International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Secretary Bird Optimization with Differential Evolution (SBODE) and Trust Energy Aware Clustering Routing (TREACR) Protocol for Wireless Sensor Network (WSN)

Author NameAuthor Details

Amsaveni Manigandan, M. Saranya

Amsaveni Manigandan[1]

M. Saranya[2]

[1]Department of Computer Science, P. K. R Arts College for Women, Gobichettipalayam, Tamil Nadu, India.

[2]Department of Computer Science, P.K.R. Arts College for Women, Gobichettipalayam, Tamil Nadu, India.

Abstract

In Wireless Sensor Networks (WSNs), nodes with limited battery power transmit data to a Base Station (BS). Ensuring security and Energy Efficiency (EE) is crucial, but traditional protocols lack a balanced approach. This study introduces the Trust Energy Aware Clustering Routing (TREACR) protocol to enhance energy efficiency and secure Data Transmission (DT). TREACR selects Cluster Heads (CHs) using Secretary Bird Optimisation with Differential Evolution (SBODE), extending Network Lifetime (NL) through efficient energy distribution. The SBODE algorithm and fitness measure effectively detect attacks, optimizing CH Election (CHE) based on mobility, latency, energy, trust, and CH distance. The TREACR protocol employs a Dynamic Trust (DT) model to assess node behavior using Wormhole (WH), flooding, Black Hole (BH), Sink Hole (SH), and Grey Hole (GH) probabilities. Evaluation results demonstrate TREACR’s effectiveness in improving Packet Delivery Ratio, Packet Loss Rate, NL, Residual Energy (RE), and End-to-End Delay (E2ED).

Index Terms

Wireless Sensor Network

Secretary Bird Optimization

Trust Energy Aware Clustering

Routing

Trust Model

Security

Optimization

Energy Efficiency

Reference

  1. 1.
    W. Elsayed, M. Elhoseny, S. Sabbeh, & A. Riad, “Self-maintenance model for wireless sensor networks,” Computers & Electrical Engineering, vol. 70, pp. 799-812, 2018.
  2. 2.
    J. S. Pan, Trong-The Nguyen, T. K. Dao, T. S. Pan, & S.C. Chu, “Clustering Formation in Wireless Sensor Networks: A Survey,” J. Netw. Intell., vol. 2, no. 4, pp. 287-309, 2017.
  3. 3.
    E. R. Montiel, M. E. Rivero-Angeles, G. Rubino, H. Molina-Lozano, R. Menchaca-Mendez, & R. Menchaca-Mendez, “Performance analysis of cluster formation in wireless sensor networks,” Sensors, vol. 17, no. 12, pp. 2902, 2017.
  4. 4.
    H. R. Farahzadi, M. Langarizadeh, M. Mirhosseini, & S. A. