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Cross-Layer Design Issues and Solutions in Mobile (Vehicular) ad-hoc Networks Jasmeet Singh Masters of Applied Science, Department of Systems and Computer Engineering Carleton University, Canada. Email id:
[email protected] Abstract— Vehicular ad-hoc networks (VANETS) are devised for addressing passenger safety issues which necessitate real-time delivery of emergency messages. Existing solutions in literature, centered upon OSI model and mobile ad-hoc networks (MANETS) have been futile to address concerns in VANETS due to dynamically changing topology and swiftly varying mobility of vehicles on highways. In recent years, various cross-layer solutions are proposed as an alternative to pure-layered design to handle different issues related to routing, security, congestion, data dissemination, scheduling, energy efficiency, QoS, admission control and effectual broadcasting etc. In this paper, various issues related to aforementioned concerns, that have been addressed in various reference papers are mentioned and their cross-layer based solutions are deliberated in brief. Succinctly, cross-layer design in VANETS facilitates alert messages to be shared among different layers and thus provide an optimized and reliable solution. Proficiency of proposed techniques has been corroborated by performing extensive simulations in C++, MATLAB, NS-2, NS-3, OMNet++, Mobisim, Qualnet and other simulators. Lastly, conclusion and some open research issues for future Intelligent Transport Systems (ITS) have been mentioned at the culmination of this paper. Index Terms — VANETS, Cross-layer design, safety, routing.
I. INTRODUCTION Research conducted in [67] have shown that 60% of accidents can be avoided, if drivers are notified apriori about road conditions ahead by sending them sudden breaking alert, overtaking vehicle alert, intersection collision warning and lane change warning. Almost 50 public safety applications, based upon dedicated short-range communication (DSRC) technology have been submitted by top car manufacturers like BMW, Chrysler, Ford and General Motors [62]. Apart from safety applications, various miscellaneous services like general announcements, notifications, the point of interest (POI) such as gas stations, restaurants, malls and advertisements by various roadside vendors are provided on roadside units (RSU). Hence, vehicular networks form the basis of intelligent transportation system (ITS) applications, which are intended for affirming road safety, offering drive comfort and providing information and entertainment services[17][9][37][12]. Mobile ad-hoc networks (MANETS) and vehicular ad-hoc networks (VANETS) have some resemblances in common, as both are self-organizing, selfmanaging, do not need underlying
infrastructure and have low bandwidth requirements. Nevertheless, network topology in VANTES is highly dynamic and mobility of vehicles is also constrained within lane borderlines [8][36][44][51]. Message communication in VANETS can be done either by sending periodic messages (Beacon messages) or by sending event-driven messages (emergency messages) [14]. Beacon messages include general vehicle information like speed, direction, location coordinates and timestamp of the message and are intended to provide the incumbent view of topology as topology keeps altering with time [15]. On the other hand, emergency messages are sent by vehicles through on-board units (OBU) and wireless sensors when they discover some dangerous situations on the highway. Thus, ensuring QoS guarantees for emergency message transfer is a major concern in VANETS, as information is passed among vehicles is decisive and needs to be received in real-time [50][22]. For maximum area coverage, emergency messages are broadcasted, however, since the maximum coverage of DSRC is 1000m, therefore broadcasting is done in multi-hop approach with receivers having capability for emergency message rebroadcasting [5][27][56]. This paper is structured as follows. In Section-II, we have discussed various types of communication architectures in VANETS. Section-III is focused on several issues in VANETS and their cross-layer based solutions along with the simulation results which validates the adeptness of proposed solutions when compared with established solutions. In Section-IV, we have mentioned some of the open research issues related to VANETS that needs to be resolved in future. Finally, in Section-V, a brief conclusion is presented. II. VANET ARCHITECTURES Communication architecture in VANETS can be pure adhoc, pure cellular or hybrid [69]. One of the popular communications type in VANETS is vehicle-to-vehicle (V2V), in which vehicular network is formed among two or more vehicles [12][68]. When OBU in the vehicle receives data from RSU along the roadside, it is called as vehicle-toinfrastructure (V2I) or vehicle-to-roadside (V2R) communication as shown in Figure 1. Hybrid infrastructure is formed by combining V2I and V2V architectures in which various heterogeneous wireless systems such as LTE, 3G and WiMAX are installed on RSU’s facilitating V2V communications [69].
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Figure 2. Receiver-oriented repetition of class-2 message [26]
Figure 1.
Urban VANET communication scenario [68]
III. CROSS-LAYER DESIGN ISSUES IN VANETS There are ample cross-layer design issues related to design, implementation and deployment of VANETS from technical and fiscal prospective. For achieving more safety, security and augmenting VANET adeptness, concerns mentioned below need to be addressed. A. Performing reliable broadcasting in VANETS. Upon identifying potential risks on the road, vehicle broadcasts emergency alert-message to following vehicles lying within Risk Zone (RZ). The problem occurs when emergency alert-message has to be transmitted out of RZ by performing multi-hop broadcasting. For increasing efficacy of multi-hop broadcasting, a number of retransmissions needs to be minimized in conjunction to fast messages dissemination [1][5][6]. To solve this issue, Bononi et al. in [8] have proposed a distributed algorithm using cross-layer interactions in MAC layer. In proposed approach, backbone member vehicles are provided high-priority access to the channel. Performance enhancements were realized when both unicasting and broadcasting were collectively used for disseminating alert messages. In [26] Trivedi et al. have proposed a distributive cross-layer technique, which uses preemptive priority for designing control channel for enabling dedicated short range communications (DSRC). Authors have categorized safety messages into three classes having different exigencies based upon their importance. Class-1 messages are emergency warning messages like head-on collision warning, cooperative forward collision alert, and vehicle crash information while Class-2 messages contain emergency notifications like imminent road hazards and weather information. Class-3 messages are meant to ensure the safety of passenger by periodically sending location, speed, and other data to ensure collision avoidance. While routing, Class-1 messages are given highest priority while class-3 messages have the least priority. Broadcast reliability is enhanced by rebroadcasting emergency alert message by the receiver as shown in Figure 2. Simulation results have shown that proposed scheme in [26] is more scalable and robust as compared to IEEE 802.11 and DSRC services.
In order to overcome the limitations of short-range, multi-hop broadcasting, Hamid at el. in [29] have used long-range cellular networks for emergency message dissemination. Their method is centered upon geographically based unicasting (GeoUnicasting), which uses GPS for finding the location of destination vehicle. When source vehicle has to send a message to destination vehicle (see Figure 3.) then first it checks that if there is an available route in its routing table. In case, no route is available, then sender vehicle uses cellular services like 3G or LTE to find the position of destination vehicle.
Figure 3. Cellular support to GeoUnicasting [29]
Simulation results in [29] show that proposed method is more effective in the case of infotainment applications where clients are ready to pay extra charges for getting superior quality of service (QoS). B. Routing issues in VANETS. For handling routing issues in VANETS, cross-layer based routing metrics are required as conventional hop-count based metrics does not offer effective results, this is because frequent changes in mobility among vehicles led to a change in hop-count based metrics more frequently [9]. Also, in metropolitan areas, when a large number of vehicles have to connect to each another, then established MANETS based routing protocols are not effective [15][37][21]. Fazio et al. in [12] have proposed a novel method, based upon a cross-layer technique which uses routing metric resulted using a prediction algorithm by performing on-demand routing. Simulations performed using NS-3 in [12] showed that substantial performance benefits were obtained by maximizing throughput, PDR and reducing delay and interference levels.
3 Chen et al. in [9] have proposed an adaptive cross-layer multipath routing protocol called R-S-AOMDV, which is an improvement of AOMDV (ad-hoc on-demand multipath distance vector) protocol.
with prediction only’.
