International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

MENU
International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Erudite Fish Swarm Optimization Based Routing Protocol to Maximize Wireless Sensor Network Lifetime

Author NameAuthor Details

A. Balasubramanian, P. Ponmuthuramalingam

A. Balasubramanian[1]

P. Ponmuthuramalingam[2]

[1]Department of Computer Science, Government Arts College, Coimbatore, Tamil Nadu, India

[2]Regional Joint Directorate of Collegiate Education, Madurai Region, Madurai, Tamil Nadu, India

Cite this Article

DOI: 10.22247/ijcna/2022/212556

Pages : 305-315

 Download PDF

1547

Views

Abstract

Wireless Sensor Networks (WSNs) are an influential network form that comprises remote nodes having sensing, processing, and communication capabilities. WSN is a unique ad-hoc network with a wireless telecommunications infrastructure that effectively supports, observes, and responds to natural and artificial events. It is impossible to employ the ad-hoc network routing methods in sensor networks since they are not scalable. WSN relies on the routing protocol to get data from sensors to their final destination in a timely way. If the routing protocol fails to work, then it is expected that a significant amount of time and effort will be spent finding the most efficient route, increasing the likelihood that the worst possible option will be selected. Because of this, WSN routing protocols must include the concept of "erudite" features, which refers to a high degree of sensing of the nodes around them to determine the optimum path. Fish swarm optimization is the basis of the new WSN routing protocol proposed in this paper, namely the Erudite Fish Swarm Optimization Based Routing Protocol (EFSORP). In EFSORP, nodes are treated as fishes. Nodes having prior knowledge about routes are selected at random. Foraging, following, swarming, and random movement is four of the most common behaviors of fishes while seeking food. These behaviors are mimicked to identify the best routes in WSN. EFSORP’s performance is evaluated in NS3. A wide range of necessary computer network performance measures are used to assess EFSORP against existing routing protocols. EFSORP's results show that it outperforms the current routing protocols on all measures.

