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An intelligent dimension-based cat swarm optimization for efficient cooperative multi-hop relay selection in vehicular network

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Abstract

A revolutionary concept for enhancing the geographical differentiations in vehicular ad hoc networks (VANET) is cooperation. Similar to multi-antenna systems such as Multiple-Input Multiple-Output (MIMO) systems, cooperative communication systems may enhance performance and geographical diversity. These systems are also easier to develop with dispersed hardware and traditionally constrained resources. In order to boost VANET efficiency with regard to certain network restrictions like energy efficiency (EE), network capacity (NC), and outage probability (OP), more network solutions are inspired by cooperative communication despite the fact that various successful research on VANET coupled with cooperative communications is mentioned, they have certain basic issues that they do not clearly address. This article primary goal is to develop and suggest an optimization technique for choosing the fewest possible multi-hops among source and destination for a cooperative VANET. The signal is transferred from the source to the cooperating nodes (their relays) and to their comparable destination during the first time slot of the first phase. In order to solve a multi-objective function including goal throughput and OP, how to choose the ideal amount of relays or hops to allow for flexible communication is the main problem being tackled here. The selection of the best source and destination multi-hop is significantly aided by the use of the novel Dimension-based Cat Swarm Optimization (D-CSO). The suggested model was shown to have a reasonable convergence rate and a fair performance over the whole network via the performance assessment.

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References

  1. Okeke GO, Krzymień WA, Jing Y, Melzer J (2015) A novel low-complexity joint user-relay selection and association for multi-user multi-relay MIMO uplink. IEEE Wirel Commun Lett 4(3):309–312

    Article  Google Scholar 

  2. Nam E, Jang C, Lee JH (2015) Performance of reactive relay selection based on cumulative distribution function of SNRs for two-way relay networks. IEEE Commun Lett 19(8):1378–1381

    Article  Google Scholar 

  3. Wang W, Teh KC, Li KH (2016) Generalized relay selection for improved security in cooperative DF relay networks. IEEE Wirel Commun Lett 5(1):28–31

    Article  Google Scholar 

  4. Adam H, Yanmaz E, Bettstetter C (2014) Medium access with adaptive relay selection in cooperative wireless networks. IEEE Trans Mob Comput 13(9):2042–2057

    Article  Google Scholar 

  5. Zhao D, Zhao H, Jia M, Xiang W (2014) Smart relaying for selection combining based decode-and-forward cooperative networks. IEEE Commun Lett 18(1):74–77

    Article  Google Scholar 

  6. Feng H, Xiao Y, Cimini LJ (2014) Net throughput of centralized and decentralized cooperative networks with relay selection. IEEE Wirel Commun Lett 3(5):477–480

    Article  Google Scholar 

  7. Xia M, Aïssa S (2015) Fundamental relations between reactive and proactive relay-selection strategies. IEEE Commun Lett 19(7):1249–1252

    Article  Google Scholar 

  8. Nguyen BV, Afolabi RO, Kim K (2013) Dependence of outage probability of cooperative systems with single relay selection on channel correlation. IEEE Commun Lett 17(11):2060–2063

    Article  Google Scholar 

  9. Su Y, Lu X, Zhao Y, Huang L, Du X (2019) Cooperative communications with relay selection based on deep reinforcement learning in wireless sensor networks. IEEE Sens J 19(20):9561–9569

    Article  Google Scholar 

  10. Zheng Z, Fu S, Lu K, Wang J, Chen B (2012) On the relay selection for cooperative wireless networks with physical-layer network coding. Wirel Netw 18:653–665

    Article  Google Scholar 

  11. Zhang L, Cimini LJ Jr (2007) Power-efficient relay selection in cooperative networks using decentralized distributed space-time block coding. EURASIP J Adv Signal Process 2008(362809):1–10

    Google Scholar 

  12. Abdulhadi S, Jaseemuddin M, Anpalagan A (2012) A survey of distributed relay selection schemes in cooperative wireless ad hoc networks. Wirel Pers Commun 63:917–935

    Article  Google Scholar 

  13. Seyfi M, Muhaidat S, Liang J, Uysal M (2011) Relay selection in dual-hop vehicular networks. IEEE Signal Process Lett 18(2):134–137

    Article  Google Scholar 

  14. Hui Y, Su Z, Luan TH, Li C (2020) Reservation service: trusted relay selection for edge computing services in vehicular networks. IEEE J Sel Areas Commun 38(12):2734–2746

    Article  Google Scholar 

  15. Singh AK, Pamula R, Jain PK, Srivastava G (2021) An efficient vehicular-relay selection scheme for vehicular communication. Soft Comput. https://doi.org/10.1007/s00500-021-06106-4

    Article  Google Scholar 

  16. Achour I, Alfayez F, Busson A (2021) A robust and efficient adaptive data dissemination protocol based on smart relay selection in vehicular networks. Wirel Netw 27:4497–4511

    Article  Google Scholar 

  17. Akin AI, Ilhan H, Özdemir Ö (2015) Relay selection for DF-based cooperative vehicular systems. EURASIP J Wirel Commun Netw 2015(30):1–9