Figure 4. End-to-end delay in R-S-AOMDV and AOMDV [9]
Simulations performed in NS-2 shows that significant performance gains have been achieved in [9] by obtaining lower end-to-end delay when R-S-AOMDV protocol was used with POO (Pareto on/off distribution) traffic model as compared to AOMDV protocol as shown in Figure 4. In [22] Katsaros et al. have proposed a V-AODV protocol which is an improvement of AODV protocol. This protocol uses cross-layer design between physical and network layer. Authors have shown using NS-2 simulations that, radio propagation model (RPM) does not represent complex scenarios in VANETS consisting of various obstacles. One of a major contribution of [22] is that they have integrated the Communication Ray Tracer (CRT) developed by SIC-XLIM laboratory [52] into NS-2, which allows accurate modeling of propagation models in VANETS. For performing cooperative routing route discovery only cross-layer cooperative routing (CLCR) was available. The disadvantage of using CLCR routing is that it uses more transmission energy for redundantly sending the hello packets. To overcome this issue in [37] Shaik et al. have proposed and efficient cross-layer routing mechanism which uses weighted neighbor stability (WNS) algorithm. Authors have also added piggybacking feature into AODV routing using WNS algorithm. The simulation was performed in NS-2, which showed performance improvement in packet delivery fraction, routing load and average end-to-end delay, when proposed WNS algorithm based routing was compared with CLCR routing. To further advance routing performance in VANETS, a novel position based routing named cross-layer, weighted, position based routing (CLWPR) is proposed in [21], which predicts the location of vehicles using navigation information (GPS). CLWPR routing is however not effective for applications that have QoS requirements, as it increases endto-end delay while increasing packet delivery ratio (PDR). Monte-Carlo simulations were performed in [21] using NS-2 for comparing end-to-end delay and PDR in CLWPR and GPSR whose results are shown in Figure 5. It is evident in Figure 5 that PDR is maximum for ‘CLWPR with prediction and eMAP information’ as compared to GPSR and ‘CLWPR
Figure 5. Performance comparison of CLWPR and GPSR [21]
A different approach was followed in [31] based upon multi-objective decision making, in order to increase routing performance by using cross-layer design approach. Authors have proposed CO-GPSR (cooperative Greedy parameter stateless routing) which used radio path diversity in VANETS. For providing QoS guarantee in routing, a novel cross-layer design solution namely CCBF was presented in [45] which uses both MAC and network layer information by using cluster based forwarding mechanisms. It is evident from Figure 6. that proposed a forwarding mechanism in [45] namely cross layered cluster based packet forwarding (CCBF) have less number of dropped packet by MAC and routing layer as compared to 802.11 MAC approach, for two different types of network loads.
Figure 6. Performance comparison of CCBF and 802.11 MAC [45]
[15] Have proposed a distributed prediction based cognitive topology control (PCTC) to provision cognitive routing in adhoc networks while [70] have presented a cross-layer design approach for improving TCP performance in cognitive Radio networks. Another intuitive technique is proposed in [30]
4 which is directed to build low-cost gateways for providing internet connectivity to vehicles passing on the highway. This method uses multi-hop connectivity and ensures that vehicle will be linked to the gateway with a probability greater than a certain threshold. Performance enhancements of proposed approach were corroborated in [30] by performing simulations in MATLAB R2012b and C++ on a real world scenario, comprising of roads around University of waterloo, Canada. Further, Sofia et al. in [39] have proposed a cross-layer based solution for routing in which physical layer information is used to find the remaining time for a link, by using which efficient routing is performed. C.
Safety issues in VANETS. Around 43,000 deaths and more than 1.8 million injuries due to accidents are reported every year in European Union (EU) [54]. For this reason, post-crash car-to-car emergency message broadcasting is mandatory as defined in IEEE 802.11p and Wireless access for vehicular standards (WAVE) standards. The cross-layer simulation model has been developed by Abdullah et al. in [1] to analyze the delays of raptor codes and repetition codes for disseminating post-crash information in case of both single antenna (SISO) and spacetime block-code (STBC) multiple antennas. Simulation results as shown in Figure 7 clearly reveal that, for both low-density and high-density traffic, the delay is less in the case of raptor code.
application layer Kalman filters and Monte-Carlo filters are used to screen the malicious locations and finally various data fusion methods are employed to compute accurate positions. D.