Index Terms

Routing

WSN

Energy

Delay

Fish

Optimization

Reference

  1. 1.
    Pathak and M. G. Bhatt, “Synergetic manufacturing systems anchored by cloud computing: A classified review of trends and perspective,” Mater. Today Proc., 2020, doi: https://doi.org/10.1016/j.matpr.2020.07.435.
  2. 2.
    T. A. Alghamdi, “Enhanced QoS routing protocol using maximum flow technique,” Comput. Electr. Eng., vol. 89, p. 106950, 2021, doi: https://doi.org/10.1016/j.compeleceng.2020.106950.
  3. 3.
    T. M. Behera, S. K. Mohapatra, P. Mukjerjee, and H. K. Sahoo, “Work-In-Progress: DEEC-VD: A Hybrid Energy Utilization Cluster-Based Routing Protocol for WSN for Application in IoT,” in 2017 International Conference on Information Technology (ICIT), 2017, pp. 97–100, doi: 10.1109/ICIT.2017.42.
  4. 4.
    B. Chakraborty and P. Mukherjee, “WAL: An Energy Efficient Hybrid Routing Protocol in Co-operative Communication,” in 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2018, pp. 1317–1322, doi: 10.1109/ICECA.2018.8474708.
  5. 5.
    A. Mathur, T. Newe, W. Elgenaidi, M. Rao, G. Dooly, and D. Toal, “A secure end-to-end IoT solution,” Sensors Actuators A Phys., vol. 263, pp. 291–299, 2017, doi: https://doi.org/10.1016/j.sna.2017.06.019.
  6. 6.
    M. Elappila, S. Chinara, and D. R. Parhi, “Survivable Path Routing in WSN for IoT applications,” Pervasive Mob. Comput., vol. 43, pp. 49–63, Jan. 2018, doi: 10.1016/j.pmcj.2017.11.004.
  7. 7.
    J. John, G. S. Kasbekar, and M. S. Baghini, “Maximum lifetime convergecast tree in wireless sensor networks,” Ad Hoc Networks, vol. 120, p. 102564, 2021, doi: https://doi.org/10.1016/j.adhoc.2021.102564.
  8. 8.
    M. C. Domingo and R. Prior, “Energy analysis of routing protocols for underwater wireless sensor networks,” Comput. Commun., vol. 31, no. 6, pp. 1227–1238, 2008, doi: https://doi.org/10.1016/j.comcom.2007.11.005.
  9. 9.
    T. N. Sugumar and N. R. Ramasamy, “mDesk: a scalable and reliable hypervisor framework for effective provisioning of resource and downtime reduction,” J. Supercomput., vol. 76, no. 2, pp. 1277–1292, Feb. 2020, doi: 10.1007/s11227-018-2662-5.
  10. 10.
    G. D. O’Mahony, K. G. McCarthy, P. J. Harris, and C. C. Murphy, “Developing novel low complexity models using received in-phase and quadrature-phase samples for interference detection and classification in Wireless Sensor Network and GPS edge devices,” Ad Hoc Networks, vol. 120, p. 102562, 2021, doi: https://doi.org/10.1016/j.adhoc.2021.102562.
  11. 11.
    A. Arena, P. Perazzo, C. Vallati, G. Dini, and G. Anastasi, “Evaluating and improving the scalability of RPL security in the Internet of Things,” Comput. Commun., vol. 151, pp. 119–132, 2020, doi: https://doi.org/10.1016/j.comcom.2019.12.062.
  12. 12.
    D. Gopika and R. Panjanathan, “Energy efficient routing protocols for WSN based IoT applications: A review,” Mater. Today Proc., 2020, doi: https://doi.org/10.1016/j.matpr.2020.10.137.
  13. 13.
    J. Ramkumar and R. Vadivel, “Performance Modeling of Bio-Inspired Routing Protocols in Cognitive Radio Ad Hoc Network to Reduce End-to-End Delay,” Int. J. Intell. Eng. Syst., vol. 12, no. 1, pp. 221–231, 2019, doi: 10.22266/ijies2019.0228.22.
  14. 14.
    J. Ramkumar and R. Vadivel, “Meticulous elephant herding optimization based protocol for detecting intrusions in cognitive radio ad hoc networks,” Int. J. Emerg. Trends Eng. Res., vol. 8, no. 8, pp. 4549–4554, 2020, doi: 10.30534/ijeter/2020/82882020.
  15. 15.
    J. Ramkumar and R. Vadivel, “CSIP—cuckoo search inspired protocol for routing in cognitive radio ad hoc networks,” in Advances in Intelligent Systems and Computing, 2017, vol. 556, pp. 145–153, doi: 10.1007/978-981-10-3874-7_14.
  16. 16.
    J. Ramkumar and R. Vadivel, “Multi-Adaptive Routing Protocol for Internet of Things based Ad-hoc Networks,” Wirel. Pers. Commun., pp. 1–23, Apr. 2021, doi: 10.1007/s11277-021-08495-z.
  17. 17.
    J. Ramkumar and R. Vadivel, “Bee inspired secured protocol for routing in cognitive radio ad hoc networks,” INDIAN J. Sci. Technol., vol. 13, no. 30, pp. 3059–3069, 2020, doi: 10.17485/IJST/v13i30.1152.
  18. 18.
    J. Ramkumar and R. Vadivel, “FLIP: Frog Leap Inspired Protocol for Routing in Cognitive Radio Ad Hoc Networks,” in International Conference on Recent Trends in Engineering and Material Sciences (ICEMS - 2016), 2016, p. 248.
  19. 19.
    J. Ramkumar and R. Vadivel, “Intelligent Fish Swarm Inspired Protocol (IFSIP) For Dynamic Ideal Routing in Cognitive Radio Ad-Hoc Networks,” Int. J. Comput. Digit. Syst., vol. 10, no. 1, pp. 1063–1074, 2020, doi: http://dx.doi.org/10.12785/ijcds/100196.
