Guaranteed Geocast Routing Protocol for Vehicular Adhoc Networks in Highway Traffic Environment

Transcription

1 Wireless Pers Commun DOI /s Guaranteed Geocast Routing Protocol for Vehicular Adhoc Networks in Highway Traffic Environment Omprakash Kaiwartya 1 Sushil Kumar 1 Ó Springer Science+Business Media New York 2015 Abstract Geocast routing is one of the most investigated choices for reliable and efficient dissemination of information because of group of vehicles sharing geographic region on the road. Most of geocast routing protocols for intermittently connected Vehicular Adhoc Networks, suffer from low packet delivery, high end-to-end delay and elevated packet loss in intermittently connected networks and low throughput, high hop-to-hop disconnection, larger hop-count in fully connected networks. In this paper, guaranteed geocast routing (GGR) protocol for intermittently connected highway traffic environment has been proposed. GGR utilizes caching and heuristic function for reliable next hop vehicle selection. It also uses FAST (group of neighboring vehicles moving at higher speed than current forwarder) in packet delivery and SLOW (group of neighboring vehicles moving at lower speed than current forwarder) in hop-to-hop failure recovery. One-hop delivery has been guaranteed through acknowledgement. The proposed protocol has been simulated using NS-2 and its performance has been compared with that of adoptive carrystore forward, spray & wait and epidemic routing protocols. Results reveal that the performance of GGR is better in terms of number of routing matrices considered for both intermittently and fully connected networks. Keywords Vehicle-to-vehicle communication Intermittently connected networks Geocast routing VANETs Next hop vehicle Guaranteed routing & Omprakash Kaiwartya omokop@gmail.com & Sushil Kumar skdohare@yahoo.com 1 Communication Networks & Simulation Research Lab, School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi , India

2 O. Kaiwartya, S. Kumar 1 Introduction The number of vehicles is increasing day by day due to the growing world economy which is making driving very risky and unsafe. The recent global status report on road safety summarizes the statistics on road safety around the world. The report considers 182 countries covering 99 % population of the world. According to the report, the total number of deaths due to various traffic accidents on road is 1.24 million per year worldwide. Taking cognizance of this fact, the UN General Assembly has declared the report as the basis for all actions that would be taking for road safety during the entire decade from 2011 to 2020 [1]. The statistical report on road accidents in Delhi in 2012 shows that out of 1822 fatal accident cases, most of them occurred on highway [2]. The story of fatal accidents in highway is no different around the world. The above studies suggests that there is an urgent need to prevent fatal accidents on road by using intelligent transport system (ITS) applications in Vehicular Adhoc Networks (VANETs). Adhoc networks, wireless local area network (LAN) and cellular telephony have been co-evolved into a new research area known as VANETs [3]. The distinguishing features of VANETs from other kinds of adhoc networks includes hybrid network architecture, high speed vehicle mobility, freedom from battery and computational power limitation and wide range of new comfort applications [4]. In order to achieve the goal of on-road safety and efficiency, the state-of-the-art equipment such as sensors, radars, cameras, display screen etc. are currently being integrated with vehicles as an on-board unit. This integration improves safety and comfort while traveling [5]. Recently, Vehicleto-Vehicle (V2V) communication applications have attracted more attention from industry and government organizations around the world due to their outstanding potential to address safety and congestion challenges at lower operational cost [6]. V2V and vehicle-toinfrastructure (V2I) communications applications can also be used for commercial and vehicular infotainment [7]. An example of V2V and V2I communication for the advancement of traffic control system and ease of travel in highway environment have been illustrated in Fig. 1. ITS applications which are based on one-hop information dissemination requires efficient medium access control (MAC) protocol for providing effective broadcasting services in highly dynamic vehicular traffic environment. A number of MAC protocols have been suggested considering either or channelization. The protocols for VANETs are time Fig. 1 Sample applications of V2V and V2I communications

3 Guaranteed Geocast Routing Protocol for Vehicular Adhoc division based multiple access (TDMA) based MAC [8], code division based multiple access (CDMA) based MAC [9], space division multiple access (SDMA) based MAC [10] and based MAC [11]. Among these protocols, p which is based on has been considered as standard MCA protocol for VANETs [12]. Therefore, the protocol has been used as MCA protocol in this work as used in [13] in performance evaluation. Dedicated short range communications (DSRC) [14] has been already employed into VANETs. It is an enhanced version of Wi-Fi technology specially designed for VANETs environment. High data rate with time critical response in rapidly changing environment is the main feature of DSRC. For most of the ITS applications, information dissemination or efficient routing is one of most critical issues considering the highly dynamic nature of vehicular traffic environment [15 17]. Epidemic routing (ER) [18], spray & wait (S&W) [19], probabilistic routing protocol using history of encounters and transitivity (PROPHET) [20], and delay tolerant networks (DTN) to vehicular delay tolerant networks (VDTN) [21] are some routing schemes specifically developed for intermittently connected network environment as posed by vehicular traffic in highway. Most of these protocols use modified version of flooding as their forwarding strategy. These protocols suffer from lower packet delivery ratio, higher end-to-end delay, elevated packet loss, lower throughput, higher hopto-hop disconnection, and large hop-count in highway traffic environment. The above protocols do not have provision for NHV selection which is an important component of routing particularly in vehicular traffic environment [22]. In this paper, we have proposed guaranteed geocast routing (GGR) protocol for VANETs to provide guaranteed delivery of packets in intermittently connected highway vehicular traffic environment. Packet forwarding of GGR is based on four considerations, namely caching of packets in intermittently connected traffic environment, neighboring vehicle speed, packet ownership transfer and heuristic function based NHV selection. Our contribution in this work is as follows: (1) caching of packets which could not be immediately forwarded due to unavailability of NHV in intermittently connected highway traffic environment. Vehicle s mobility is used for delivery of packets until an appropriate NHV is available. Mathematical model to analyze the probability of successful delivery of packets in intermittently connected networks is also derived without considering caching. (2) Division of neighboring vehicles into two groups FAST and SLOW considering their speed. FAST includes all the neighboring vehicles moving at higher speed as compared to current forwarder whereas SLOW includes the remaining neighboring vehicles moving at lower speed as compared to current forwarder. An NHV and two safe vehicles (SVs) are selected from FAST and SLOW respectively. The impact of using FAST on end-to-end delay is mathematically analyzed. (3) Guaranteed delivery of packets through ownership transfer approach, i.e., packet delivery is guaranteed by using ownership transfer in hop-tohop forwarding. Acknowledgement is used in each successive one-hop communication to transfer packet ownership that ultimately guarantees delivery of packets up to destination. Mathematical model to analyze the impact of acknowledgement (ACK) mechanism on packet delivery time is also presented. (4) Selection of NHV based on heuristic function, i.e., An NHV is selected from FAST using a heuristic based cost function. The cost function has two components which represent present and future cost of packet delivery. The time spent by the packet to reach up to the current sender vehicle represents present cost whereas future cost is represented by heuristic function. Mathematical derivation for the construction of heuristic based cost function used for NHV selection is also provided. GGR protocol is simulated using NS-2. A comparative study between GGR and the stateof-the-art protocols: ACSF, S&W and ER for intermittently connected networks, is carried out.