Efficient data dissemination in VANETS. In the case of dangerous situations, when a vehicle receives the warning message then it tries to disseminate the message to following vehicles in real-time so that other drivers can act appropriately. However, broadcasting may result in broadcast storm problem in which broadcasted warning messages by all vehicles result in network congestion, message retransmission scenarios, and traffic collisions etc. This issue was resolved by Aquino et al. in [27] using cross-layer design approach. Authors have proposed HyDi protocol, a novel data dissemination protocol, which work both under wellconnected and intermittently connected networks. In wellconnected networks, suppression mechanism has to be employed by vehicle, for reducing link layer congestion. On the other hand, in the case of intermittently connected networks, HyDi uses to store and carry forward mechanism which means, when a vehicle realizes that there is no vehicle to whom it can send its emergency data, then that vehicle store its emergency data and transfer it when the connection is established. Most of the papers in existing literature used either sender-initiated or receiver-initiated methods. However, HyDi protocol in [27] uses a hybrid technique that leverages both aforementioned methods. Simulations were done using OMnet++ 4.2 simulator by using which performance analysis of proposed protocol HyDi was done in comparison to existing protocols DV-CAST [55] and SRD [56] for dense highways and sparse highways respectively. Figure 8 shows that, when traffic was 1600 vehicles/hour, then delay in case of HyDi protocol is less as compared to DV-CAST and SRD data dissemination protocols.
Figure 7. Delay in Repetition code and Raptor code in STBC [1]
Most of the safety applications in VANETS including collision avoidance, co-operative driving, traffic reports, roadside conditions, and crash reports are based upon the location of the vehicle. Hence, validating location information is an important concern. A novel cross-layer design technique has been proposed in by Weigle et al. [47] for validating location information. The proposed method guarantees location accuracy at physical, network and application layer. At the physical layer, radars are used to ensure if the position estimation is correct or not. Further, the validity of location information is done at network layer by exchanging location information by various vehicles with each other. Finally, at
Figure 8. Emergency message propagation delay of HyDi protocol as compared to DV-CAST and SRD protocol at 1600 vehicles/hour [27]
Other challenges in emergency message broadcast include hidden the terminal problem, the unreliability of link and sending multiple redundant messages [57][36]. Cross-layer broadcast protocol (CLBP) was proposed in [15] for solving
5 aforementioned issues by using multi-hop message dissemination in inter-vehicle systems. Simulations were performed using the NS-2 simulator to compare packet error rate (PER), relay selection delay and emergency message access delay of CLBP with the ad-hoc multi-hop broadcast protocol (AMB). Results show that CLBP used fewer resources and reduces PER, emergency message access delay and relay selection delay as compared to AMB. E. Performing fast handover in VANETS. Most of the established handover techniques use mobile WiMAX networks and are focused on improving scanning latency. Stream Control Transmission Protocol (SCTP) was used in [25] to provide vertical handover between UMTS and WLAN. Another research conducted in [71] proposed a crosslayer handoff design for MIMO enabled WLANs. A Crosslayer based vehicular fast handover scheme (VHFS) for VANETS is presented by Chiu et al. in [10] which combines physical layer and MAC layer information for reducing handover delay. This technique uses topology information which is broadcasted by oncoming small size vehicles (OSV). Proposed Cross-layer model in [10] consist of three types of vehicles namely, Relay vehicles (RV), Broken Vehicle (BV) and Oncoming small size vehicles (OSV), which represents topology considered in the paper as shown in Figure 9. Simulations were performed in NS-2 which showed that handover latency and packet loss in proposed VHFS handover technique was less than WiMAX.
dissemination. Prediction of residual energy absolute error and mean relative error is done using MATLAB simulator. G.
