A comprehensive survey on congestion control techniques and the research challenges on VANET

The nature of vehicular mobility and high speed of vehicular ad hoc network (VANET) with dynamic change in the network topology let the vehicular remain as one of the most challenging problems in vehicular-to-vehicular (V2V) communications. Information dissemination is the major problem in VANET with a fixed bandwidth which is causing congestion on the resources, such as channels and affects the performance of the important application, especially when the emergency or secure transmission of messages is exchanged between the vehicles-to-vehicles communication. To mitigate these problems and introduce a safe vehicular environment in urban and highway, congestion detection and control has been considered and with various strategies and techniques which is take the attention of researchers in VANET. In our survey we mentioned recent techniques and approaches which is used in congestion detection and control and applied different matrices and parameters which is used to evaluate these approaches. In addition, the study also explained the limitation and problems that face the current congestion detection and control schemes, finally we present various solution approach and future expectations in vehicular communication

Evaluation of efficient vehicular ad hoc networks based on a maximum distance routing algorithm

Traffic management at road intersections is a complex requirement that has been an important topic of research and discussion. Solutions have been primarily focused on using vehicular ad hoc networks (VANETs). Key issues in VANETs are high mobility, restriction of road setup, frequent topology variations, failed network links, and timely communication of data, which make the routing of packets to a particular destination problematic. To address these issues, a new dependable routing algorithm is proposed, which utilizes a wireless communication system between vehicles in urban vehicular networks. This routing is position-based, known as the maximum distance on-demand routing algorithm (MDORA). It aims to find an optimal route on a hop-by-hop basis based on the maximum distance toward the destination from the sender and sufficient communication lifetime, which guarantee the completion of the data transmission process. Moreover, communication overhead is minimized by finding the next hop and forwarding the packet directly to it without the need to discover the whole route first. A comparison is performed between MDORA and ad hoc on-demand distance vector (AODV) protocol in terms of throughput, packet delivery ratio, delay, and communication overhead. The outcome of the proposed algorithm is better than that of AODV

Survey on Congestion Detection and Control in Connected Vehicles

The dynamic nature of vehicular ad hoc network (VANET) induced by frequent topology changes and node mobility, imposes critical challenges for vehicular communications. Aggravated by the high volume of information dissemination among vehicles over limited bandwidth, the topological dynamics of VANET causes congestion in the communication channel, which is the primary cause of problems such as message drop, delay, and degraded quality of service. To mitigate these problems, congestion detection, and control techniques are needed to be incorporated in a vehicular network. Congestion control approaches can be either open-loop or closed loop based on pre-congestion or post congestion strategies. We present a general architecture of vehicular communication in urban and highway environment as well as a state-of-the-art survey of recent congestion detection and control techniques. We also identify the drawbacks of existing approaches and classify them according to different hierarchical schemes. Through an extensive literature review, we recommend solution approaches and future directions for handling congestion in vehicular communications

A Hybrid (Active-Passive) VANET Clustering Technique