4 O. Kaiwartya, S. Kumar The rest of the paper is organized as follows. Section 2, provides a summary of early and latest research work in routing protocols for intermittently connected networks. Theoretical details of GRR protocol and its mathematical derivations are provided in Sect. 3. Simulation and result analysis are discussed in Sect. 4. The conclusion of the work is presented in Sect Related Work Vahdat and Backer have suggested ER in [18] where random pairwise exchange of message among mobile nodes ensures message delivery. The objectives of ER were to maximize delivery rate for messages and minimize utilization of resources and latency during delivery of packets from source to destination. It does not require any prior knowledge about the network. In ER, each node maintains a list of message bundles that are carried by it and network nodes exchange all messages bundles that are not common whenever they came into each other s communication range. Using this strategy, bundle of messages are eventually spread to all nodes, including their destination. Epidemic is shown to be effective, but suffers from the disadvantages of flooding as node density increases. Another routing scheme for intermittently connected mobile networks, known as S&W was suggested by Spyropoulos et al. [19]. It has two phases, namely spray and wait phase. In spray phase, limited copies of messages are initially flooded by the source node as well as by the other nodes receiving a copy of the message from distinct relays for every message originating at a source node. In case the message is not reached up to destination in spray phase, wait phase is initiated in which each node carrying a message copy performs direct transmission. Hence, S&W waits until one of relay node s messages reaches to the destination node. In [23], the problem of communication disruption among vehicles in highway environment has been addressed by selecting appropriate relay vehicles enabled with temporary storage capability. Road side unit (RSUs) which are far way along any highway creates coverage gap where vehicle falling into the gap are not able to communicate to these RSUs. To address this problem, a cache enabled relay vehicle has been selected considering the speed of target vehicle and relay vehicle so that both the vehicles remain into their communication range when they pass through the coverage gaps creates by RSUs along highway. Although the vehicle speed management is possible for one or two hop communications, yet for multi-hop communications with larger hop count speed management among vehicles are not possible. PROPHET has been presented in [20]. In PROPHET, routing decisions are made using history of vehicle s encounters and their transitivity. For each node, probability of delivery of packets to each destination of the network is predicted using a metric. The metric measures the chance of delivery of packets considering a pair of source and destination. Whenever a node enters into communication range of another node, it compares its metric and increases the accuracy of the metric by exchanging the metric information. Musolesi and Mascolo [24] have suggested a prediction based routing protocol in known as context aware adaptive routing (CAR) for delay tolerant mobile networks. Kalman Filter based prediction and multi-criteria decision theory has been used for selecting next hop during forwarding of packets from source to destination. NHV selection decision considers the mobility of probable NHV and its possible encounters with the destination vehicle in the vehicle s movement history. According to the NHV selection theory in CAR, a vehicle having higher mobility in the network is a better carrier for packets due to its higher probability of encounter with other vehicles in the network. Past encounter history of

5 Guaranteed Geocast Routing Protocol for Vehicular Adhoc vehicles has been given its due important in NHV selection in CAR. Due to high dynamic network environment, movement history based NHV selection could be catastrophic in general situations and may lead to higher packet loss. A geographic routing protocol for vehicular delay-tolerant networks has been suggested in [25]. The protocol uses geographical location information for making routing decision and message spraying technique for disseminating information. The protocol is named as geographic spraying (GeoSpray). A hybrid approach between single and multiple-copy packet forwarding technique is used by source and relaying vehicles. In this hybrid approach, multiple-copy packet forwarding technique is used for spreading limited number of bundle of packets in initial specified period to exploit large number of alternative paths. After the completion of the initial specified period for multiple-copy forwarding, GeoSpray changes the forwarding mode into single-copy packet forwarding technique for taking advantage of encounter probabilities of vehicles. Periodic clearance of already delivered bundle of packets has been utilized for better resource utilization in the networks. Li et al. [26] have suggested a hybrid position based DTN forwarding technique for vehicular sensor networks. Authors have addressed the issue of packet delivery in intermittently connected vehicular sensor networks by combining position-based forwarding scheme with store-carry-forward routing scheme of DTNs. The direction of driving of vehicles has been utilized for making the decision to hold or forward the packets by intermediate vehicles. The protocol works in three modes, namely greedy mode, perimeter mode without periodic checking, and perimeter mode with periodic checking. Although making forwarding decision based on vehicle s driving direction solely may be suitable for urban vehicular traffic environment due availability of dense road network, yet the forwarding technique is not suitable in highway vehicular traffic environment due to the fewer directional changes opted by vehicles in highway. Buffer management model and system for queuing of cached packets has been presented by Niyato et al. [27]. The behavior of the traffic source under competitive environment is studied and analyzed using the buffer management mode and queueing system. Due to this study and analysis, the resources used in transmission of a mobility enabled router are shared among multiple traffic sources for optimizing their transmission strategies non-cooperatively considering utilization in mind. RSU based geographic routing has been suggested by Mershad et al. [28] considering roadside units as message routers (ROAMER). RSUs are assumed to be connected through Internet which has been utilized by on-road vehicles for delivery of information in long distance communications. Authors have claimed that ROAMER effectively adjusts its routing behavior for both dense and sparse vehicular networks. Although the routing strategy looks promising considering their theoretical aspects, yet but the strict assumption of RSUs is a major drawback of the routing protocol. 3 GGR Protocol The area of routing in VANETs is focused towards urban environment [29 35]. Two main characteristics of urban VANETs traffic environment are high density and constrained speed of vehicles that make it distinct from highway VANETs traffic environment. The two main constraints of highway VANETs traffic environment are sparse vehicle networks and high speed vehicle mobility [36]. These constraints make routing task quite challenging in highway VANETs. The design of GGR protocol is a sincere effort to resolve the challenges of routing in highway VANETs. The proposed protocol is based on following considerations.