    Google Scholar 

  18. Saghezchi FB, Radwana A, Rodriguez J (2017) Energy-aware relay selection in cooperative wireless networks: an assignment game approach. Ad Hoc Netw 56:96–108

    Article  Google Scholar 

  19. Ying B, Nayak A (2018) A power-efficient and social-aware relay selection method for multi-hop D2D communications. IEEE Commun Lett 22(7):1450–1453

    Article  Google Scholar 

  20. Tian D, Zhou J, Sheng Z, Chen M, Ni Q, Leung VCM (2017) Self-organized relay selection for cooperative transmission in vehicular ad-hoc networks. IEEE Trans Veh Technol 66(10):9534–9549

    Article  Google Scholar 

  21. Siddig AA, Ibrahim AS, Ismail MH (2020) An optimal power allocation and relay selection full-duplex store-carry-forward scheme for intermittently connected vehicular networks. IEEE Access 8:163903–163916

    Article  Google Scholar 

  22. Li S, Wang F, Gaber J, Zhou Y (2020) An optimal relay number selection algorithm for balancing multiple performance in flying ad hoc networks. IEEE Access 8:225884–225901

    Article  Google Scholar 

  23. Poursajadi S, Madani MH (2021) Adaptive optimal relay selection in cooperative vehicular communications under security constraints. Veh Commun 31:1003600

    Google Scholar 

  24. Dan CHEN, Hong JI, Xi LI, Yi LI (2010) QoS-guaranteed multi-relay selection and power allocation optimization in cooperative systems. J China Univ Posts Telecommun 17(6):25–31

    Article  Google Scholar 

  25. Chen D, Ji H, Li X, Li Y (2011) Energy-efficient joint relay node selection and power allocation over multihop relaying cellular networks toward LTE-Advanced. J China Univ Posts Telecommun 18(3):1–7

    Article  Google Scholar 

  26. Dong L, Han Z, Petropulu AP, Poor HV (2010) Improving wireless physical layer security via cooperating relays. IEEE Trans Signal Process 58(3):1875–1888

    Article  MathSciNet  MATH  Google Scholar 

  27. Yang M, Guo D, Huang Y, Duong TQ, Zhang B (2016) Physical layer security with threshold-based multiuser scheduling in multi-antenna wireless networks. IEEE Trans Commun 64(12):5189–5202

    Article  Google Scholar 

  28. Li J, Petropulu AP, Weber S (2011) On cooperative relaying schemes for wireless physical layer security. IEEE Trans Signal Process 59(10):4985–4997

    Article  MathSciNet  MATH  Google Scholar 

  29. Krikidis I, Thompson JS, McLaughlin S (2008) Relay selection for secure cooperative networks with jamming. IEEE Trans Wirel Commun 8(10):5003–5011

    Article  Google Scholar 

  30. Zou Y, Wang X, Shen W (2013) Optimal relay selection for physical-layer security in cooperative wireless networks. IEEE J Sel Areas Commun 31(10):2099–2111

    Article  Google Scholar 

  31. Bao VNQ, Linh-Trung N, Debbah M (2013) Relay selection schemes for dual-hop networks under security constraints with multiple eavesdroppers. IEEE Trans Wirel Commun 12(12):6076–6085

    Article  Google Scholar 

  32. Bouzidi A, Riffi ME, Barkatou M (2019) Cat swarm optimization for solving the open shop scheduling problem. J Ind Eng Int 15:367–378

    Article  Google Scholar 

  33. Gradshteyn IS, Ryzhik IM (2007) Table of integrals, series and products. Math Comput 20(96):1157–1160

    MATH  Google Scholar 

  34. Ai TJ, Kachitvichyanukul V (2009) A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery. Comput Oper Res 36(5):1693–1702

    Article  MATH  Google Scholar 

  35. Venkata Rao R (2016) Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34

    Google Scholar 

  36. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  37. Devulapalli PK, Pokkunuri PS, Maganti SB (2021) Energy efficient multi-hop cooperative transmission protocol for large scale mobile ad hoc networks. Wirel Pers Commun (Springer) 121(4):3309–3328

    Article  Google Scholar 

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Acknowledgements

The Deanship of Scientific Research of King Khalid University, Abha, Kingdom of Saudi Arabia, is acknowledged by the authors for sponsoring this study under Large Groups RGP.2/170/44.

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Authors and Affiliations

  1. Department of ECE, Lovely Professional University, Phagwara, Punjab, 144411, India

    Uppula Kiran, Krishan Kumar & Ajay Roy

  2. Computer Science and Engineering Department, College of Sciences and Arts, King Khalid University, Dhahran Al Janoub Campus, 64261, Abha, Kingdom of Saudi Arabia

    Shamimul Qamar

  3. Electrical Engineering Department, Jamia Millia Islamia University, New Delhi, 110025, India

    Abdul Azeem

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  1. Uppula Kiran
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  2. Krishan Kumar
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  3. Ajay Roy
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Correspondence to Shamimul Qamar.

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Kiran, U., Kumar, K., Roy, A. et al. An intelligent dimension-based cat swarm optimization for efficient cooperative multi-hop relay selection in vehicular network. Neural Comput & Applic 35, 15381–15395 (2023). https://doi.org/10.1007/s00521-023-08541-w

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