Clustering serves a vital role in the operation of Vehicular Ad hoc Networks (VANETs) by continually grouping highly mobile vehicles into logical hierarchical structures. These moving clusters support Intelligent Transport Systems (ITS) applications and message routing by establishing a more stable global topology. Clustering increases scalability of the VANET by eliminating broadcast storms caused by packet flooding and facilitate multi-channel operation. Clustering techniques are partitioned in research into two categories: active and passive. Active techniques rely on periodic beacon messages from all vehicles containing location, velocity, and direction information. However, in areas of high vehicle density, congestion may occur on the long-range channel used for beacon messages limiting the scale of the VANET. Passive techniques use embedded information in the packet headers of existing traffic to perform clustering. In this method, vehicles not transmitting traffic may cause cluster heads to contain stale and malformed clusters. This dissertation presents a hybrid active/passive clustering technique, where the passive technique is used as a congestion control strategy for areas where congestion is detected in the network. In this case, cluster members halt their periodic beacon messages and utilize embedded position information in the header to update the cluster head of their position. This work demonstrated through simulation that the hybrid technique reduced/eliminated the delays caused by congestion in the modified Distributed Coordination Function (DCF) process, thus increasing the scalability of VANETs in urban environments. Packet loss and delays caused by the hidden terminal problem was limited to distant, non-clustered vehicles. This dissertation report presents a literature review, methodology, results, analysis, and conclusion

Congestion Control in Vehicular Ad Hoc Networks

RÉSUMÉ Les réseaux Véhiculaires ad hoc (VANets) sont conçus pour permettre des communications sans fil fiables entre les nœuds mobiles à haute vitesse. Afin d'améliorer la performance des applications dans ce type de réseaux et garantir un environnement sûr et confortable pour ses utilisateurs, la Qualité de Service (QoS) doit être supportée dans ces réseaux. Le délai ainsi que les pertes de paquets sont deux principaux indicateurs de QoS qui augmentent de manière significative en raison de la congestion dans les réseaux. En effet, la congestion du réseau entraîne une saturation des canaux ainsi qu’une augmentation des collisions de paquets dans les canaux. Par conséquent, elle doit être contrôlée pour réduire les pertes de paquets ainsi que le délai, et améliorer les performances des réseaux véhiculaires. Le contrôle de congestion dans les réseaux VANets est une tâche difficile en raison des caractéristiques spécifiques des VANets, telles que la grande mobilité des nœuds à haute vitesse, le taux élevé de changement de topologie, etc. Le contrôle de congestion dans les réseaux VANets peut être effectué en ayant recours à une stratégie qui utilise l'un des paramètres suivants : le taux de transmission, la puissance de transmission, la priorisation et l’ordonnancement, ainsi que les stratégies hybrides. Les stratégies de contrôle de congestion dans les réseaux VANets doivent faire face à quelques défis tels que l'utilisation inéquitable des ressources, la surcharge de communication, le délai de transmission élevé, et l'utilisation inefficace de la bande passante, etc. Par conséquent, il est nécessaire de développer de nouvelles approches pour faire face à ces défis et améliorer la performance des réseaux VANets. Dans cette thèse, dans un premier temps, une stratégie de contrôle de congestion en boucle fermée est développée. Cette stratégie est une méthode de contrôle de congestion dynamique et distribuée qui détecte la congestion en mesurant le niveau d'utilisation du canal. Ensuite, la congestion est contrôlée en ajustant la portée et le taux de transmission qui ont un impact considérable sur la saturation du canal. Ajuster la portée et le taux de transmission au sein des VANets est un problème NP-difficile en raison de la grande complexité de la détermination des valeurs appropriées pour ces paramètres. Considérant les avantages de la méthode de recherche Tabou et son adaptabilité au problème, une méthode de recherche multi-objective est utilisée pour trouver une portée et un taux de transmission dans un délai raisonnable. Le délai et la gigue, fonctions multi-objectifs de l'algorithme Tabou, sont minimisés dans l'algorithme proposé. Par la suite, deux stratégies de contrôle de congestion en boucle ouverte sont proposées afin de réduire la congestion dans les canaux en utilisant la priorisation et l'ordonnancement des messages. Ces stratégies définissent la priorité pour chaque message en considérant son type de contenu (par exemple les messages d'urgence, de beacon, et de service), la taille des messages, et