Quantitative and qualitative correlation analysis of optimal route discovery for vehicular ad-hoc networks

Abstract

Vehicular ad-hoc networks (VANETs) are a significant field in the intelligent transportation system (ITS) for improving road security. The interaction among the vehicles is enclosed under VANETs. Many experiments have been performed in the region of VANET improvement. A familiar challenge that occurs is obtaining various constrained quality of service (QoS) metrics. For resolving this issue, this study obtains a cost design for the vehicle routing issue by focusing on the QoS metrics such as collision, travel cost, awareness, and congestion. The awareness of QoS is fuzzified into a price design that comprises the entire cost of routing. As the genetic algorithm (GA) endures from the most significant challenges such as complexity, unassisted issues in mutation, detecting slow convergence, global maxima, multifaceted features under genetic coding, and better fitting, the currently established lion algorithm (LA) is employed. The computation is analyzed by deploying three well-known studies such as cost analysis, convergence analysis, and complexity investigations. A numerical analysis with quantitative outcome has also been studied based on the obtained correlation analysis among various cost functions. It is found that LA performs better than GA with a reduction in complexity and routing cost.

Keywords

vehicular ad-hoc network / lion algorithm / fuzzy / quality of service / routing

References

[1] BitamS, MelloukA, ZeadallyS. Bio-inspired routing algorithms survey for vehicular ad hoc networks [J]. IEEE Communication Surveys and Tutorials, 2015, 17(2): 843-867

[2] ZeadallyS, HuntR, ChenY S, IrwinA, HassanA. Vehicular ad hoc networks (VANETs): Status, results, and challenges [J]. Telecommun Syst, 2012, 50(4): 217-241

[3] BeylotA L, LabiodH. CONVOY: A new cluster-based routing protocol for vehicular networks, in vehicular networks: Models and algorithms [M]. London, UK: John Wiley & Sons, 2013, 3: 91-139

[4] BaiF, KrishnanH, SadekarV, HollandG, ElbattTTowards characterizing and classifying communication-based automotive applications from a wireless networking perspective [M]. San Francisco, 2006, USA: Autonet, CA

[5] WuC, OhzahataS, KatoT. Flexible, portable, and practicable solution for routing in VANETs: A fuzzy constraint q-learning approach [J]. IEEE Transactions on Vehicular Technology, 2013, 62(9): 4251-4263

[6] WangW, XieF, ChatterjeeM. Small-scale and large-scale routing in vehicular ad hoc networks [J]. IEEE Trans Veh Technol, 2009, 58(9): 5200-5213

[7] WuC, KumekawaK, KatoT. Distributed reinforcement learning approach for vehicular ad hoc networks [J]. IEICE Trans Commun, 2010, E93-B(6): 1431-1442

[8] GoonewardeneR T, AliF H, StipidisE. Robust mobility adaptive clustering scheme with support for geographic routing for vehicular ad hoc networks [J]. IET Intell Transp Syst, 2009, 3(2): 148-158

[9] ZhangX M, WangE B, XiaJ J, SungD K. An estimated distance-based routing protocol for mobile ad hoc networks [J]. IEEE Trans Veh Technol, 2011, 60(7): 3473-3484

[10] RajakumarB RLion algorithm for standard and large scale bilinear system identification: A global optimization based on Lion’s social behavior [C]//2014 IEEE Congress on Evolutionary Computation (CEC), 2014, Beijing, IEEE: 21162123

[11] PackerC, PuseyA E. Male takeovers and female reproductive parameters: A simulation of oestrous synchrony in lions (Panthera leo) [J]. Animal Behavior, 1983, 31(2): 334-340

[12] RajakumarB R. The Lion’s Algorithm: A new nature-inspired search algorithm [J]. Procedia Technology, 2012, 6: 126-135

[13] EizaM H, OwensT, NiQ, ShiQ. Situation-aware QoS routing algorithm for vehicular ad hoc networks [J]. IEEE Transactions on Vehicular Technology, 2015, 64(12): 5520-5535

[14] ZhouY, WangJ. A local search-based multi-objective optimization algorithm for multi-objective vehicle routing problem with time windows [J]. IEEE Journal of Systems, 2015, 9(3): 1100-1113

[15] YangH, YangS, XuY, CaoE, LaiM, DongZ. Electric vehicle route optimization considering time-of-use electricity price by learnable partheno-genetic algorithm [J]. IEEE Transactions on Smart Grid, 2015, 6(2): 657-666

[16] AhrensM, GesterM, KlewinghausN, MullerD, PeyerS, SchulteC, TellezG. Detailed routing algorithms for advanced technology nodes [J]. IEEE Transactions on Computer-aided Design of Integrated Circuits and Systems, 2015, 34(4): 563-576

[17] EizaM H, OwensT, NiQ. Secure and robust multi-constrained QoS aware routing algorithm for VANETs [J]. IEEE Transactions on Dependable and Secure Computing, 2016, 13(1): 32-45

[18] BauerH, IonghH H, SilvestreI. Lion social behaviour in the west and central African savanna belt [J]. Mammalian Biology, 2003, 68(1): 239-243

[19] KlirG J, ClairU S, BoYFuzzy set theory: Foundations and applications [M]. Englewood Cliffs, 1997, USA: Prentice-Hall, NJ

[20] FogelL J, OwensA J, WalshM JArtificial intelligence through simulated evolution [M], 1966, New York, Wiley Publishing

[21] DoerrB, HappE. CHRISTIAN KLEIN: Crossover can provably be useful in evolutionary computation [J]. Theor Comput Sci, 2012, 425: 17-33

[22] BackT, HoffmeisterF, SchwefelH P. An overview of evolutionary algorithms for parameter optimization [J]. Journal of Evolutionary Computation, 1993, 1(1): 1-24

[23] JongK A DAn analysis of the behavior of a class of genetic adaptive systems [D]. Computer and Communication Sciences, 1975, Ann Arbor, University of Michigan

[24] PackerC, PuseyA E. Divided we fall: Cooperation among lions [J]. Scientific American, 1997, 276: 52-59

[25] PACKER, PUSEY.. Cooperation and competition within coalition of male lions: Kin selection or game theory [J]. Macmillan Journals, 1982, 296(5859): 740-742

[26] KohonenTSelf-organization and associative memory [M], 1984, Berlin, Germany, Springer-Verlag