Symbol Description
QoS Quality of Service
E-Region Effective Region
ECritical Distance
DDistance between Source and Destination.
Pi Perpendicular Distance of ith neighbor vehicle, joining the
line between source and destination
Ci Minimum value of Circumference Distance of ith neighbor
vehicle
Zi Distance between ith neighbor vehicle and Destination.
NTotal number of neighbor Vehicles.
Vi Velocity of ith neighbor vehicle
Vs Velocity of Source vehicle
RTransmission Region
E2E delay End to End Delay
FV Forwarding Vehicle
RREQ Packet Route Request Packet
RREP Packet Route Reply Packet
theDestination,itrepliestotheSourcevehicle.Other-
wise, it broadcasts the RREQ packet until the Destination
vehicle receives that packet. After receiving the RREQ
packet, Destination replies an RREP (Route Reply) packet
toSourceinaunicastwaywithitsposition.Inthisway,
the source has been updated with the new position of the
destination vehicle in a xed interval. On the other hand,
all vehicles in the network broadcast a Beacon message
(Hello Packet) within their transmission region to each

6 A. DEBNATH ET AL.: A ROUTING TECHNIQUE FOR ENHANCING THE QUALITY OF SERVICE IN VANET
other in a xed interval of time (1 sec). This message
always helps to update a vehicle about its surrounding
neighbor vehicle.
3.2 Construction of the Communication Path
AfterreceivingtheRREPpacketfromthedestination
vehicle, the source vehicle starts building the routing
path, knowing the id and the corresponding location of
the surrounding neighboring vehicles, which are mov-
ing around in its operating transmission range “R”, (see
Figure 1). At rst, the source selects appropriate neigh-
boring vehicles within the transmission region. Figure 1,
shows a green region, from which, source vehicle selects
the neighboring vehicle which will be a source point for
thenextround.Thisselectionobeys(1).Thephilosophy
of this selection is that the source always tries to select
those vehicles (Xi,Yi) which are in between source and
destination, having distance Zifrom the destination point
(see Figure 1). This Ziis less than the distance Dbetween
source and destination. This is also shown in Figure 1.
The other relevant equations for determining the next
forwarding vehicle are shown below in (2) and (3).
D>Zi(1)
D=XD(2)
Zi=(Xi−XD)2+(Yi)2(3)
Where i =1toN.Nisthetotalnumberofreddots
(neighbor Vehicles in the transmission range of Source),
see Figure 1.
In the transmission range, red dots are considered for
the selection of Forwarding Vehicles (FV). Black dots are
not considered since they violate equation (1). If Piis the
perpendicular distance from the point (Xi,Yi)totheline
joining between source and destination, then
Pi=Yi(4)
Figure 1: Selection of Appropriate Vehicle is done from green
area
If any vehicle having Yi=0or in other words if that
vehicle lies on the line joining the source and destination
point, that will generate the shortest distance and hence
willproducethebestdatarate.Nevertheless,ifthatpoint
is close to the periphery or close to the boundary of the
transmission region, then, during the time of data trans-
fer, the moving vehicle may go outside the range of the
source point that will cause link failure. In such a con-
dition, the source point again has to look for another
vehicle. We consider another parameter Ci,toavoidlink
failure and to get the best data rate. This parameter is
the distance of the Vehicle from the circumference of the
transmission region of the source. This Circumference
Distance, Ci,isshowninFigure1. In reality, the direction
of the moving vehicle determines the actual real distance,
when the same vehicle will cross the boundary. With-
outlossofgenerality,wecanassumetheminimumvalue
of the possible Cifor a given (Xi,Y
i). Mathematically
this minimum value can be expressed by Equation (5),
as shown below.