l’état du réseau (par exemple, les métriques de la vélocité, la direction, l'utilité, la distance, et la validité). L'ordonnancement des messages est effectué sur la base des priorités définies. De plus, comme seconde technique d'ordonnancement, une méthode de recherche Tabou est employée pour planifier les files d'attente de contrôle et de service des canaux de transmission dans un délai raisonnable. A cet effet, le délai et la gigue lors de l'acheminement des messages sont minimisés. Enfin, une stratégie localisée et centralisée qui utilise les ensembles RSU fixés aux intersections pour détecter et contrôler de la congestion est proposée. Cette stratégie regroupe tous les messages transférés entre les véhicules qui se sont arrêtés à une lumière de signalisation en utilisant les algorithmes de Machine Learning. Dans cette stratégie, un algorithme de k-means est utilisé pour regrouper les messages en fonction de leurs caractéristiques (par exemple la taille des messages, la validité des messages, et le type de messages, etc.). Les paramètres de communication, y compris le portée et le taux de transmission, la taille de la fenêtre de contention, et le paramètre AIFS (Arbitration Inter-Frame Spacing) sont déterminés pour chaque grappe de messages en vue de minimiser le délai de livraison. Ensuite, les paramètres de communication déterminés sont envoyés aux véhicules par les RSUs, et les véhicules opèrent en fonction de ces paramètres pour le transfert des messages. Les performances des trois stratégies proposées ont été évaluées en simulant des scénarios dans les autoroutes et la circulation urbaine avec les simulateurs NS2 et SUMO. Des comparaisons ont aussi été faites entre les résultats obtenus à partir des stratégies proposées et les stratégies de contrôle de congestion communément utilisées. Les résultats révèlent qu’avec les stratégies de contrôle de congestion proposées, le débit du réseau augmente et le taux de perte de paquets ainsi que de délai diminuent de manière significative en comparaison aux autres stratégies. Par conséquent, l'application des méthodes proposées aide à améliorer la performance, la sureté et la fiabilité des VANets.----------ABSTRACT Vehicular Ad hoc Networks (VANets) are designed to provide reliable wireless communications between high-speed mobile nodes. In order to improve the performance of VANets’ applications, and make a safe and comfort environment for VANets’ users, Quality of Service (QoS) should be supported in these networks. The delay and packet losses are two main indicators of QoS that dramatically increase due to the congestion occurrence in the networks. Indeed, due to congestion occurrence, the channels are saturated and the packet collisions increase in the channels. Therefore, the congestion should be controlled to decrease the packet losses and delay, and to increase the performance of VANets. Congestion control in VANets is a challenging task due to the specific characteristics of VANets such as high mobility of the nodes with high speed, and high rate of topology changes, and so on. Congestion control in VANets can be carried out using the strategies that can be classified into rate-based, power-based, CSMA/CA-based, prioritizing and scheduling-based, and hybrid strategies. The congestion control strategies in VANets face to some challenges such as unfair resources usage, communication overhead, high transmission delay, and inefficient bandwidth utilization, and so on. Therefore, it is required to develop new strategies to cope with these challenges and improve the performance of VANets. In this dissertation, first, a closed-loop congestion control strategy is developed. This strategy is a dynamic and distributed congestion control strategy that detects the congestion by measuring the channel usage level. Then, the congestion is controlled by tuning the transmission range and rate that considerably impact on the channel saturation. Tuning the transmission range and rate in VANets is an NP-hard problem due to the high complexity of determining the proper values for these parameters in vehicular networks. Considering the benefits of Tabu search algorithm and its adaptability with the problem, a multi-objective Tabu search algorithm is used for tuning transmission range and rate in reasonable time. In the proposed algorithm, the delay and jitter are minimized as the objective functions of multi-objective Tabu Search algorithm. Second, two open-loop congestion control strategies are