V2V Routing in VANET Based on Fuzzy Logic and Reinforcement Learning
AbstractTo ensure the transmission quality of real-time communications on the road, the research of routing protocol is crucial to improve effectiveness of data transmission in Vehicular Ad Hoc Networks (VANETs). The existing work Q-Learning based routing algorithm, QLAODV, is studied and its problems, including slow convergence speed and low accuracy, are found. Hence, we propose a new routing algorithm FLHQRP by considering the characteristics of real-time communication in VANETs in the paper. The virtual grid is introduced to divide the vehicle network into clusters. The node’s centrality and mobility, and bandwidth efficiency are processed by the Fuzzy Logic system to select the most suitable cluster head (CH) with the stable communication links in the cluster. A new heuristic function is also proposed in FLHQRP algorithm. It takes cluster as the environment state of heuristic Q-learning, by considering the delay to guide the forwarding process of the CH. This can speed up the learning convergence, and reduce the impact of node density on the convergence speed and accuracy of Q-learning. The problem of QLAODV is solved in the proposed algorithm since the experimental results show that FLHQRP has many advantages on delivery rate, end-to-end delay, and average hops in different network scenarios.
 Bagherlou, H.; Ghaffari, A. (2018). A routing protocol for vehicular ad hoc networks using simulated annealing algorithm and neural networks, The Journal of Supercomputing, 74(6), 2528-2552, 2018.
 Bof, N.; Baggio, G.; Zampieri, S. (2016). On the role of network centrality in the controllability of complex networks, IEEE Transactions on Control of Network Systems, 4(3), 643-653, 2016.
 Gustafson, C.; Mahler, K.; Bolin, D.; Tufvesson, F. (2020). The COST IRACON geometry-based stochastic channel model for vehicle-to-vehicle communication in intersections, IEEE transactions on vehicular technology, 69(3), 2365-2375, 2020.
 Hammood, O. A.; Nizam, M.; Nafaa, M.; Hammood, W. A. (2019). RESP: Relay suitabilitybased routing protocol for video streaming in vehicular Ad hoc networks, International Journal of Computers Communications and Control, 14(1), 21-38, 2019.
 Hassanabadi, B.; Shea, C.; Zhang, L.; Valaee, S. (2014). Clustering in vehicular ad hoc networks using affinity propagation, Ad Hoc Networks, 13, 535-548, 2010.
 Jin, W.; Gu, R.; Ji, Y. (2019). Reward function learning for q-learning-based geographic routing protocol, IEEE Communications Letters, 23(7), 1236-1239, 2019.
 Kanumalli, S. S.; Chinta, A.; Chandra Murty, P. S. R. (2019). Isolation of wormhole attackers in IOV using WPWP packet, Revue d'Intelligence Artificielle, 33(1), 9-13, 2019.
 Li, F.; Song, X.; Chen, H.; Li, X.; Wang, Y. (2018). Hierarchical routing for vehicular ad hoc networks via reinforcement learning, IEEE Transactions on Vehicular Technology, 68(2), 1852- 1865, 2018.
 Lipman, T.; Rodier, C.; Shaheen, S.; Finson, R. (2010). Intelligent transportation systems, Plant Physiology, 66(1), 93-96, 2010.
 Liu, Q.; He, X.; Guan, F. W.; Zhao, Y. C.; Jiang, F.; Tian, F. X.; Wang, S. X. (2019). Method and implementation of improving the pointing accuracy of an optical remote sensor using a star sensor, Traitement du Signal, 36(4), 311-317, 2019.
 Lu, K. J.; Nguyen, T.; Tran, N.; Karacolak, T. (2020). Parasitic spirals for enhancing bandwidth of a simultaneous transmit and receive patch antenna, Microsystem Technologies, 1-6, 2020.
 Mohammadnezhad, M.; Ghaffari, A. (2019). Hybrid routing scheme using imperialist competitive algorithm and RBF neural networks for VANETs, Wireless Networks, 25(5), 2831-2849, 2019.
 Pourghebleh, B.; Navimipour, N. J. (2019). Towards efficient data collection mechanisms in the vehicular ad hoc networks, International Journal of Communication Systems, 32(5), 1-20, 2019.