Ci=(R−(Xi)2+(Yi)2)(5)
This Circumference Distance (Ci)ofeachselectedneigh-
boring vehicle plays a vital role to maintain the link relia-
bility and longtime End to End communication between
Source and FV, as it minimizes the link failure. We have
observed in our study, the value of Ciis one of the crit-
ical parameters, which needs to be taken care of during
the selection process to determine which vehicle will
route the data next and become the new source vehi-
cle. There is another important parameter, called Critical
Distance (E), which species the minimum acceptable
value of Cione requires, so that in one second the dis-
tance between the source and the chosen Forwarding
Vehicle (FV) does not leave the transmission boundary,
given both Source and all other vehicles are moving [See
Figure 2]. We have chosen “Beacon Interval” as one sec-
ond which is shown in Table 3while calculating Efrom
(6). If the velocity of the Source is vsand the velocity
of the other ith vehicle is vi, then the maximum relative
velocity between these two vehicles will be (vi+vs)and
therefore the maximum value of Ewe can calculate using
the following equation as shown below.
E≤(vi+vs)t.(6)
Where, tis one second, which is one of the Beacon
intervals we have taken, see Table 3. If the relative velocity
is zero, then we can assume E=0. Equation (6) yields the
maximum possible value of E, we need for minimum link
failures.Inreallife,theremaybeadistributionofE, but
we did not consider that in present scenarios. In Table 3,
as describe before, we show all the simulation parameters

A. DEBNATH ET AL.: A ROUTING TECHNIQUE FOR ENHANCING THE QUALITY OF SERVICE IN VANET 7
to determine the performance of the new methodolo-
gies.Finally,thesourcedeterminestowhichvehicle,the
source to send the data by knowing the values of Ciand
Pifor all the vehicles in his transmission range using the
following criteria, as shown below.
Ti={Min(Pi+Ci)}for Ci>E(7)
ThesourcelooksfortheminimumvalueofthesumofPi
and Ci(call it Ti) with a constraint that satises Ci >E.
This constraint guarantees the minimum link failure. If
none of the vehicles is found in the Eective Region (E-
Region) as shown in Figure 2, then the Source looks for
the Vehicle beyond the Eective Region but within the
Transmission Region of Source. Once the Source deter-
mines where to send the actual message, then that chosen
vehiclebecomesthenextSourceandtheprocesscontin-
ues until the data packet arrives at the destination vehicle.
In Figure 3,weshowaowchart,howourprotocolworks
for the generation of the next forwarding vehicle (FV),
Figure 2: White portion is called Effective Region or E-Region
whichwillthenactasasourcevehicle.Thisprocess
continues until the data reaches the nal destination.
4. TIME COMPLEXITY OF THE PROPOSED
ALGORITHM
It covers the computational complexity of each step of the
proposed algorithm. According to the algorithm, initially,
the source minimizes the number of the neighbor vehi-
cle (N). Using the equations (1) to (3) we will determine
the worst-case run time O(N).Inthenextstepsource
measure sum of P and C (T =P+C) for each neighbor
vehicle to understand the value of O(N). Going forward
the source considers that vehicle which has the lowest T
value and then runs the system for O(logN)times.The
critical step is to understand if the covering time for the
critical distance (E=(vi+vsource)∗t)islessthan1sec.
Under that condition that vehicle will not be selected for
the next forwarding vehicle. And this process will con-
tinueuntilthesourcegetsaneighborvehiclewhichhas
the covering time greater than or equal to 1sec. So for
this process, it takes O(N) times. Accordingly, the total
time complexity of this proposed method is described as
below:
Time complexity ∼3O(N)+O(logN).(8)
5. PERFORMANCE EVALUATION
Based on the above-described method, as shown in
Figure 4, we show the schematic diagram of scenarios
where there are several number of vehicles in a street.