proposed that prevent the congestion occurrence in the channels using the prioritizing and scheduling the messages. These strategies define the priority for each message by considering the content of messages (i.e. types of the messages for example emergency, beacon, and service messages), size of messages, and state of the networks (e.g. velocity, direction, usefulness, distance and validity metrics). The scheduling of the messages is conducted based on the defined priorities. In addition, as the second scheduling technique, a Tabu Search algorithm is employed to schedule the control and service channel queues in a reasonable time. For this purpose, the delay and jitter of messages delivery are minimized. Finally, a localized and centralized strategy is proposed that uses RSUs set at intersections for detecting and controlling the congestion. These strategy clusters all the messages that transferred between the vehicles stopped before the red traffic light using Machine Learning algorithms. In this strategy, a K-means learning algorithm is used for clustering the messages based on their features (e.g. size of messages, validity of messages, and type of messages, and so on). The communication parameters including the transmission range and rate, contention window size, and Arbitration Inter-Frame Spacing (AIFS) are determined for each messages cluter based on the minimized delivery delay. Then, the determined communication parameters are sent to the vehicles by RSUs, and the vehicles operate based on these parameters for transferring the messages. The performances of three proposed strategies were evaluated by simulating the highway and urban scenarios in NS2 and SUMO simulators. Comparisons were also made between the results obtained from the proposed strategies and the common used congestion control strategies. The results reveal that using the proposed congestion control strategies, the throughput, packet loss ratio and delay are significantly improved as compared to the other strategies. Therefore, applications of the proposed strategies help improve the performance, safety, and reliability of VANets

Performance analysis of V2V dynamic anchor position-based routing protocols

Recently, vehicular ad hoc networks (VANETs) have received more attention in both academic and industry settings. One of the challenging issues in this domain is routing protocols. VANETs unique characteristics such as high mobility with the constraint of road topology, fast network topology changes, frequently disconnected networks, and time-sensitive data exchange makes it difficult to design an efficient routing protocol for routing data in vehicle-to-vehicle (V2V) and vehicle-toinfrastructure communications. Designing routing protocols for V2V commutations are more challenging due to the absence of infrastructure nodes in the communication procedure. They become even more challenging, when they get benefit from dynamic anchor computation method in which the anchor nodes (junctions or basic nodes for routing) are dynamic in their routing procedure. Positionbased routing protocols have been proven to be superior and outperform the other protocols since there is no requirement to establish and save a route between source and destination during the routing process which is suitable for dynamic nature of vehicular networks. In this paper, the performance of V2V dynamic anchor position-based routing protocols, which are proposed for the most challenging condition of packet routing in VANET, are investigated and evaluated under two different scenarios (i.e. various vehicle densities and velocities) through NS-2. The obtained results are then illustrated based on average delay, packet delivery ratio and routing overhead as routing performance indicators. Our objective is to provide a quantitative assessment of the applicability of these protocols in different vehicular scenarios. The comparison provided in this paper makes the research contribution of this survey paper quite higher than a regular survey paper only with explanations.This research is supported by UM High Impact Research MoE Grant UM.C/625/1/HIR/MOHE/FCSIT/09 from the Ministry of Education Malaysia.Jabbarpour, MR.; Marefat, A.; Jalooli, A.; Noor, RM.; Khokhar, RH.; Lloret, J. (2015). Performance analysis of V2V dynamic anchor position-based routing protocols. Wireless Networks. 