Energy Efficiency in broadcasting When an emergency message is broadcasted, then change in topology from sparse to congest or vice-versa led to performance degradations in transmissions. For solving this issue, [36] have taken dynamically changing topology into account and have designed cross-layer based Transmit power adaption (TPA) algorithm for enabling a vehicle to dynamically adapt QoS parameter (Transmission power) when network load, link conditions, and traffic density are changed. Simulations of TPA were performed using the NS-3 simulator, where beacon messages and event-driven messages were passed among vehicles. Three type of scenarios namely Manhattan grid, random Voronoi map and Tiger map were considered in [36]. Also, evaluation of proposed TPA algorithm was done against default 802.11 mechanism and Rawat algorithm [57] whose results are shown in Figure 10. It is evident from Figure 10. that throughput of proposed TPA algorithm is more than 802.11 and Rawat method.
Figure 10. Performance evaluation in Manhattan Grid [57]
Figure 9. Simulation topology used in [10]
F.
Link Quality selection issue in VANETS Due to the high mobility of vehicles in VANETS, the links are short-lived which makes the traditional ad-hoc protocols inefficient for VANETS. To overcome this issue a cross-layer based approach was presented in [41] which uses time series data of physical layer to pass the useful metrics to upper layers. This technique has also been verified against actual vehicular traces data. Another issue in VANETS is that traditional networks have not used direct coupling of physical layer with network topology. This issue was addressed by Leung et al. in [38] by using cross-layer design, where the residual time of link is computed from power metric at the physical layer. Authors in [40] have also proposed a crosslayer approach for future estimation of link quality and remaining residual time during which effective data transmission can be done. This knowledge is very useful in decision making by routing protocols and emergency message
Authors in [34] have proposed an energy efficient gradientbased algorithm for heterogeneous cognitive radio networks with Femtocells as Stackelberg game. The cross-layer based energy efficient design was introduced in [53] which is based on Opportunistic routing (OR). Another cross-layer approach proposed in [33] uses an algorithm to dynamically adapt transmission power based upon local traffic density. This led to establishing a connection for the longer duration of time. The cross-layer approach was proposed by Zhao et al. in [6] for designing energy-efficient cross layer broadcast protocol (CLBP) for emergency message dissemination. Authors have proposed a relay metric for relay selection, which uses physical layer channel conditions, vehicle speed, and geographical locations into consideration. To further guarantee Quality of service (QoS), IEEE 802.11p enhanced distributed coordination access (EDCA) was used. NS-3 simulation results show that relay selection delay, packet error rate (PER) and emergency message access delay were less in the case of proposed CLBP protocol as compared to ad-hoc multi-hop broadcast (AMB) protocol.
6 H. Congestion control issue in VANETS Congestion detection and avoidance are relatively easy in MANETS as the node mobility is less but in the case of VANETS due to the high speed of vehicles, the MANETS based congestion control algorithms that exist in literature are sub-optimal and thus needs to be improved [32][33][57]. A cross-layer solution for these issues is proposed in [32] which consist of adaptive and distributed congestion detection and congestion control protocol. Congestion-detection phase collects knowledge from various layers of a protocol stack which is then combined and mapped. In order to perform congestion control parameters like transmission rate, contention window and transmit power are used. Simulations performed in OMNET++ simulator using SUMO traffic simulator in VENIS (vehicles in network simulation) environment showed performance improvements by using proposed method. I. Admission control issues in VANETS As services provided by VANETS needs to be transmitted in real-time hence admission control and priority assignment is imperative to provide QoS. Krishnamurthy et al. in [13] have proposed an optimal admission control scheme in CDMA networks named as Markov modulated Poisson Process (MMPP). In [49] authors have used statistics to predict the future mobility of users based upon their historical mobility data. Hence admission control and reservation is guaranteed by using the mobility prediction data. A joint admission control scheme has been proposed in [48] for both WLAN and CDMA networks. The cross-layer solution for VANETS based upon physical, MAC and Network layer has been proposed in [58] to provide admission control and priority control. Simulations were performed in Qualnet 5.0 for evaluation of proposed technique. Simulation results show that proposed techniques reduces end-to-end delay and increases throughput and packet delivery ratio (PDR).