The Source vehicle is at node (n0)and(n1)isthecorre-
sponding node of the Destination vehicle or the vehicle
Figure 3: Flowchart of Proposed Method

8 A. DEBNATH ET AL.: A ROUTING TECHNIQUE FOR ENHANCING THE QUALITY OF SERVICE IN VANET
of the nal destination. In Table 4,wecanobservethat
n2has the lowest Tivalue. So this node will be selected
as an FV. The source ensures that the selected vehicle
is within the Eective Region (E-Region). While select-
ing this vehicle at n2, we assumed that this vehicle is
inside the eective transmission range, which is shown
in Figure 2. The critical distance “E”inthiscaseisonly
18m. Thus, 250m original transmission range will be
reduced to 232m. The reason for this 18m deduction is
caused by the cars that are moving at a speed of 9m/sec
[See Table 3] and therefore the maximum relative veloc-
ity between the source and the intermediate receiving
vehiclewillbeabout18m/sec.Atthesametime,the
Beacon Time Interval is assumed to be about 1 sec (see
Table 3). Thus, the complete information to travel from
the source to the intermediate selected vehicle will be
about 1sec. This selected node n2in Table 4also obeys
all the three equations as described in (1), (2) and (3).
That is the green zone as shown in Figure 1.Duetothe
above criteria, the chances for link failure will be reduced
while selecting the vehicle at node n2.If one requires
more time margin, then one can choose the next vehi-
cle, which will be n3in Table 4.However,thatwillreduce
thedatarateandmayrequireoneortwomoreinter-
mediate receivers, before the data can arrive at the nal
Figure 4: A scenario for Selecting an appropriate Forwarding
Vehicle
Table 3: Simulation Parameter
Symbol Values
Simulation Area 3500meterX3500meter
Number of nodes 100,200,250,300
Vehicle Speed 9–11m/sec
Transmission Range 250 m/300m
Channel Data Rate 2Mb/s
Beacon Interval 1 sec
Propagation Model TwoRayGround
Packet Type UDP
Type of Communication CBR (Constant Bit Rate)
Mobility Model Free Walk
Carrier Frequency 5.9GHz
MAC Protocol IEEE 802.11p for DSRC(Dedicated Short
Range Communication)
(Interface Queue) IFQ Size 50 packets
Table 4: Simulation Result
Node_id Position PerpenDist Circum_Dist Ti={Min(Pi+Ci)}
n2 (529,327) 27.45m 19.41m 46.87m
n3 (473,283) 16.65m 76.16m 92.82m
n4 (452,329) 29.30m 95.25m 124.56m
n5 (437,244) 55.72m 10199m 157.71m
n6 (403,345) 45.20m 137.59m 182.79m
n7 (376,278) 21.84m 170.87m 192.72m
destination. In our example, we have considered the city
scenario, where the network range is 3500m X 3500m,
having bidirectional roads where the maximum speed of
the cars is about 25km/hour. All the vehicle information
isextractedwiththehelpoftheOPENSTREETMAP
database and for simulation using Network Simulator
(NS2.35). For vehicle movement, we have used open-
source mobility software SUMO-0.31. The total number
of vehicles in the street is generated by SUMO. These
numbers were varied, in our model, between 100 and 300.
Source vehicle sends data through intermediate vehicles
(we call it Forwarding Vehicle) till it reaches to Desti-
nation Vehicle. For performance evaluation, we compare
our proposed protocol with the ve most popular bench-
mark protocols in terms of PDR, E2E Delay, Control
Packet Overhead and also bit/sec/vehicle. These proto-
cols are iCAR, MM-GPSR, GPCR and PRHMM [38]. The
required parameters which were used for the simulation
purpose are shown in Table 3.