6 O. Kaiwartya, S. Kumar Caching of packets in disconnected environments Efficient utilization of high speed (FAST) and low speed (SLOW) neighboring vehicles Packet ownership transfer with acknowledgment for guaranteed delivery Novel heuristic function based NHV selection 3.1 Caching of Packets in Unconnected Environment VANETs in highway traffic environment have been considered as sparse network due to less number of forwarding option availability in highway. This is because volume of intercity traffic is not very high as other modes of transportation are also available [36]. This unique characteristic of highway forces us to integrate caching methodology in GGR. Each vehicle has a small cache memory in the network layer which is used to preserve the packet until it finds an appropriate NHV in the transmission range. The cache does not check periodically for the availability of probable NHV for currently cached packets rather it operates in event driven fashion. As soon as the current forwarding vehicle gets a new vehicle entry in its transmission range an event is generated. The newly generated event checks whether there are some packets cached for it or not. This event driven operation of caching approach minimizes unnecessary bandwidth utilization of GGR. The requirement of cache in highway traffic environment has been analyzed by using contradiction theory. As shown in Fig. 2, the problem of packet delivery without using cache can be formulated as follows. The packet can be delivered from source region to destination geocast region using opposite lane vehicles. The opposite lane is divided into required number of transmissions which are shown by circles. The region of each circle is vertically divided into two parts. The front part (towards destination) is considered as message transporter region (MTR). We need at least one vehicle on MTR for every transmission range circle considered to deliver the packet without using cache. The area of MTR can be calculated as MTR ¼ Area of sector BSC þ DABS þ DDCS Considering Transmission range ¼ R; Half of one directional lane width ¼ SA ¼ SD ¼ x; AB ¼ DC ¼ y; angle BSC ¼ a Fig. 2 Packet delivery analysis between source and geocast region without using cache

7 Guaranteed Geocast Routing Protocol for Vehicular Adhoc Substituting y ¼ MTR ¼ a 360 pr2 þ xy 2 þ xy 2 p ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi R 2 x 2 and a ¼ 2 tan 1 MTR ¼ R 2 tan 1 x p pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi þ x ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi R 2 x 2 R 2 x 2 x y ð1þ GGR uses position information of a vehicle for routing. The position information of a vehicle in represented by both x and y coordinates in plane. Therefore, we have considered 2D network model. The availability of vehicles in the network area follows Poisson distribution with vehicle density k. Poisson distribution has been used since we are interested in finding number of vehicles present in MTR given the mean density of vehicle in the network area. Further, the arrival of each vehicle is also independent. If X is the random variable representing number of vehicles present on MTR, then the probability of presence of n vehicles on MTR P MTR (X = n) can be calculated as P MTR ðx ¼ n Þ ¼ ðk MTR n! Þn e ð kmtrþ ð2þ In highway environment, probability of presence of no vehicles is high on many MTRs. The probability of presence of no vehicle on MTR P MTR (X = 0) can be expressed as P MTR ðx ¼ 0Þ ¼ e kmtr ð3þ For the successful message forwarding, at least one vehicle is required on each MTR. The probability of presence of at least one vehicle on MTR P MTR ðx 1Þcan be expressed as P MTR ðx 1Þ ¼ 1 e kmtr ð4þ Considering n number of MTR between source and destination region, the probability of successful packet delivery P succ requires at least one vehicle on each of n MTRs between source and destination. The probability P succ can be expressed as P succ ¼ 1 e kmtr n ð5þ The results in Fig. 3 show that the probability of successful packet delivery decreases with increasing probability of finding no vehicle on MTR P MTR (X = 0). The probability of successful packet delivery P succ increases with increasing the density of vehicle k. Itis clear that the value of P succ becomes almost zero for P MTR ðx ¼ 0Þ ¼ 0:5 and values larger than this. This indicates that the caching methodology should be applied in case of intermittently connected networks due to lower vehicle density. Intermittently connected networks have high probability of P MTR (X = 0) on many MTRs between source and destination. Cooperative and distributed cache invalidation or replacement strategy has been used to deal with the possible memory loss problem caused by caching of packets in disconnected network environment [37, 38]. In this strategy, validity of any cached packets has been checked among neighboring vehicles using cooperative and distributed approach. An on-demand approach has been used for validity verification of cached packets to keep network load under control during vehicular communication.

8 O. Kaiwartya, S. Kumar λ=.0001 Vehicles/m 2 λ=.0002 Vehicles/m 2 λ=.0003 Vehicles/m 2 P succ P MTR (X=0) Fig. 3 Probability of successful delivery of a packet considering probability of presence of no vehicles in MTR 3.2 Efficient Utilization of High and Slow Speed of Neighboring Vehicles The high speed vehicle mobility in VANETs has two consequences. First, it frequently changes vehicular network topology which increases one-hop link disconnection. Second, it helps in maintaining packet mobility especially in intermittently connected network. GGR utilizes the second aspect of vehicle mobility in packet forwarding. The vehicle currently holding the packet to be forwarded is considered as owner vehicle (OV). Following the standard traffic rule, OV divides all vehicles of MTR into two groups FAST and SLOW. FAST includes all vehicles moving at higher speed compared with OV and SLOW includes all vehicles moving at lower or equal speed compared with OV. The objectives of both FAST and SLOW vehicle groups are as follows. FAST This group of vehicles is used to select an NHV. They are responsible for fastest possible delivery of packets towards destination geocast region particularly in intermittently connected networks. Generally it includes all vehicles of lane-1 inside MTR (cf. Fig. 4). SLOW This group of vehicles is used to select SVs. SVs are responsible for preservation of the packet in case of any unwanted happening with OV. Generally it includes all vehicles of lane-2 and lane-3 inside MTR. (cf. Fig. 4). SVs are vehicles moving at normal speed of highway. The impact of using FAST on end-to-end delay of packet delivery has been analyzed. End-to-end delay is the time required for a packet to be transmitted across a network from source to destination. End-to-end delay in connected network ETE connected can be expressed as ETE connected ¼ N delay trans þ delay prop þ delay proc where N is the number of one-hop connections, delay trans is the transmission delay, delay prop is the propagation delay, and delay proc is the processing delay. For the computation ð6þ