21(3):911-929. doi:10.1007/s11276-014-0825-8S911929213Jalooli, A., Shaghaghi, E., Jabbarpour, M.R., Md Noor, R., Yeo, H., & Jung, J.J. (2014). Intelligent advisory speed limit dedication in highway using VANET. The Scientific World Journal, 2014, 629412.Jalooli, A., Hussin, N., Noor, R.M, & Jung, J.J. (2014). Public alerts on landslide natural disaster using vehicular communications. International Journal of Distributed Sensor Networks. doi: 10.1155/2014/969864 .Vasilakos, A.V. (2008). Special issue: Ambient Intelligence. Information Sciences, 178(3), 585–587.Sattari, M. R. J., Noor, R. M., & Ghahremani, S. (2013). Dynamic congestion control algorithm for vehicular ad hoc networks. International Journal of Software Engineering and Its Applications, 7(3), 95–108.Cheng, X., Wang, C.-X., Laurenson, D. I., Salous, S., & Vasilakos, A. V. (2009). An adaptive geometry-based stochastic model for non-isotropic MIMO mobile-to-mobile channels. IEEE Transactions on Wireless Communications, 8(9), 4824–4835.Li, M., Li, Z., & Vasilakos, A.V. (2013). A survey on topology control in wireless sensor networks: Taxonomy, comparative study, and open issues. Proceedings of the IEEE, 101(12), 2538–2557.Cheng, X., Wang, C.X., Laurenson, D.I., Salous, S., Vasilakos, A.V. (2011). New deterministic and stochastic simulation models for non isotropic scattering mobile to mobile Rayleigh fading channels. Wireless Communications and Mobile Computing, 11(7), 829–842.Jiang, D., & Delgrossi, L. (2008). IEEE 802.11 p: Towards an international standard for wireless access in vehicular environments. In IEEE Vehicular Technology Conference (pp. 2036–2040). VTC Spring 2008.Lloret, J., Canovas, A., Catalá, A., & Garcia, M. (2013). Group-based protocol and mobility model for VANETs to offer internet access. Journal of Network and Computer Applications, 36(3), 1027–1038.Whaiduzzaman, M., Sookhak, M., Gani, A., Buyya, R. (2014). A survey on vehicular cloud computing. Journal of Network and Computer Applications, 40, 325–344.Bilal, S. M., Khan, S. U., Madani, S. A., Nazir, B., Othman, M. (2014). Road oriented traffic information system for vehicular ad hoc networks. Wireless Personal Communications, 77(4), 2497–2515.Cho, K.-H., & Ryu, M.-W. (2012). A survey of greedy routing protocols for vehicular ad hoc networks. Smart CR, 2(2), 125–137.Sharef, B. T., Alsaqour, R. A., & Ismail, M. (2014). Vehicular communication ad hoc routing protocols: A survey. Journal of Network and Computer Applications, 40, 363–396.Ghafoor, K. Z., Mohammed, M. A., Lloret, J., Bakar, K. A., & Zainuddin, Z. M. (2013). Routing protocols in vehicular ad hoc networks: Survey and research challenges. Network Protocols and Algorithms, 5(4), 39–83.Perkins, C.E., & Royer, E.M. (1999). Ad hoc on-demand distance vector routing. In Proceeding of Second IEEE Workshop on Mobile computing systems and applications, WMCSA’99 (pp. 90–100).Johnson, D.B., & Maltz, D.A. (1996). Dynamic source routing in ad hoc wireless networks. In Mobile computing (pp. 153–181). Heidelberg: Springer.Liu, G., Lee, B-S., Seet, B-C., Foh, C-H., Wong, K-J., & Lee, K-K. (2004). A routing strategy for metropolis vehicular communications. In Information networking. networking technologies for broadband and mobile networks (Lecture notes in computer science, pp. 134–143, Vol. 3090). Berlin, Heidelberg: Springer.Füßler, H., Mauve, M., Hartenstein, H., Käsemann, M., & Vollmer, D. (2003). Mobicom poster: Location-based routing for vehicular ad-hoc networks. ACM SIGMOBILE Mobile Computing and Communications Review, 7(1), 47–49.Hui, F. (2005). A survey on the characterization of Vehicular Ad Hoc Networks routing solutions. In ECS (pp. 1–15).Jabbarpour, M. R., Md Noor, R., Khokhar, R. H., & Ke, C.-H. (2014). Cross-layer congestion control model for urban vehicular environments. Journal of Network and Computer Applications, 44, 1–16.Khokhar, R.H., Zia, T., Ghafoor, K.Z., Lloret, J., Shiraz, M. (2013). Realistic and Efficient Radio Propagation Model for V2X Communications. KSII Transactions on Internet & Information Systems, 7(8). doi: 10.1007/978-3-319-04283-1 .Fonseca, A., & Vazão, T. (2013). Applicability of position-based routing for VANET in highways and urban environment. Journal of Network and Computer Applications, 36(3), 961–973.Nikumbh, M. D., & Bhoi, M. A. (2013). A survey of positioned based routing protocol in VANET. International Journal of Modern Engineering Research (IJMER), 3(2), 1015–1018.da Silva Camões, A. R. (2013). Geographic location and routing in vehicular networks. Master, tecnicolisboa.Raw, R. S., & Das, S. (2011). Performance comparison of