5.1 Performance Evaluation by Comparing with
MM-GPSR, iCAR, GPCR and PRHMM
5.1.1 Packet Delivery Ratio (PDR)
The link reliability comes, when the source can send
all the data to the FV before it passes through the
transmission region. When the FV leaves the transmis-
sion region before receiving all the data from the source,
link failure occurs and then packet Delivery Ratio (PDR)
will reduce. Since the data, that source needs to send to
the destination will not be complete, and hence source
needstondotherFVdierentthantheonethesource
was communicating before. In Figure 5we show the
plot,showingthePacketDeliveryRatioishighestinour
case compare to other various methods because we have
dened an E-Region from where we always selected the
FV. It is important to note that as the number of vehi-
cles in the transmission region increases then PDR will
increase since there are large numbers of possibilities to
route the data using various other vehicles. This is also
shown in Figure 5.Asanexample,ifthereisonly1vehi-
cle then PDR will be very low, since, once that only vehicle
passes the transmission boundary then no data can send
any more by the source to the destination. To explain a
bit better why our PDR is largest and close to 95% for 300

A. DEBNATH ET AL.: A ROUTING TECHNIQUE FOR ENHANCING THE QUALITY OF SERVICE IN VANET 9
Figure 5: Packet Delivery Ratio vs Number of Vehicles
Figure 6: Packet Delivery Ratio vs Lower Number of Vehicles
vehicles compare to other methods, we need to explain,
once again, the eect of E-region and the parameter E
as dened in Figure 2.ThisEdependsontherelative
velocity of the source vehicle and the intermediate data
packet receiving vehicles as shown in (6). If both of these
vehiclesarelocatedinthetransmissionrange“(R-E)”, as
shown in Figure 2, then it can maintain the data com-
munication for a long period, therefore a large number
of packets can be delivered. In Figure 5,iCARgives
thesecond-highestPDR.IniCAR,adynamic“Junction
Vehicle” is selected at each junction in some xed time
interval that could be due to their method which han-
dles multiple sources and destination pairs In Figure 5
MM-GPSR achieves better PDR comparing with GPCR
sinceitimprovesindataforwardingmethodoverthe
GPCR’s next vehicle selection method and reduces the
redundancy in path selection up to destination vehicle.
In the GPCR method, one always selects a vehicle, which
is very near to the Destination. Therefore, the intermedi-
ate routing vehicles may reside close to the boundary of
the transmission region. This also causes a large number
of packet drops due to the frequent link failure between
source and forwarding vehicle. It is interesting to notice
that the slope of each method is almost the same, which is
about 0.1 PDR/Vehicle, as shown in Figure 5.Thatmeans
the PDR increases linearly for all methods as a function
of the number of vehicles. Thus, the best gure of merit
of the various methods can be described in terms of the
largestinterceptinthePDRaxis.Thatcorrespondsto
the value of PDR when the number of vehicles goes to
zero.Webelievethatthelargestinterceptinourcasecame
from the minimization of TiFrom (7), the result of which
is described in Table 4. Thus, in the future, the challenge
will be to enhance these parameters, which increases the
value of the intercept of the above curves with the PDR
axis as shown in Figure 5.InFigure6, there is another
case of Packet Delivery Ratio (PDR) vs lower Number
of Vehicles. Here we show the performance of PRHMM
protocol,therangesofthenumberofvehiclesforthis
protocol are from 40 to 70. In our proposed method,
we considered the number of vehicles is in the range of
100–300. Therefore, we t the data to extrapolate the val-
uesofPDR,whenthenumbersofvehiclesareinthe
range of 40–70 for comparison purposes with PRHMM.
In Figure 6,wecanseethecomparisonbetweenourpro-
posed method, the PRHMM method and other protocols
for the low number of vehicles in the street. The num-
berofthevehiclefrom50to70,PRHMMwasbetterand
inothercasesthenumberofthevehiclefrom40to48,
our proposed method is better. Another problem with the
data as plotted in Figure 6(red squares) for the PRHMM
method has no patterns. Whereas the tted data which
isthebluelineforourproposedmethodasshownin
Figure 6,isassumedtobelinearfromtheextrapolated
data taken from Figure 5.
5.1.2 Average Control Packet Overhead
Position-based routing (PBR) protocol uses the beacon
messages to get the position of a given neighboring vehi-
cle in a regular interval. However, under the consid-
eration of the beacon message, RREQ and RREP mes-
sages, we calculate the average control packet overhead.
Figure 7shows the number of control data packets, which
have been generated during the communication over the
number of vehicles to get the updated position of the des-
tination vehicle. This is called overhead. In both cases,
our proposed method shows a better result than the other
threemethods.Thisisaneectoftheintroductionofthe
parameter “E” and E-Region in our model as described
in Figure 2. As the forwarding vehicle is selecting from
this E-Region, which is far from the transmission bound-
ary that reduces the failure of the communication link.