9 Guaranteed Geocast Routing Protocol for Vehicular Adhoc Current Forwarding Vehicle FAST Lane-2 Lane-1 Lane-3 SLOW Fig. 4 FAST and SLOW group of vehicles selection of End-to-end delay in disconnected network ETE disconnected, we have modified the above formula as X N 2 ETE disconnected ¼ N 1 delay trans þ delay prop þ delay proc þ d i ð7þ i¼1 where N 1 is the number of connected one-hop connection, N 2 is the number of disconnected or void transmission range circles between source to destination and d i is the delay of ith disconnected transmission range circle. This formula can be simplified as X N2 2R ETE disconnected ¼ N 1 delay trans þ delay prop þ delay proc þ ð8þ V i¼1 i where V i is the vehicle speed of ith vehicle and R is the transmission range. The end-to-end delay of connected path is assumed to be constant which mostly depends on number of one hop connections. The relation of end-to-end delay of disconnected network can be further simplified by assuming the number of disconnected circles to be constants and average vehicle speed v for each vehicle. ETE disconnected / 1 V ð9þ Therefore, end-to-end delay of disconnected network is inversely proportional to speed of vehicle. This reveals that FAST reduces end-to-end delay in intermittently connected networks. 3.3 Message Ownership Transfer with Acknowledgment for Guaranteed Delivery GGR protocol ensures delivery of packets using ownership transfer scheme. OV selects an NHV from FAST and delivers packets to NHV. The delivery of packets to NHV initiates an operation

10 O. Kaiwartya, S. Kumar SV OV ACK ACK SV ACK DATA NHV Lane-2 Lane-1 Lane-3 Fig. 5 Ownership transfer mechanism for guaranteed delivery of ownership transfer. After a successful receiving of packet, NHV sends an ACK to OV. OV sends the ACK to SVs to relieve their responsibility of packet preservation. The ACK means that ownership has been transferred successfully and now NHV has become OV (cf. Fig. 5). The impact of ACK on packet delivery time has been analyzed to validate the fact that ownership transfer mechanism does not degrade the performance of GGR in terms of round trip time (t round_trip ). The transmission time of a packet, t tran can be expressed as t tran ¼ s packet ð10þ r bit where s packet is the size of the packet and r bit is the bit rate in the medium. The propagation time of a bit through a medium, t prop can be expressed as t prop ¼ d ð11þ where d is the distance between sending and receiving vehicles, and s prop is the propagation speed in the medium. The packet delivery time of a packet PDT is the sum of transmission time and propagation time which can be expressed as s prop PDT ¼ t tran þ t prop ð12þ The round trip time, RTT, is the time, from the start of transmission to reception of an ACK. The round trip time is affected by PDT and processing time. It can be expressed as RTT ¼ 2 PDR þ t proc ð13þ where t proc is the packet processing time at the receiver end. This formula of RTT is valid if length of data and ACK packets are comparable or equal. In GGR protocol, the length of ACK packet is considered one bit of information ensuring about ownership transfer. Therefore RTT can be expressed as RTT ¼ t tran þ 2 t prop þ t proc ð14þ In VANETs, vehicles have sufficient packets processing speed with no power limitations and use of DSRC. Therefore, RTT is comparable to PDT. Thus, we have RTT ffi PDT ð15þ

11 Guaranteed Geocast Routing Protocol for Vehicular Adhoc FAST β 1 C Destination-1 OV A γ1 E γ2 H D B F Geocast Region β2 G Destination-2 Fig. 6 The calculation of d max and b max used in NHV selection 3.4 Novel Heuristic Function Based NHV Selection The A-Star (A*) heuristic based algorithm is widely used in path finding and graph traversal. It uses a cost function based on past knowledge and future heuristic [39]. In GGR, A-Star approach has been applied for NHV selection from FAST. The cost function, f(x), for NHV selection is expressed as fðþ¼gx x ðþþhx ð16þ where g(x) represents the total time spent in travelling by the packet from source vehicle to OV using caching, and h(x) represents the future travelling cost of the packet from OV to the destination. Now, we define a novel heuristic function h(x) as hx ðþ¼t cache þ d max þ b max ð17þ where t cache represents total time required to deliver a packet from OV to the destination using caching (without using any intermediate vehicle), d max represents greatest travel distance towards destination defined as the maximum of distances travelled by vehicles towards the destination, and b max represents the maximum angle from angles drawn by joining three points (vehicle position, its destination, geocast destination) for every vehicles of FAST. The t cache can be calculated as j t cache ¼ V ig i j ð18þ where V i G i represents the distance between vehicle V i and geocast region G i, S i represents the speed of vehicle V i. The calculation of d max and b max are explored in detail as follows. As shown in Fig. 6, OV of a packet has two vehicles A, and E in its FAST. It needs to select an NHV from these two vehicles. NHV is decided based on the value of f(x) for each vehicle in FAST. The vehicle posing smallest value for f(x) will be NHV. Vehicles A and E are going to Destination-1 and Destination-2 respectively. The distance d max is calculated as follows. Assuming that each vehicle knows its position, destination, and geocast destination, anglesc 1, and c 2 can be calculated as cosðc 1 S i Þ ¼ jacj2 þjabj 2 jbcj 2 ; ð19þ 2jACjjABj

12 O. Kaiwartya, S. Kumar and cosðc 2 Þ ¼ jefj2 þjegj 2 jfgj 2 2jEFjjEGj ð20þ In general, the angle c i for any vehicle V i having geocast region G i and going to destination D i can be expressed as j cosðc i Þ ¼ V id i j 2 þjv i G i j 2 jd i G i j 2 2jV i D i jjv i G i j ð21þ The travel distance towards destination AD and EH for vehicles A and E respectively, can be calculated as jadj ¼ cosðc 1 ÞjACj; ð22þ and jehj ¼ cosðc 2 ÞjEGj ð23þ In general, the distance d i for any vehicle V i of FAST can be expressed as d i ¼ cosðc i ÞjV i D i j ð24þ The d max can be calculated by comparing values of AD and EH. It is possible that two or more vehicles of FAST may have equal travel distance towards geocast destination d max. This case is handled by b max in heuristic function. The b max can be decided by comparing following values b 1 ¼ cos 1 jac! j 2 þjbcj 2 jabj 2 2jACjjBCj and b 2 ¼ cos 1 jfgj 2 þjegj 2 jefj 2 2jFGjjEGj Similarly, the general formula of b i for vehicle V i having geocast region G i and going to destination D i can be expressed as! b i ¼ cos 1 jv i D i j 2 þjd i G i j 2 jv i G i j 2 ð26þ 2jV i D i jjd i G i j Memory loss due to heuristic consideration in NHV selection approach has been addressed using the same cooperative and distributed approach used for cache validation or replacement in Sect Packet caching technique along with cooperative and distributed cache validation or replacement ensures the robustness in terms of almost eliminating packet loss caused by high mobility of vehicles in on-road vehicular traffic environment. Cooperative and distributed approach used to deal memory loss due packet caching and heuristic consideration in NHV selection represents cooperation among neighboring vehicles for enhancing efficiency in information dissemination of the protocols. 3.5 GGR Algorithm In this section, an algorithm for GGR protocol that guarantees packet delivery in highway VANETs has been presented. Explanation of each step of GGR and a flowchart for it has been also provided.! ð25þ