Position based routing Protocols in vehicle-to-vehicle (V2V) Communication. International Journal of Engineering Science and Technology, 3(1), 435–444.Hassan, A. N., Abdullah, A. H., Sheet, D. K., & Qureshi, K. N. (2014). Comparison of position based routing protocols of vehicular AD HOC network. World Applied Sciences Journal, 31(3), 341–345.He, G. (2002). Destination-sequenced distance vector (DSDV) protocol. Networking Laboratory: Helsinki University of Technology.Karp, B., & Kung, H-T. (2000). GPSR: Greedy perimeter stateless routing for wireless networks. In Proceedings of the 6th annual international conference on Mobile computing and networking (pp. 243–254). ACM.Gerls, M. (2002). Fisheye State Routing (FSR) for Ad Hoc Networks. Internet Draft, draft-ietf-manet-fsr-03 txt.Clausen, T., Jacquet, P., Adjih, C., Laouiti, A., Minet, P., Muhlethaler, P., Qayyum, A., & Viennot, L. (2003). Optimized link state routing protocol (OLSR).Attar, A., Tang, H., Vasilakos, A. V., Yu, F. R., & Leung, V. C. (2012). A survey of security challenges in cognitive radio networks: solutions and future research directions. Proceedings of the IEEE, 100(12), 3172–3186.Park, V., & Corson, M.S. (1997). Temporally-ordered routing algorithm (TORA) version 1 functional specification. Internet-Draft, draft-ietf-manet-tora-spec-00. Txt.Namboodiri, V., Agarwal, M., & Gao, L. (2004). A study on the feasibility of mobile gateways for vehicular ad-hoc networks. In Proceedings of the 1st ACM International Workshop on Vehicular ad hoc networks (pp. 66–75). ACM.Patel, V. J., & Anuradha, P. G. (2012). A review on routing overhead in broadcast based protocol on VANET. International Journal of Engineering and Innovative Technology (IJEIT), 2(5), 109–113.Beijar, N. (2002). Zone routing protocol (ZRP). Finland: Networking Laboratory, Helsinki University of Technology.Nikaein, N., Bonnet, C., & Nikaein, N. (2001). Harp-hybrid ad hoc routing protocol. In Proceedings of International Symposium on Telecommunications (IST) (pp 56–67).Mauve, M., Widmer, J., & Hartenstein, H. (2001). A survey on position-based routing in mobile ad hoc networks. IEEE Network, 15(6), 30–39.Krishna, P., Vaidya, N. H., Chatterjee, M., & Pradhan, D. K. (1997). A cluster-based approach for routing in dynamic networks. ACM SIGCOMM Computer Communication Review, 27(2), 49–64.Song, T., Xia, W., Song, T., Shen, L. (2010). A cluster-based directional routing protocol in VANET. In 12th IEEE International Conference on Communication Technology (ICCT) (pp. 1172–1175).Zeng, Y., Xiang, K., Li, D., & Vasilakos, A. V. (2013). Directional routing and scheduling for green vehicular delay tolerant networks. Wireless Networks, 19(2), 161–173.Spyropoulos, T., Rais, R. N., Turletti, T., Obraczka, K., & Vasilakos, A. (2010). Routing for disruption tolerant networks: Taxonomy and design. Wireless Networks, 16(8), 2349–2370.Vasilakos, A. V., Zhang, Y., & Spyropoulos, T. (2012). Delay tolerant networks: Protocols and applications. Boca Raton: CRC Press.Aquino, R., & Edwards, A. (2006). A reactive location routing algorithm with cluster-based flooding for inter-vehicle communication. Computación y Sistemas, 9(4), 297–313.Wang, T., & Wang, G. (2010). TIBCRPH: traffic infrastructure based cluster routing protocol with handoff in VANET. In 19th Annual IEEE Wireless and Optical Communications Conference (WOCC) (pp. 1–5).Kihl, M., Sichitiu, M., & Joshi, H.P. (2008). Design and evaluation of two geocast protocols for vehicular ad-hoc networks. Journal of Internet Engineering, 2(1), 127–135.Allal, S., & Boudjit, S. (2012). Geocast routing protocols for vanets: Survey and guidelines. In Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS) (pp. 323–328).Ghafoor, K. Z., Abu Bakar, K., Lloret, J., Khokhar, R. H., & Lee, K. C. (2013). Intelligent beaconless geographical forwarding for urban vehicular environments. Wireless Networks, 19(3), 345–362.Ibrahim, K., Weigle, M.C., & Abuelela, M. (2009). p-IVG: Probabilistic inter-vehicle geocast for dense vehicular networks. In IEEE 69th Vehicular Technology Conference. VTC Spring 2009. (pp 1–5).Park, S., Lee, E., Park, H., Lee, H., & Kim, S.-H. (2010). Mobile geocasting to support mobile sink groups in wireless sensor networks. IEEE Communications Letters, 14(10), 939–941.Chaurasia, N., Sharma, S., & Soni, D. (2011). Review study of routing protocols and versatile challenges of MANET. International Journal, 1(2), 150–157.Kihl, M., Sichitiu, M., Ekeroth, T., & Rozenberg, M. (2007) Reliable geographical multicast routing in vehicular ad-hoc networks. In Wired/wireless internet communications (Lecture notes in computer science, pp. 315–325, Vol. 4517). Berlin, Heidelberg: Springer.Chen, Y.