Thus the need for Route Request Packets and Route
Reply Packets also reduce in number during the data
communication. iCAR, have two more additional packet
overheads. One is to collect the vehicle density informa-
tionforeachroadsegmentataxedinterval;anotheris
broadcasting the information about the junction node.
These two extra control data packets in the network
increase the Packet Overhead; this is shown in Figure 7.
MM-GPSR and GPCR suer from frequent link failure,
therefore, a huge number of extra control packet has been

10 A. DEBNATH ET AL.: A ROUTING TECHNIQUE FOR ENHANCING THE QUALITY OF SERVICE IN VANET
Figure 7: Average Control Packet Overhead vs Number of
Vehicles
Table 5: Data Transferred in kbit/Sec/Vehicle
for 300 vehicles in Network
Proposed Method 14.7
iCAR 7.87
MM-GPSR 2.26
GPCR 1.21
required to maintain the data communication. Therefore,
both the protocols show the higher Control Packet Over-
head compared to our proposed method. Finally, it is
important to say that the overhead is the lowest which
can be seen from Figure 7for 300 numbers of vehicles in
the network. GPCR has the highest overhead close to 67%
of all transmitted data and control packet when there is a
totalof300numbersofvehiclesinthenetworkhavinga
total area (3500mX3500 m).
5.1.3 Average End to End (E2E) Delay
In Figure 8, the average E2E Delay has been shown for the
number of vehicles. One can observe that for all the pro-
tocols, the average E2E delay reduces when the number
of vehicles increases. Once again E2E delay for our case is
the lowest. It is about 1.411 sec for our proposed method
for 300 vehicles and the corresponding value for GPCR is
about 4.511sec for 300 cars. Thus, the proposed method is
approximately 4 times faster compared to GPCR, for the
number of vehicles 300 in the network. When the num-
bers of vehicles are 300, the newly proposed method is
about approximately 3.20 times faster compared to the
GPCR method. In Table 4below, we show that one trans-
mitsthelargestbitpersecpervehiclefortheproposed
method. It is important to see from the Table 5,thatthe
transmitted data in our method is twice faster compare to
iCAR and about 12 times faster compare to GPCR. The
reason for this superior performance is explained below.
The model that we have developed, the source always
tries to connect with those vehicles, which fall under its
E-Region and that is the safest region to transmit data,
sincethathaslessprobabilityforlinkfailure.SeeFigure2,
Figure 8: Average E2E Delay vs Number of Vehicles
for E-Region and the denition of E.TheEvalue is cal-
culated from (6) based on 32 km/hour maximum speed
of any or all vehicles. The distribution of speeds is not
considered. On the other hand, iCAR is broadcasting the
control packet very frequently to get the information on
vehicle position, speed and also their direction as well
as the Junction information. This information helps to
assign the weight to each connected road of a junction.
Normally this makes congestion over actual data deliv-
ery and unnecessary consumption of bandwidth. It is the
very cause, for the large E2E delay for a given number of
vehiclescomparedtoourproposedmethod.Butitshows
abetterresultthanandMM-GPSR,foritsprediction
method to select the forwarding vehicle. Finally, due to
the frequent link failure in GPCR, most of the packets
are dropped before receiving the destination, since this
method looks for the FV.
The above section shows the performance of the pro-
posedmethodasafunctionofthenumberofvehiclesin
thegivennetwork.OnecanobservethateachFigures5,
7and 8represents the worst performance of our pro-
posed method for each group of vehicles. This is because
of equation (6), which yields the maximum possible value
of E. In all cases, the critical distance Ehas the maximum
distance which depends on the relative velocity of the
source and neighbor vehicle. From the above discussion,
we can conclude that if the relative velocity between two
vehicles decreases then the PDR, Average E2E delay and
Control packet overhead value will befall into the shaded
region of Figures 5,7and 8respectively and the perfor-
mance will be increased. This is the relative velocity eect
of VANET.