Authors have proposed an Optimal Congestion and Medium Access Control (OC-MAC) algorithm which decomposes a source problem into a flow control problem. Figure 11 shows the simulation results, in which proposed OC-MAC algorithm is compared with three approaches namely conventional client-server algorithm which is using IEEE 802.11 at the MAC layer, non-relay cooperation approach and always relay co-operation approach. It is evident from Figure 11 that throughput of proposed OC-MAC is greater than the conventional approach, no-relay, and always-relay schemes. Another issue is that IEEE 802.11 DCF protocol does not provide support for QoS which results in degraded channel utilization. Solution to this problem was provided by Liu et al. in [24] by using cross-layer based priority scheduling as a result of which timely delivery of priority messages is guaranteed. Proposed cross-layer scheme namely AEFE is compared with existing AEDCF technique using simulation as shown in Figure 12 Simulation results clearly show that proposed AEFE cross-layer scheme has higher percentage gain in throughput as compared to AEDCF scheme. K. Privacy and security concerns in VANETS Privacy requirements in VANETS vary from public safety applications to private applications due to their different demands [2]. Solution to this issue was done in [59] by proposing VANET-OLSR protocol which is aimed to provide privacy by dividing the network into clusters which consist of one cluster-head and many voters. However, VANET-OLSR protocol is vulnerable to blackhole attack in which attacker advertises that, it has a valid route to the destination and then start dropping those packets without informing source node [60]. To solve this issue, most of the papers in literature have adopted watchdog mechanism, which can only detect black hole attacks at the routing level and hence suffers from raising false positives.
J. Issues related to effective scheduling in VANETS Several scheduling techniques have been proposed in the literature for VANETS. As the link capacities are VANETS is not fixed, hence the scheduling is a more complex task and mandates joint optimizations at MAC and physical layers. For addressing these issues, the cross-layer algorithm is proposed in [51] that uses opportunistic cooperation strategy to perform optimal scheduling.
Figure 12. Throughput gain % in AFAE and AEDCF [24]
Figure 11. Throughput comparison in [51]
Authors in [4] have proposed in cross-layer based cooperative intrusion detection system. The simulation was performed in MATLAB 8.0 in conjunction with Mobisim simulator [61] which uses output trace files of MATLAB as input. Figure 13 shows that proposed cross-layer solution have less positive rates in comparison to a solution without cross-layer.
7 using multi-hop communication which causes delay depending upon traffic density, channel noise, and interference. Further, in the case of sparse traffic density, the mobility of vehicles can be considered independent of one another, while in the case of dense traffic density, vehicle’s mobility is dependent upon one other. In [3] a cross-layer approach for providing Quantity of service (QoS) in an urban environment is proposed for video streaming applications. The main objective of this paper is to maximize peak signal to noise ratio (PNSR) of received video as a repercussion of which end-to-end distortion of packets is minimized. Simulation results in Figure 15 show that average PNSR of proposed cross-layer path selection scheme is less than that of upper bound achieved by the analytical model.
Figure 13. MATLAB simulation results for false positive rates [4]
Due to security concerns of public key infrastructure (PKI) Armknecht et al. in [2] have proposed a cross-layer based extension to PKI known as PKI+ as well as source geographical routing which uses non-repudiation and privacy enhancement features. The benefit of using PKI+ is that it let users act autonomously after they receive a master key and master certificate from Certificate authority (CA). PKI+ approach works in five stages as shown in Figure 14. During the first stage, CA sets up the PKI+ by generating a secret key and publishing the public key. During the second stage, the user uses submits its private key to CA and gets master certificate and master key. Then during the third stage, the user can create its own pseudonym by using its master key, master certificate, and CA’s public key. As shown in Figure 14, in fourth stage CA uses its tracking data services to find out the owner of a pseudonym. Finally, at last stage, CA uses revocation data services to revoke the keys from the user.
