Efficient traffic control and lifetime maximization in mobile ad hoc network by using PSO–BAT optimization

Abstract

Recent developments in dynamic mobile ad-hoc network enhance the network speed and reliability. The nodes in the dynamic ad-hoc network are moving in nature. Due to the increased subscribers in this network, the network traffic has increased to manifold which in turn creating the challenge of maintaining the energy level. In path optimization process in mobile ad-hoc network consumes more energy and the draining of the energy is dependent on network reliability and connectivity. Further, the network also suffers by harmful attacks such as denial of service attack, black hole attack and warm hole attack. The primary focus of this paper is to prevent these attacks with the help of dynamic mobile ad-hoc network on demand protocol and hybrid meta-heuristics methodologies, and also to reduce the energy drain rate. This is achieved by estimating the velocity and fitness value of the nodes. Finally, the empirical simulation results of hybrid particle swarm optimization with bat algorithm (PSO–BAT) shows that the energy drain rate level is reduced 90% as 1 mJ/s than ad-hoc on demand vector. The end-to-end delay minimized to 50% than existing Ad hoc on-demand distance vector routing. The performance metrics routing overhead and execution time has been reduced and throughput is gradually increased in PSO–BAT optimization in dynamic mobile ad hoc network scenario.

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References

  1. 1.

    Femila, B. (2019). Optimizing transmission power and energy efficient routing protocol in MANETs. Wireless Personal Communications, 5(2), 1–16.

    Google Scholar 

  2. 2.

    Majd, N. (2019). Evaluation of parameters affecting the performance of routing protocols in mobile ad hoc networks (MANETs) with a focus on energy efficiency. In Future of information and communication conference (pp. 1210–1219). Berlin: Springer.

  3. 3.

    Malhotra, T. (2019). Authentication, KDC, and key pre-distribution techniques-based model for securing AODV routing protocol in MANET. In Smart innovations in communication and computational sciences (pp. 175–186). Singapore.

  4. 4.

    Xu, L., & Huang, T. (2019). An energy-aware computation offloading method for smart edge computing in wireless metropolitan area networks. Journal of Network and Computer Applications, 9(2), 31–41.

    Google Scholar 

  5. 5.

    Wali, U. (2019). A comprehensive study on reactive and proactive routing protocols under different performance metric. Journal of Emerging Technologies, 1(2), 39–51.

    MathSciNet  Google Scholar 

  6. 6.

    Harrag, R. (2019). PSO-IZRP: New enhanced zone routing protocol based on PSO independent zone radius estimation. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 32(1), 24–61.

    Article  Google Scholar 

  7. 7.

    Yang, C. (2019). Mobility modelling and data-driven closed-loop prediction in bike-sharing systems. IEEE Transactions on Intelligent Transportation Systems, 21(1), 45–55.

    Google Scholar 

  8. 8.

    Papathanasopoulou, A. (2019). Data-driven traffic simulation models: Mobility patterns using machine learning techniques. Mobility Patterns, Big Data and Transport Analytics, 12(5), 263–295.

    Article  Google Scholar 

  9. 9.

    Wang, Z. (2019). A realistic mobility model with irregular obstacle constraints for mobile ad hoc networks. Wireless Networks, 25(2), 487–506.

    Article  Google Scholar 

  10. 10.

    Anandakumar, H., & Umamaheswari, K. (2017). Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers. Cluster Computing, 20(2), 1505–1515.

    Article  Google Scholar 

  11. 11.

    Shah, J. L. (2019). Secure neighbor discovery protocol: Review and recommendations. International Journal of Business Data Communications and Networking (IJBDCN), 15(1), 71–87.

    Article  Google Scholar 

  12. 12.

    Liu, S. (2019). IoT-NUMS: Evaluating NUMS elliptic curve cryptography for IoT platforms. IEEE Transactions on Information Forensics and Security, 14(3), 720–729.

    Article  Google Scholar 

  13. 13.

    Liu, Z. (2019). Digital twin-based process reuse and evaluation approach for smart process planning. International Journal of Advanced Manufacturing Technology, 100(8), 1619–1634.

    Article  Google Scholar 

  14. 14.

    Guleria, V. (2019). Meta-heuristic ant colony optimization based unequal clustering for wireless sensor network. Wireless Personal Communications, 12(3), 1–21.

    Google Scholar 

  15. 15.

    Femila, B. (2019). Optimizing transmission power and energy efficient routing protocol in MANETs. Wireless Personal Communications, 10(5), 1–16.

    Google Scholar 

  16. 16.

    Anandakumar, H., & Umamaheswari, K. (2018). A bio-inspired swarm intelligence technique for social aware cognitive radio handovers. Computers and Electrical Engineering, 71, 925–937. https://doi.org/10.1016/j.compeleceng.2017.09.016.

    Article  Google Scholar 

  17. 17.

    Gandotra, J. (2016). Device-to-device communication in cellular networks: A survey. Journal of Network and Computer Applications, 7(1), 99–117.

    Article  Google Scholar 

  18. 18.

    Kyriazis, G., & Rouskas, A. (2017). Joint access and backhaul power consumption optimization in heterogeneous mobile broadband networks. Journal of Green Engineering, 6(4), 337–368.

    Article  Google Scholar 

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Correspondence to N. Sridevi.

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Sridevi, N., Nagarajan, V. & Sakthidasan @ Sankaran, K. Efficient traffic control and lifetime maximization in mobile ad hoc network by using PSO–BAT optimization. Wireless Netw 27, 861–870 (2021). https://doi.org/10.1007/s11276-019-02173-6

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Keywords

  • Ad hoc on-demand distance vector
  • Dynamic MANET on demand
  • Mobile ad hoc network
  • Particle swarm optimization