  20. 20.
    J. Ramkumar and R. Vadivel, “Improved frog leap inspired protocol (IFLIP) – for routing in cognitive radio ad hoc networks (CRAHN),” World J. Eng., vol. 15, no. 2, pp. 306–311, 2018, doi: 10.1108/WJE-08-2017-0260.
  21. 21.
    J. Ramkumar, C. Kumuthini, B. Narasimhan, and S. Boopalan, “Energy Consumption Minimization in Cognitive Radio Mobile Ad-Hoc Networks using Enriched Ad-hoc On-demand Distance Vector Protocol,” 2022 Int. Conf. Adv. Comput. Technol. Appl., pp. 1–6, Mar. 2022, doi: 10.1109/ICACTA54488.2022.9752899.
  22. 22.
    P. Menakadevi and J. Ramkumar, “Robust Optimization Based Extreme Learning Machine for Sentiment Analysis in Big Data,” 2022 Int. Conf. Adv. Comput. Technol. Appl., pp. 1–5, Mar. 2022, doi: 10.1109/ICACTA54488.2022.9753203.
  23. 23.
    O. C. Ghica, C. Nita-Rotaru, G. Trajcevski, and P. Scheuermann, “Security of electrostatic field persistent routing: Attacks and defense mechanisms,” Ad Hoc Networks, vol. 36, pp. 270–295, 2016, doi: https://doi.org/10.1016/j.adhoc.2015.07.016.
  24. 24.
    S. Gopinath, K. Vinoth Kumar, P. Elayaraja, A. Parameswari, S. Balakrishnan, and M. Thiruppathi, “SCEER: Secure cluster based efficient energy routing scheme for wireless sensor networks,” Mater. Today Proc., vol. 45, pp. 3579–3584, 2021, doi: https://doi.org/10.1016/j.matpr.2020.12.1096.
  25. 25.
    Y. Yang, X. Shi, Q. Ma, Y. Li, X. Yin, and Z. Wang, “Path stability in partially deployed secure BGP routing,” Comput. Networks, vol. 206, p. 108762, 2022, doi: https://doi.org/10.1016/j.comnet.2022.108762.
  26. 26.
    Y. Lin, Y. Mao, Y. Zhang, and S. Zhong, “Secure deduplication schemes for content delivery in mobile edge computing,” Comput. Secur., vol. 114, p. 102602, 2022, doi: https://doi.org/10.1016/j.cose.2022.102602.
  27. 27.
    R. Fotohi, E. Nazemi, and F. Shams Aliee, “An agent-based self-protective method to secure communication between UAVs in unmanned aerial vehicle networks,” Veh. Commun., vol. 26, p. 100267, 2020, doi: https://doi.org/10.1016/j.vehcom.2020.100267.
  28. 28.
    T. Yang, X. Xiangyang, L. Peng, L. Tonghui, and P. Leina, “A secure routing of wireless sensor networks based on trust evaluation model,” Procedia Comput. Sci., vol. 131, pp. 1156–1163, Jan. 2018, doi: 10.1016/J.PROCS.2018.04.289.
  29. 29.
    G. A. E. Satish Kumar and P. Rama Devi, “A novel proactive routing strategy to defend node isolation attack in MANETS,” Mater. Today Proc., 2021, doi: https://doi.org/10.1016/j.matpr.2020.11.361.
  30. 30.
    M. A. Cheema et al., “Blockchain-based secure delivery of medical supplies using drones,” Comput. Networks, vol. 204, p. 108706, 2022, doi: https://doi.org/10.1016/j.comnet.2021.108706.
  31. 31.
    M. P. Lokhande and D. D. Patil, “Secured energy efficient machine -to-machine communication for telerobotic system,” Informatics Med. Unlocked, vol. 26, p. 100731, 2021, doi: https://doi.org/10.1016/j.imu.2021.100731.
  32. 32.
    N. Nasser and Y. Chen, “SEEM: Secure and energy-efficient multipath routing protocol for wireless sensor networks,” Comput. Commun., vol. 30, no. 11, pp. 2401–2412, 2007, doi: https://doi.org/10.1016/j.comcom.2007.04.014.
  33. 33.
    G. Rathee, A. Sharma, R. Kumar, F. Ahmad, and R. Iqbal, “A trust management scheme to secure mobile information centric networks,” Comput. Commun., vol. 151, pp. 66–75, 2020, doi: https://doi.org/10.1016/j.comcom.2019.12.024.
  34. 34.
    M. Kuchaki Rafsanjani and H. Fatemidokht, “FBeeAdHoc: A secure routing protocol for BeeAdHoc based on fuzzy logic in MANETs,” AEU - Int. J. Electron. Commun., vol. 69, no. 11, pp. 1613–1621, 2015, doi: https://doi.org/10.1016/j.aeue.2015.07.013.
  35. 35.
    A. Chowdhury and D. De, “FIS-RGSO: Dynamic Fuzzy Inference System Based Reverse Glowworm Swarm Optimization of energy and coverage in green mobile wireless sensor networks,” Comput. Commun., vol. 163, pp. 12–34, 2020, doi: https://doi.org/10.1016/j.comcom.2020.09.002.
  36. 36.
    X. Zhang, X. Lu, and X. Zhang, “Mobile wireless sensor network lifetime maximization by using evolutionary computing methods,” Ad Hoc Networks, vol. 101, p. 102094, 2020, doi: https://doi.org/10.1016/j.adhoc.2020.102094.
SCOPUS
SCImago Journal & Country Rank

Announcements

Archives

Downloads

Contact Us

Dr.M. Milton Joe
EverScience Publications,
Nagercoil, Tamilnadu,India

Talk to Us

+91 9442777224

Email Us

editor@ijcna.org

info@ijcna.org

info@everscience.org

Quick Links

Follow Us

Copyright © 2023; EverScience Publications

Designed & Maintained By Shiro Software Solutions