-S., Lin, Y.-W., & Lee, S.-L. (2010). A mobicast routing protocol in vehicular ad-hoc networks. Mobile Networks and Applications, 15(1), 20–35.Junhai, L., Liu, X., & Danxia, Y. (2008). Research on multicast routing protocols for mobile ad-hoc networks. Computer Networks, 52(5), 988–997.Li, P., Guo, S., Yu, S., & Vasilakos, A.V. (2012). CodePipe: An opportunistic feeding and routing protocol for reliable multicast with pipelined network coding. In INFOCOM, 2012 Proceedings IEEE (pp. 100–108).Yen, Y.-S., Chao, H.-C., Chang, R.-S., & Vasilakos, A. (2011). Flooding-limited and multi-constrained QoS multicast routing based on the genetic algorithm for MANETs. Mathematical and Computer Modelling, 53(11), 2238–2250.Royer, E.M. (2000). Multicast ad hoc on-demand distance vector (MAODV) routing. IETF Internet Draft, draft-ietf-manet-maodv-00 txt.Jetcheva, J.G., & Johnson, D.B. (2001). Adaptive demand-driven multicast routing in multi-hop wireless ad hoc networks. In Proceedings of the 2nd ACM International Symposium on Mobile ad hoc networking & computing (pp. 33–44). ACM.Patel, A., Latifi, M., Souza, A.B., Xavier, F.A., Celestino, J., & Oliveira, F.D. (2013). Stable multicast trees based on Ant Colony optimization for vehicular Ad Hoc networks. In Proceedings of the 2013 International Conference on Information Networking (ICOIN), IEEE Computer Society (pp. 101–106).Laouiti, A., Jacquet, P., Minet, P., Viennot, L., Clausen, T., & Adjih, C. (2003) Multicast optimized link state routing. INRIA research report RR–4721.Lee, S.-J., Su, W., & Gerla, M. (2002). On-demand multicast routing protocol in multihop wireless mobile networks. Mobile Networks and Applications, 7(6), 441–453.Tian, K., Zhang, B., Mouftah, H., Zhao, Z., & Ma, J. (2009). Destination-driven on-demand multicast routing protocol for wireless ad hoc networks. In ICC’09 IEEE International Conference on Communications, 2009 (pp. 1–5).Tonguz, O. K., Wisitpongphan, N., & Bai, F. (2010). DV-CAST: A distributed vehicular broadcast protocol for vehicular ad hoc networks. IEEE Wireless Communications, 17(2), 47–57.Nekovee, M., & Bogason, B.B. (2007). Reliable and effcient information dissemination in intermittently connected vehicular adhoc networks. In IEEE 65th Vehicular Technology Conference (pp. 2486–2490). VTC2007-Spring.Maia, G., Aquino, A.L., Viana, A., Boukerche, A., & Loureiro, A.A. (2012). HyDi: A hybrid data dissemination protocol for highway scenarios in vehicular ad hoc networks. In Proceedings of the second ACM international symposium on Design and analysis of intelligent vehicular networks and applications (pp. 115–122). ACM.Nakorn, N.N., & Rojviboonchai, K. (2010). DECA: Density-aware reliable broadcasting in vehicular ad hoc networks. In 2010 International Conference on Electrical Engineering/Electronics Computer Telecommunications and Information Technology (ECTI-CON) (pp. 598–602).Zhou, L., Chao, H.-C., & Vasilakos, A. V. (2011). Joint forensics-scheduling strategy for delay-sensitive multimedia applications over heterogeneous networks. Selected Areas in Communications, IEEE Journal on, 29(7), 1358–1367.Yao, Y., Cao, Q., & Vasilakos, A.V. (2013). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for wireless sensor networks. In IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS), 2013 (pp. 182–190).Lochert, C., Hartenstein, H., Tian, J., Fussler, H., Hermann, D., & Mauve, M. (2003). A routing strategy for vehicular ad hoc networks in city environments. In Proceedings of the IEEE Intelligent Vehicles Symposium, 2003 (pp. 156–161).Käsemann, M., Füßler, H., Hartenstein, H., & Mauve, M. (2002). A reactive location service for mobile ad hoc networks. Citeseer.Chen, J-C. (2003). Dijkstra’s shortest path algorithm. Journal of Formalized Mathematics 15.Seet, B-C., Liu, G., Lee, B-S., Foh, C-H., Wong, K-J., Lee, K-K. (2004). A-STAR: A mobile ad hoc routing strategy for metropolis vehicular communications. In NETWORKING 2004. Networking technologies, services, and protocols; performance of computer and communication networks; mobile and wireless communications (pp. 989–999). New York: Springer.Gong, J., Xu, C-Z., & Holle, J. (2007). Predictive directional greedy routing in vehicular ad hoc networks. In IEEE 27th International Conference on Distributed Computing Systems