13 Guaranteed Geocast Routing Protocol for Vehicular Adhoc

14 O. Kaiwartya, S. Kumar Explanation of Steps of GGR Algorithm As soon as a vehicle receives a packet for forwarding towards geocast destination, it uses steps 1 15 of the algorithm. In the 1st step, initialization of sets is performed. In 2nd step, the SONV set is assigned the information about neighbors of current vehicle. In 3rd step, OV checks whether GR is within its transmission range. If GR is found within transmission, OV transmits the packet to all vehicles of GR and the algorithm exits. In case GR is not within transmission range of OV, the algorithm executes steps 4 15 to determine NHV. In step 4, vehicles moving at slower speed than OV, are identified as SLOW and vehicles moving at higher speed than OV, are identified as FAST. In 5th step, the packet is transmitted to two vehicles of SLOW for packet safety or preservation. In 6th step, value of heuristic based cost function f(x) is calculated for each vehicle of FAST. In 7th step, vehicles having lowest f(x) value are identified as CNHV. If CNHV contains more than one vehicle then b i is calculated for each vehicle of CNHV in 9th step. The vehicle with greatest b i value is selected as NHV in steps 10 and 11. Otherwise, in 12th step, the single CNHV vehicle is selected as NHV. The packet is transmitted to NHV in step 13. In 14th step, the ACK received from NHV is forwarded to SV. In step 15, the NHV becomes OV and thereafter the algorithm exits. To facilitate the understanding of logical flow of steps in the algorithm, a flowchart is also presented in Fig Complexity Analysis The complexity of GGR algorithm can be described using two components namely space and time complexity. Considering that vehicles on-board unit has sufficient storage capacity, only time complexity analysis is the major component in the complexity analysis of the proposed routing technique. Let, N nv is the number of neighboring vehicles of a forwarder vehicle v f and half of the vehicles from N nv are FAST vehicles and remaining half are SLOW vehicles. The number of traffic flows of the forwarding vehicle is represented by N tf and N tf \N nv. Maximum number of re-transmission required for successful transmission is denoted by N re-t and N re t \N nv. Using these notations, the complexity of GGR can be expressed asoðn nv longðn nv ÞÞ. This is due to the fact that the proposed routing scheme groups the current neighboring vehicle into FAST and SLOW and further processes each group of vehicles for appropriate forwarding vehicles selection considering heuristic function based refinement. 4 Simulation and Results In this section, outcome of the simulations carried out to analyze the performance of GGR protocol in terms of handling intermittent network traffic environment, speed for packet delivery and safety, and novel heuristic function based NHV prediction is presented. We computed packet delivery in intermittently connected networks with varying buffer sizes and increasing vehicle density, flooding effects, end-to-end delay, packet loss, and throughput. Results obtained for GGR protocol are compared with three wellknown routing protocols for intermittently connected networks ER [18], S&W [19], and ACSF [23].

15 Guaranteed Geocast Routing Protocol for Vehicular Adhoc Fig. 7 Flowchart of GGR algorithm 4.1 Simulation Environment GGR algorithm has been simulated using network simulator, ns-2.34 [40, 41]. Mobility model generator for vehicular networks (MOVE) [42] has been used to generate realistic mobility model and highway traffic environment. MOVE is built on the top of an opensource micro-traffic simulator. Most of the essential scenario of highway traffic environment such as number of roads, number of lanes in each road, number of flows in each lane, speed of vehicles etc. have been setup and implemented utilizing two main modules of

16 O. Kaiwartya, S. Kumar MOVE namely road map editor and vehicle movement editor. The mobility trace generated by MOVE has been directly used in ns2. The two types of network environment, intermittently connected and fully connected have been used to evaluate the performance of GGR. In intermittently connected network, performance of main function namely caching approach, preservation policy and heuristic based NHV selection have been analyzed. In fully connected network, the analysis focuses on verification, whether our caching approach increases forwarding overhead when they are no longer required. A km 2 simulation area has been used to implement highway road and traffic patterns. Highway of six lanes, each lane of width 35 m has been used as road pattern. Vehicle speed used in the simulation for the computation of various network performance parameters is km/h, except for the computation of packet delivery time in intermittently connected networks. We have considered transmission range of 300 m, packet size of 512 bytes, traffic type as CBR, shadowing propagation model, wireless channel type, Omni directional antenna model, and p MAC protocols as basic parameters setup for using GGR. The routing protocols used for comparative study with GGR are ACSF, S&W and ER. After setting the network and traffic flow with above discussed parameters, we conduct simulations. In each simulation run source vehicle and geocasting region is selected randomly that remain same for all the ten simulation runs. In this way, we take average of ten different simulation runs for each particular value used in results. 4.2 Simulation Metrics The performance analysis of GGR and its comparison with that of ACSF, S&W and ER is based on following routing metrics. Packet delivery ratio It is a ratio between number of packets generated at source vehicle and number of packets delivered to vehicles at geocast region. Packet duplication It is the total number of duplicate copies of a particular packet generated by vehicles to send it from source to destination. It is studied as per packet duplication rate. In intermittently connected networks, it is an important metric since most of the routing protocols for intermittently connected networks, are based on some form of flooding approach. Packet loss The packet loss is the failure of one or more transmitted packets to arrive at their destination. It is mainly because of network partitions, congestion and buffer overflows. End-to-End delay End-to-end delay is the time taken by a packet to travel across network from source to destination and vice versa. It is actually the addition of transmission delay, propagation delay and processing delay for each of links between source to destination. Throughput Throughput is the number of message successfully delivered from source to destination in unit time. It is measured in terms of bits per second. It is not a crucial metric for sparse networks (highway) but for dense networks (urban), it must be observed. Hop-to-hop disconnection It is the failure of packet delivery during one hop communication between any sender and receiver. It shows the reliability of hop-tohop connection establishment scheme or NHV selection scheme.