The problem of video streaming in the context of VANETS is least addressed in existing literature. Also, this issue is different from broadcasting and multicasting issues. In [42], Soldo et al. have provided a fully distributed solution known as SMUG (streaming media urban grid) for providing video streaming support in VANETS. Their solution is based upon inter-vehicle communication which is used for distributing multi-media content from roadside access points to vehicles in VANETS. Performance analysis of SMUG was done using NS-2 simulator under urban data propagation conditions. The performance of SMUG was compared against theoretical results for broadcast capacity obtained in [63], which provides an upper bound of maximum achievable throughput.
Figure 15. Average PNSR versus number of nodes [3]
M. Reducing end-to-end packet delays with context adaptation
Figure 14. Operational stages of PKI+ [2]
L. Providing QoS in video streaming in VANETS Roadside units (RSU) receive video data using wired networks and then forwards these packets to the destination
Designing MAC protocols and routing protocols in a way that they not only provide less end-to-end delays but also adapt to changing topology and mobility is a challenging issue in VANETS. Authors in [17] have proposed a cross-layer based framework for location aware and delay aware communication protocol (LD-CROP) in VANETS for delivering packets over low rate paths to base stations. Simulations were performed in NS-2 which showed that proposed protocol namely LD-CROP is able to predict delays accurately and get good performance in terms of end-to-end
8 delay, fairness and success rates when compared to VADD [64] and dynamic source routing (DSR). IV.
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OPEN CROSS-LAYER DESIGN ISSUES IN VANETS
A. Cross-layer design Standardization in VANETS. Protocols like IPv4, IPv6, and IEEE 802.11ac are not suitable for VANETS as the vehicle mobility is faster and topology changes are frequent. So, by providing cross-layer solution solved the original issue, but let to another interoperability issue as the cross-layer designs presented by various papers were application specific [65][17]. B. Realistic mobility models for VANETS. Most of the mobility models present in literature do not truly represent real-world mobility scenarios in VANETS as mobility models are reliant on road conditions, traffic congestion, specific time of day, event-based traffic and location-based [49][65][61]. The problem to represent realistic mobility models for VANETS is difficult because it is based on one or more aforementioned scenarios. C. Balancing modularity and cross-layer design in VANETS. As we know that, layered based approach provides modularity and security as interfaces are clear-cut, but in the case of cross-layer design, in order to facilitate fine grain information exchange the interfaces between the layers become fuzzy [65]. As in vehicular networks, the message dissemination is done in a multi-hop manner and hence collaboration of vehicles is obligatory. As as result of crosslayer design having less modularity, data transfer among vehicles may lead to potential security concerns.
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V. CONCLUSION This paper is aimed at discovering numerous issues in VANETS and then analyzing their cross-layer based solutions provided in various references papers. Various issues have been cataloged into 13 different sub-categories like admission control, congestion control, message dissemination and security etc. Further, a high-level description of the contribution of the particular reference paper, author’s approach to solving the issue along with simulation results have been briefly discussed. From my study, I have concluded that among various cross-layer design issues in VANETS, extensively researched concerns are related to routing issues, emergency data dissemination and energy efficiency in transmission. Nevertheless, other issues like privacy, message security, and passenger safety concerns are also getting research attention nowadays. In addition to these, there are some open issues related to VANETS like providing system stability, standardizing cross-layer design and providing realistic mobility modeling.
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Jasmeet Singh was born in India, in 1993. He received his Bachelors of Technology in Computer Science and Engineering from Guru Gobind Singh Indraprastha University, New Delhi, India in 2011. He is currently pursuing Master of Applied Science in Electrical and Computer Engineering from Carleton University, Ottawa, Canada. He is working on his research under the guidance of Professor Dr. Marc St. Hilaire at Carleton University. Jasmeet Singh also a graduate student member of IEEE. Singh’s research focus is on Cloud Computing and Network Security.