5.2 Performance Evaluation by Comparing with
Dcmr, Asgr
In the next following section, the series of simulation
results has been shown here, while comparing with
another two protocols DCMR [39]andASGR[40]in
termsofPDRandAverageE2Edelayasafunctionof

A. DEBNATH ET AL.: A ROUTING TECHNIQUE FOR ENHANCING THE QUALITY OF SERVICE IN VANET 11
Figure 9: (a) Packet Delivery Ratio vs Number of Vehicles, (b) Average E2E vs Number of Vehicles (c) Control Packet Overhead (%) vs
Number of Vehicles, (d) Packet Delivery Ratio vs Packet Generation/ Second. (e) Average E2E Delay vs Packet Generation/ Second (f)
Control Packet Overhead (%) vs Packet Generation/ Second
Packet generation rate in second and the Number of car
onthegivennetwork.Forthesimulation,wehavecon-
sidered the size of the network is (2500m ×2500 m), the
size of the data packet is 512byte, the number of vehi-
clesisrangingfrom30to330inthegivennetworkand
other parameters are the same as in Table 3. In all cases,
our proposed method has shown better results compared
to the other two protocols. According to Figure 9(a), the
proposed method initially starts with low PDR. How-
ever, as the number of vehicles is increased the PDR
also increases. On the other hand, in Figure 9(b) we can
observe that the average E2E delay is approximately the
same for all protocols in the small network (2500 ×2500)
with the highest number of vehicles. However, for the
lowernumberofvehiclesthedelayishighcompared
to the other two protocols, due to the selection of a
data forwarding vehicle within the Eective region as
shown in Figure 2of our proposed method. According
to Figure 9(c), as the number of vehicles is increasing
the control packet overhead also increases, which is due
to the use of RREQ and RREP packet on our proposed
method. Figure 9(d), (e) and (f) show the QoS in terms
of PDR, Average E2E delay and Control Packet Overhead
for packet generation in second. As the packet genera-
tion rate is increased, the performance will be decreased
in all cases, as it increases the more congestion and delay
in the network. However, our proposed method shows
better results compared to the other two methods while
the packet generation rate is increased. In DCMR, it uses
the dynamic clustering concept based on the connectivity
of vehicles. This clustering concept increases the average
E2Edelayduetothebroadcastoftheclusterheadandthe
gateway node in the given network. It causes unnecessary
extra control packets each time the number of clusters has
builtupinthenetworkoronaroad.ButinASGRthey
consider the link quality and the transmission delay for
any selected neighbor vehicle. If that selected vehicle is
very near to the perimeter of the transmission region of
the source, may break the communication link with the
source in the next beacon time (1sec). Therefore, our pro-
posed method uses the critical distance Eto improve the
connectivity and other QoS, which is better than ASGR
and DCMR.
6. CONCLUSION
This proposed novel method enhances the Quality of
Service (QoS) during the V2V communication, which
improves the Packet Delivery Ratio (PDR), reduces
the End to End (E2E) delay as well as reduces the
number of Control Packet Overhead compare to the
other existing methods. This proposed method also
demonstrates the highest number of data transmission,
compared to any other available methods. The observed
kbit/sec/vehicle for our case was found to be about
14.7kbit/sec/vehicle and for other cases, these ranges
from (7.8–1.2) kbit/sec/vehicle. We have found the said
data when there are about 300 vehicles in the net-
work. The corresponding network size was close to
(3500mX3500m). This was achieved by minimizing the
link failure, which increases the communication time
between vehicles during the data routing. This com-
munication starts with selecting an appropriate FV by

12 A. DEBNATH ET AL.: A ROUTING TECHNIQUE FOR ENHANCING THE QUALITY OF SERVICE IN VANET
considering the minimum Tivalue, among all neighbor-
ing vehicles within the transmission region of Source
vehicle. At the same time, it considers an “E-Region”
and “Critical Distance (E)”, while satisfying three funda-
mental equations (1), (2) and (3). The critical distance,
E, is determined from the possible maximum relative
velocity of the source and all the neighboring other vehi-
cles, one of which will then act as a Forwarding Vehicle.
The FV always resides within the E-Region. This ensures
the minimization of the link failure and at the same
time increases the data communication time between
two vehicles. The simulation results show, our proposed
protocol achieves better performance compared to other
existing methods on all the described four parameters.