17 Guaranteed Geocast Routing Protocol for Vehicular Adhoc Hop count It is the total number of vehicles minus one involved in packet delivery path from source to destination. It is one of the important routing matric for fully connected network. The end-to-end delay is indirectly affected by it. 4.3 Analysis of Results The simulation results obtained for GGR protocol have been presented into two sections. In section A, impact of intermittently connected networks on the performance of GGR has been analyzed. In section B, the performance of GGR in fully connected network has been analyzed Impact of Intermittently Connected Network In this simulation, the vehicle density is kept very low to create network partition in between communicating vehicles. We considered vehicles on roads for this simulation. A high time-to-live (TTL) value, 10 min is used to focus on the tolerance of network partitions. Results in Fig. 8 show the comparison of packet delivery as function of cache size in terms of packet capacity among GGR, S&W, and ER. It can be clearly observed that the packet delivery rate of GGR is above 90 % and cache size has no impact on packet delivery of GGR. This can be attributed to the fact that GGR uses packet caching closely integrated with packet preservation technique that eliminates packet delivery failure. In addition, the packet forwarding of GGR is not based on any flooding technique, because of which its packet delivery does not depend on cache size. The packet delivery of ACSF is also unaffected from increasing size of cache but its packet delivery is far below from the packet delivery of GGR. S&W uses flooding for packet forwarding in restricted way. Initially it floods limited number of copies of a packet in spray phase. This is the reason for continuous increment in packet delivery with increasing buffer size. It also uses packet caching to minimize failure in packet delivery caused by partitioned networks. This is reason of comparable packet delivery of S&W and GGR with larger cache size. ER shows very low packet delivery because its packet forwarding is completely based on flooding. It requires even larger cache size in comparison with S&W and GGR to achieve higher packet delivery rate since traditional flooding cause high packet duplication at each vehicle. Thus, the performance of GGR is better compared to S&W and ER in terms of packet delivery. Results in Fig. 9 show the comparison of packet delivery as function of number of vehicles in the network among GGR, S&W, and ER. It clearly shows that network partition has no impact on packet delivery of GGR which is above 90 % for each of the network density considered. This can be attributed to the fact that GGR uses SLOW integrated with packet caching for on-demand recovery of failure in packet delivery. Two SVs are responsible for re-generation of any hop-to-hop lost packet. The packet delivery of ACSF is decreasing with the increase in number of vehicles due its strict two hop forwarding technique. Initially, S&W shows much lower packet delivery compared to GGR for almost partitioned networks. But, as soon as network partition decreases with increasing network density, the packet delivery of S&W continuously increases. The reason behind this is that S&W initially assumes fully connected network in spray phase and starts flooding with limited copies of a packet. It then expects that spray phase will deliver the packet before starting wait phase. In case of partitioned network, spray phase fails almost every time

18 O. Kaiwartya, S. Kumar Fig. 8 Comparison of packet delivery considering increasing cache size in terms of number of packets Packet Delivery [%] GGR ACSF S&W ER Cache Size [NP] Fig. 9 Comparison of packet delivery considering minor increase in number of vehicle Packet Delivery [%] GGR ACSF S&W ER Number of Vehicles [N] resulting in much lower packet delivery. As soon as the network density increases, the spray phase starts succeeding in delivering the packets. This improves the packet delivery of S&W with increasing network density. ER shows lowest packet delivery compared to S&W and GGR in completely partitioned network because ER does not have any packet recovery technique in case of route failure. Moreover, its forwarding is completely dependent on flooding which does not work well in partitioned networks. Thus, the packet delivery of GGR is least affected by network partitions compared to S&W and ER. Results in Fig. 10 show the comparison of network load for forwarding as a function of number of vehicles in the network among GGR, S&W, and ER. Network load is defined as the percentage of vehicles receiving duplicate copies of a packet. It clearly reveals that the network load of GGR is almost half compared to S&W and ER. Initially, GGR shows

19 Guaranteed Geocast Routing Protocol for Vehicular Adhoc % network load for intermittently connected networks and approaches towards 10 % with increasing network density. This can be attributed to the fact that GGR does not use flooding for forwarding. It selects a FAST vehicle as NHV and two SLOW vehicles as SVs during one hop forwarding which eliminates the need for larger number of packet duplication. Also, the FAST and SLOW vehicle selection approach is not affected by network density. The network load of ACSF is also decreasing with the increase in number of vehicles but the network load is higher than the network load of GGR. This is because of cooperation approach among all the vehicles during forwarding. Both, S&W and ER initially show almost 100 % network load with intermittently connected networks. But, with increasing network density, packet duplication of S&W decreases slowly as compared to GGR. The reason for this is that initially with intermittently connected networks, the limited flooding schemes in spray phase of S&W behaves as traditional flooding which is similar to ER. Therefore, S&W and ER show almost 100 % network load with intermittently connected networks. The decrement of network load with increasing network density of S&W is higher than ER because of its capability to limit flooding which becomes effective with increasing network density. Thus, GGR shows lowest network load compared to S&W and ER. Results in Fig. 11 compare end-to-end delay as a function of number of vehicles in the network among GGR, S&W, and ER. It clearly indicates that the end-to-end delay of GGR is least compared to S&W and ER. Also, increasing the speed of vehicles shows positive impact on end-to-end delay of GGR by further reducing its end-to-end delay. This can be attributed to the fact that GGR always use FAST vehicles for packet forwarding. FAST vehicles increase the packet delivery speed of GGR. The end-to-end delay of ACSF is also decreasing with the increase in number of vehicles but its end-to-end delay is higher than the end-to-end delay of GGR due to the vehicle speed based intermediate vehicle selection. S&W also shows decreasing end-to-end delay with increasing network density, however, its end-to-end delay is always higher than GGR for each of the network density considered. The reason behind this is that in spray phase, S&W always waits for successful delivery of packets. The ER shows high end-to-end delay because of its complete dependability on flooding for forwarding. Also, the decrement in end-to-end delay with increasing network Fig. 10 Comparison of percentage of vehicles receiving duplicate copies of a packet (Network Load) considering minor increase in number of vehicles Network Load [%] ER S&W ACSF GGR Number of Vehicles [N]