Workshops, 2007. ICDCSW’07 (pp. 2–2).Ding, Y., Wang, C., & Xiao, L. (2007). A static-node assisted adaptive routing protocol in vehicular networks. In Proceedings of the fourth ACM international workshop on Vehicular ad hoc networks. ACM (pp. 59–68).Borsetti, D., & Gozalvez, J. (2010) Infrastructure-assisted geo-routing for cooperative vehicular networks. In IEEE Vehicular Networking Conference (VNC) (pp. 255–262).Cianfrani, A., Eramo, V., Listanti, M., Polverini, M., & Vasilakos, A. V. (2012). An OSPF-integrated routing strategy for QoS-aware energy saving in IP backbone networks. IEEE Transactions on Network and Service Management, 9(3), 254–267.Luo, J., Gu, X., Zhao, T., & Yan, W. (2010). A mobile infrastructure based VANET routing protocol in the urban environment. In IEEE 2010 International Conference on Communications and Mobile Computing (CMC) (pp. 432–437).Xiang, L., Luo, J., & Vasilakos, A. (2011). Compressed data aggregation for energy efficient wireless sensor networks. In 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON) (pp. 46–54).Cheng, H., Xiong, N., Vasilakos, A. V., Tianruo Yang, L., Chen, G., & Zhuang, X. (2012). Nodes organization for channel assignment with topology preservation in multi-radio wireless mesh networks. Ad Hoc Networks, 10(5), 760–773.Pan, H-Y., Jan, R-H., Jeng, A.A-K., Chen, C., & Tseng, H-R. (2011) Mobile gateway routing for vehicular networks. In Proceedings of the 8th IEEE Asia Pacific wireless communication symposium (APWCS 2011).Granelli, F., Boato, G., & Kliazovich, D. (2006). MORA: A movement-based routing algorithm for vehicle ad hoc networks. In IEEE Workshop on Automotive Networking and Applications (AutoNet 2006), San Francisco, USA.Menouar, H., Lenardi, M., & Filali, F. (2007). Movement prediction-based routing (MOPR) concept for position-based routing in vehicular networks. In 66th IEEE Vehicular Technology Conference, VTC-2007 Fall 2007 (pp. 2101–2105).Menouar, H., Lenardi, M., & Filali, F. (2006). An intelligent movement-based routing for VANETs. In ITS world congress, London.Menouar, M., Lenardi, M., & Filali, F. (2005). A movement prediction based routing protocol for vehicle-to-vehicle communications. Communications, 21, 07–2005.Wei, G., Ling, Y., Guo, B., Xiao, B., Vasilakos, A.V. (2011) Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman filter. Computer Communications, 34(6), 793–802.Luo, Y., Zhang, W., & Hu, Y. (2010). A new cluster based routing protocol for VANET. In IEEE Second International Conference on Networks Security Wireless Communications and Trusted Computing (NSWCTC) (pp. 176–180).Raw, R.S., & Lobiyal, D. (2010). B-MFR routing protocol for vehicular ad hoc networks. In IEEE International Conference on Networking and Information Technology (ICNIT) (pp. 420–423).Stojmenovic, I., Ruhil, A. P., & Lobiyal, D. (2006). Voronoi diagram and convex hull based geocasting and routing in wireless networks. Wireless communications and mobile computing, 6(2), 247–258.Prasanth, K., Duraiswamy, K., Jayasudha, K., & Chandrasekar, C. (2009). Edge node based greedy routing for VANET with constant bit rate packet transmission. International Journal of Recent Trends in Engineering, 2(4), 14–19.Wang, X., Vasilakos, A. V., Chen, M., Liu, Y., & Kwon, T. T. (2012). A survey of green mobile networks: Opportunities and challenges. Mobile Networks and Applications, 17(1), 4–20.Brahmi, N., Boussedjra, M., Mouzna, J., & Bayart, M. (2009). Adaptative movement aware routing for vehicular ad hoc networks. In Proceedings of the International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly (pp. 1310–1315). New York: ACM.Haklay, M., & Weber, P. (2008). Openstreetmap: User-generated street maps. IEEE Pervasive Computing, 7(4), 12–18.Behrisch, M., Bieker, L., Erdmann, J., & Krajzewicz, D. (2011). Sumo-simulation of urban mobility-an overview. In SIMUL 2011, The Third International Conference on Advances in System Simulation (pp. 55–60).Baumgart, I., Heep, B., & Krause, S. (2007). OverSim: A flexible overlay network simulation framework. In IEEE Global Internet Symposium (pp. 79–84).Martinez, F. J., Toh, C.-K., Cano, J.-C., Calafate, C. T., & Manzoni, P. (2011). A street broadcast reduction scheme (SBR) to mitigate the broadcast storm problem in VANETs. Wireless Personal Communications, 56(3), 559–572.Youssef, M., Ibrahim, M., Abdelatif, M., Chen, L., & Vasilakos, A. (2013). Routing metrics of cognitive radio networks: A survey. IEEE Communications Surveys & Tutorials, 16(1), 92–109