These are important for improving QoS. This is the rst
timewherewehaveseenthatPDRisalmostcloseto
97%whenthenumberofvehiclesinthenetworkis
about 300 and the crorresponding size of the network is
3500mX3500m.
ACKNOWLEDGMENT
This work is done at the “Center of Innovation” at NIT Agar-
tala, India. One of the authors Dr. Bidyut K. Bhattacharyya is
the founder of this innovation center.
ORCID
Arindam Debnath http://orcid.org/0000-0001-8786-2823
Habila Basumatary http://orcid.org/0000-0002-6874-6461
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14 A. DEBNATH ET AL.: A ROUTING TECHNIQUE FOR ENHANCING THE QUALITY OF SERVICE IN VANET
AUTHORS
Arindam Debnath received B.Tech in
Information Technology and M.Tech in
Computer Science and Engineering from
West B engal Uni ve rsi ty o f Tech no log y in
the year 2009 and 2012 respectively. Cur-
rently he is working as a PhD scholar
in Computer Science and Engineering
department at National Institute of Tech-
nology, Agartala, India. His research interests are Wireless net-
working in VANET, routing signal, channelization in VANET.
Corresponding author. Email: arindamphd17@gmail.com
Habila Basumatary received B.Tech and
M.Tech degree in Information Technol-
ogy from Assam University, Silchar and
North Eastern Regional Institute of Sci-
ence and Technology, Arunachal Pradesh,
India in 2015 and 2017 respectively. She
is currently working towards the PhD
degree in Computer Science and Engi-
neering department at National Institute of Technology, Agar-
tala, India. Her research interests include energy ecient rout-
ing in Wireless Sensor Networks (WSN), mobility management
in WSN.
Mili Dhar received B.Tech from Tripura
University and M.Tech degree in Com-
puter Science and Engineering from NIT
Arunacha l Pradesh, India in 2016 and
2018 respectively. She is currently work-
ing towards the PhD degree in Computer
Science and Engineering department at
National Institute of Technology, Agar-
tala, India. Her research interests include Controller placement
problem for Software Dened Networks (SDN), mobility man-
agement in VANET.
Bidyut K. Bhattacharyya (F’20) received
theB.ScdegreeinPhysics(withhonors)
from the Presidency College, Calcutta,
West B engal , In dia , the M.S c deg re e in
Physics from the Indian Institute of Tech-
nology Kanpur, India and the Ph.D. degree
in Physics from the State University of
New York at Bualo, NY, USA in 1976,
1978 and 1983 respectively. From 1984 to 2003 he worked at
Intel Corporation Chandler, Arizona, USA as Principal Engi-
neer and then at Hillsboro, Oregon, USA as Sta Engineer from
2003 to 2007. He is the author of 27 patents and more than 60
papers.Dr.BhattacharyyaPh.D.workisrecognizedasoneof
the greatest experiment to show the Lowest Density Liquid Ever
Found in Nature. He was the recipient of IAA award by founder
of Intel Corporation Dr. Andy Grove and Dr. Gordon Moore.
He was awarded the IEEE Grade in 2000 for his contributions
to the electronic packaging.
Mrinal Kanti Debbarma (M’17) received
hisB.TechdegreeinComputerScience
and Engineering from Institute of Engi-
neering and Technology Lucknow in 1994,
M.Tech degree in Computer Science and
Engineering from Motilal Nehru National
Institute of Technology Allahabad in 2011,
and PhD degree in Computer Science and
Engineering from Assam University, Silchar in 2016. Dr. Deb-
barmaisanAssociateProfessorinthedepartmentofCom-
puter Science and Engineering department at National Institute
ofTechnology,Agartala.Hisresearchinterestsarenetwork-
ing, mobile communication, wireless sensor network. He is
presently guiding PhD students, PG students. He has around
18 years of academic and 5 years of industrial experience. He
has published more than 40 technical research papers in inter-
national and national journals and conferences. He is a member
of IEEE IAENG and International Association of Computer
Science and Information Technology.