20 O. Kaiwartya, S. Kumar Fig. 11 Comparison of end-toend delay considering minor increase in number of vehicles and different speed of vehicles End-to-End Delay [ms] ER 10 S&W ACSF 5 GGR S=50Km/h GGR S=60Km/h Number of Vehicles [N] density of ER is very slow compared to S&W and GGR because flooding increases the probability of collision of packets with increasing vehicle density. Thus, GGR outperforms S&W and ER in terms of end-to-end delay. Results in Fig. 12 show the comparison of packet loss as a function of number of vehicles in the network among GGR, S&W, and ER. It clearly shows minimum packet loss for GGR that is less than 10 % compared to S&W and ER. This can be attributed to the fact that GGR uses unique SV concept for immediate recovery of one hop transmission failure which ultimately reduce possibility of the packet loss. Also, the network density has marginal impact on the packet loss of GGR compared to S&W and ER because the NHV selection of GGR is not dependent on network density. The packet loss of ACSF is higher than that of GGR with the increase in number of vehicles due to the increasing complexity during vehicles speed management in cooperative speed based forwarding of ACSF. The packet loss of S&W is higher than GGR for each of the network density considered because S&W has no mechanism to recover one hop transmission failure which ultimately results in packet loss. Also, the packet loss of S&W decreases with increasing network density and increasing cache size. The reason behind this is that S&W uses limited flooding mechanism in spray phase. This limited flooding scheme of S&W causes packet loss with insufficient cache size. The increment in cache size of S&W improves the capacity of handling more packets caused by spray phase flooding which ultimately reduces packet loss. ER shows high packet loss compared to S&W and GGR with intermittently connected networks. This is because of complete paralysis of flooding scheme of ER in delivering packets in intermittently connected networks. The impact of cache size increment on packet loss is low for ER compared to S&W. It is because of ER needs larger cache compared to S&W for the same network density as ER uses traditional flooding whereas S&W uses limited flooding. Therefore, GGR has least packet loss rate compared to S&W and ER Fully connected environment In this simulation, the vehicle density is kept high varying from 100 to 600 vehicles in the network area to analyze the performance of GGR in fully connected network. The primary

21 Guaranteed Geocast Routing Protocol for Vehicular Adhoc Fig. 12 Comparison of packet loss considering minor increase in number of vehicles with different cache size Packet Loss [%] ER CS=50 ER CS=100 S&W CS=50 S&W CS=100 ACSF GGR Number of Vehicles [N] goal of this part of analysis is to give a valid proof that all the concepts used to overcome the problem of partitioned networks in GGR, do not overload packet forwarding in fully connected networks. We compute throughput, hop-to-hop disconnection and hop count in delivery path with increasing number of vehicles in the network. We considered km/h vehicle speed for analysis. A nominal TTL value 0.5 s is used so that stale packets do not overload network. Results in Fig. 13 show the comparison of throughput as a function of number of vehicles in the network among GGR, S&W, and ER. It can be clearly observed that the throughput of GGR is better compared to S&W and ER in fully connected network. This can be attributed to the fact that GGR did not cache the packet unless it has a FAST vehicle available for NHV. It means the packet caching mechanism of GGR did not hamper packet forwarding as additional delay in fully connected network. The throughput of ACSF is lower than that of GGR. With increasing vehicle density the strict two-hop forwarding technique of ACSF result in reduction in throughput with the increase in hop count. S&W initially uses limited packet flooding in spray phase of forwarding which overloads fully connected networks causing lesser throughput than GGR. ER uses traditional flooding based forwarding which is again cumbersome for fully connected networks and ultimately causing lowest throughput compared to S&W and GGR. Therefore, GGR s concepts to cope up network partitions do not overload the forwarding behavior in the fully connected network and gives better performance compared to S&W and ER in terms of throughput. Results in Fig. 14 show the comparison of hop-to-hop disconnection as a function of number of vehicles in the network among GGR, S&W, and ER. It clearly shows that the hop-to-hop disconnection of GGR is lowest compared to S&W and ER in fully connected network. This can be attributed to the fact that the heuristic function based NHV selection in GGR selects most reliable FAST vehicle towards destination direction. The heuristic function uses angle b max as one of the important parameter in NHV selection. The inclusion of angle b max in NHV selection reduces the possibility of sudden direction change of NHV which ultimately decreases the hop-to-hop disconnection of GGR. The hop-to-hop

22 O. Kaiwartya, S. Kumar Fig. 13 Comparison of throughput with increasing vehicle density Throughput [Kbps] GGR ACSF S&W ER Number of Vehicles [N] disconnection of ACSF is higher than that of GGR with increasing vehicles density due to its two-hop focused forwarding technique. S&W does not have any NHV selection scheme rather it believes in spraying limited number of packets in the first phase of forwarding. This spraying scheme becomes inefficient in the highway speed range causing higher hopto-hop disconnection compared to GGR. The hop-to-hop disconnection of ER is highest compared to S&W and GGR because in ER vehicles exchange all packets whenever they come into the contact of each other s transmission range. This large number of packet exchange resulted into high contention rate among vehicles ultimately causing highest hopto-hop disconnection in ER compared to S&W and GGR. Thus, it can be clearly noted that GGR outperforms S&W and ER in terms of hop-to-hop disconnection. Results in Fig. 15 show the comparison of hop count in packet delivery path as a function of number of vehicles in the network among GGR, S&W, and ER. It clearly reveals that the hop count in packet delivery path of GGR is smallest compared to S&W and ER in fully connected network. The reason behind this is that GGR always selects unique NHV from FAST with lowest value of cost function f(x). The FAST vehicles have high probability of being in greatest forward distance. The NHV selection from FAST enables GGR to use least possible number of hops in delivering packets from source to destination in fully connected network. Therefore, the vehicle density of network has lowest impact on hop count of GGR compared to S&W and ER. The hop count of ACSF is higher than that of GGR due to the inclusion of all the vehicle in forwarding within the source and destination vehicles. The initial spraying mechanism of S&W causes bigger hop count in delivery path compared to GGR. This can be attributed to the fact that in fully connected network, the spraying technique of S&W automatically converts into traditional flooding based forwarding which ultimately results in bigger hop count value in packet delivery path. The traditional flooding based forwarding of ER includes all the vehicles moving towards destination in packet delivery path causing biggest hop count value compared to S&W and GGR. Thus, the GGR uses least hop count in packet delivery path compared to S&W and ER.