 Rivoirard, L.; Wahl, M.; Sondi, P.; Berbineau, M.; Gruyer, D. (2018). Chain-Branch-Leaf: A clustering scheme for vehicular networks using only V2V communications, Ad Hoc Networks, 68, 70-84, 2018.
 Ren, J.; Huang, S. Y.; Song, W.; Han, J. (2019). A novel indoor positioning algorithm for wireless sensor network based on received signal strength indicator filtering and improved Taylor series expansion, Traitement du Signal, 36(1), 103-108, 2019.
 Sachan, V.; Kumar, I.; Shankar, R.; Mishra, R. K. (2018). Analysis of transmit antenna selection based selective decode forward cooperative communication protocol, Traitement du Signal, 35(1), 47-60, 2018.
 Shimada, R.; Gassho, A.; Matsubara, N. (2019). Comparison between the impression of hot-cold and warm-cool in unspecific scale on the evaluation oftheexperimental combined environment, Journal of Environmental Engineering , 84(766), 1041-1050, 2019.
 Sodhro, A. H.; Obaidat, M. S.; Abbasi, Q. H.; Pace, P.; Pirbhulal, S.; Fortino, G.; Qaraqe, M. (2019). Quality of service optimization in an IOT-driven intelligent transportation system, IEEE Wireless Communications, 26(6), 10-17, 2019.
 Wahab, O. A.; Otrok, H.; Mourad, A. (2013). VANET QoS-OLSR: QoS-based clustering protocol for vehicular ad hoc networks, Computer Communications, 36(13), 1422-1435, 2013.
 Wang, F. F.; Hu, H. F. (2019). An energy-efficient unequal clustering routing algorithm for wireless sensor network, Revue d'Intelligence Artificielle, 33(3), 249-254, 2012.
 Wu, C.; Kumekawa, K.; Kato, T. (2010). Distributed reinforcement learning approach for vehicular ad hoc networks, IEICE transactions on communications, 93(6), 1431-1442, 2010.
 Wu, C.; Ohzahata, S.; Kato, T. (2013). Flexible, portable, and practicable solution for routing in VANETs: A fuzzy constraint Q-learning approach, IEEE Transactions on Vehicular Technology, 62(9), 4251-4263, 2013.
 Xiao, D. G.; Peng, L. X.; Song, D. (2012). Improved GPSR routing algorithm in hybrid VANET environment, Journal of Software, 33(3), 249-254, 2012.
 Yang, Q.; Lim, A.; Li, S.; Fang, J.; Agrawal, P. (2010). ACAR: Adaptive connectivity aware routing for vehicular ad hoc networks in city scenarios, Mobile Networks and Applications, 15(1), 36-60, 2010.
 Yang, X. Y.; Zhang, W. L.; Lu, H. M.; Zhao, L. (2020). V2V Routing in VANET Based on Heuristic Q-Learning, International Journal of Computers Communications & Control, 15(5), 3928, 2020.
 Zang, H. J.; Huang, Y.; Cao, H. B.; Li, C. C. (2019). A novel privacy protection protocol for vehicular ad hoc networks based on elliptic curve bilinear mapping, Ingenierie des Systemes d'Information, 24(4), 397-402, 2019.
 Zhang, D.; Zhang, T.; Liu, X. (2019). Novel self-adaptive routing service algorithm for application in VANET, Applied Intelligence, 49(5), 1866-1879, 2019.
 Zhang, X. L.; Qian, Z.; Zhang, T.(2012). Improved GPSR-SD routing protocol for VANET, Journal of Highway and Transportation Research and Development, 11(4), 98-103, 2017.
 Zhao, L.; Han, G.; Li, Z.; Shu, L. (2020). Intelligent digital twin-based software-defined vehicular networks, IEEE Network, 34(5), 178-184, 2020.
 Zhao, L.; Liu, Y.; Al-Dubai, A.Y.; Zomaya, A.; Hawbani, A. (2020). A novel generation adversarial network-based vehicle trajectory prediction method for intelligent vehicular networks, IEEE Internet of Things Journal, in press, 2020.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.