23 Guaranteed Geocast Routing Protocol for Vehicular Adhoc Fig. 14 Comparison of hop-tohop disconnection with increasing vehicle density Hop-to-Hop Disconnection [%] GGR ACSF S&W ER Number of Vehicles [N] Fig. 15 Comparison of hop count in delivery path with increasing vehicle density Hop Count in Delivery Path GGR ACSF S&W ER Number of Vehicles [N] 5 Conclusion In this paper, a GGR protocol for highway traffic environment has been proposed and simulated using NS-2. The performance of the protocol is evaluated in terms of packet delivery, packet duplication, packet loss, end-to-end delay, throughput, hop-to-hop disconnection, and hop count, and compared with the state-of-the-art protocols. Analysis of the simulation results lead to the following conclusion. The packet delivery of GGR is found to be better than that of the state-of-the-art protocols. The increment in buffer size has no impact on packet delivery of GGR. The packet delivery of GGR is also least affected by network partition as compared to ACSF, S&W and ER. The network load of

24 O. Kaiwartya, S. Kumar GGR is almost half compared to S&W and ER. It starts with about 40 % for intermittently connected networks and approaches towards 10 % with increasing network density. The end-to-end delay of GGR is least in comparison to what it is in case of ACSF, S&W and ER. Additionally, increasing the speed of vehicles brings a reduction in end-to-end delay of GGR. The packet loss of GGR is nominal that is \10 %. In fully connected network, the throughput of GGR is better than that of the state-of-the-art protocols. The hop-to-hop disconnection of GGR is lowest in fully connected network. The hop count in packet delivery path of GGR is smallest compared to the state-of-the-art protocols in fully connected network. From the analysis of results obtained through simulation, it is clear that GGR is more suitable for highway traffic in vehicular Adhoc networks as compared to the considered state-of-the-art protocols. References 1. Status Report on Road Safety (2013). World Health Organization (WHO). violence_injury_prevention/road_safety_status/2013/en/. Accessed 10, The statistical report 2012, Delhi Police. Accessed 13, Karagiannis, G., Altintas, O., Ekici, E., Heijenk, G., Jarupan, B., Lin, K., & Weil, T. (2011). Vehicular networking: A survey and tutorial on requirements, architectures, challenges, standards and solutions. Communications Surveys & Tutorials, IEEE, 13(4), Olariu, S., & Weigle, M. C. (Eds.). (2010). Vehicular networks: From theory to practice. Boca Raton: CRC Press. 5. Emmelmann, M., Bochow, B., & Kellum, C. (Eds.). (2010). Vehicular networking: automotive applications and beyond (Vol. 2). Hoboken: Wiley. 6. Hartenstein, H., & Laberteaux, K. P. (Eds.). (2010). VANET: Vehicular applications and inter-networking technologies. Chichester: Wiley. 7. Baiocchi, A., & Cuomo, F. (2013). Infotainment services based on push-mode dissemination in an integrated VANET and 3G architecture. Journal of Communications and Networks, 15(2), Omar, H. A., Zhuang, W., & Li, L. (2013). VeMAC: A TDMA-based MAC protocol for reliable broadcast in VANETs. IEEE Transactions on Mobile Computing, 12(9), Watanabe, F., Fujii, M., Itami, M., & Itoh, K. (2005). An analysis of incident information transmission performance using MCS/CDMA scheme. In Intelligent Vehicles Symposium, Proceedings, IEEE (pp ). IEEE. 10. Blum, Jeremy J., & Eskandarian, Azim. (2007). A reliable link-layer protocol for robust and scalable intervehicle communications. IEEE Transactions on Intelligent Transportation Systems, 8(1), IEEE Working Group. (2010). IEEE standard for information technology-telecommunications and information exchange between systems-local and metropolitan area networks-specific requirements-part 11: Wireless LAN medium access control (MAC) and physical layer (PHY) specifications amendment 6: Wireless access in vehicular environments. IEEE Standard, p. 12. Armstrong, L. & Fisher, W. IEEE P Wireless access for vehicular environment. Draft standard, Accessed 16, Kaiwartya, O., Kumar, S., Lobiyal, D. K., Abdullah, A. H., & Hassan, A. N. (2014). Performance improvement in geographic routing for vehicular Ad Hoc networks. Sensors, 14(12), DSRC Standards: What s New? ITS Standards Advisory number 3. US Department of Transportation. Accessed 02, Kaiwartya, O., Kumar, S., Lobiyal, D. K., Tiwari, P. K., Abdullah, A. H., & Hassan, A. N. (2015). Multi-objective dynamic vehicle routing problem and time seed based solution using particle swarm optimization. Journal of Sensors, 2015, Rao, R. S., Soni, S. K., Singh, N., & Kaiwartya, O. (2014). A probabilistic analysis of path duration using routing protocol in VANETs. International Journal of Vehicular Technology, 2014, Dora, D. P., Kumar, S., & Kaiwartya, O. (2015). Efficient dynamic caching for geocast routing in VANETs. In Proceedings of the international conference on signal processing and integrated networks (SPIN-2015), February, Noida. 18. Vahdat, A., & Becker, D. (2000). Epidemic routing for partially connected ad hoc networks (p. 18). Technical Report CS , Duke University.

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26 O. Kaiwartya, S. Kumar Omprakash Kaiwartya is currently a Ph.D. research scholar at School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India. His research interests include Vehicular Ad hoc Networks and Mobile Ad hoc Networks. He received his MCA and M. Tech degrees in Computer Science and Technology from School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India in 2008 and 2012, respectively, and B.Sc. degree in Computer Science from Guru Ghashidas University, India in He has worked as Software Consultant at Jawaharlal Nehru University, New Delhi, India from Mr. Omprakash has published papers in International Journals and Conferences including ACM, IEEE, Springer, Hindawi, Inderscience, and MDPI. Sushil Kumar received his Ph.D., M. Tech and MCA degrees in Computer Science from School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India in 2014, 1999 and 1997 respectively, and B.Sc. degree in Mathematics from Kanpur University, India in He is currently working as Assistant Professor at School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India. His research interest includes vehicular ad hoc networks, mobile ad hoc networks and wireless sensor networks. Dr. Kumar has published papers in International Journals and Conferences including IEEE, Springer, Inderscience, and Hindawi Publishing Corporation.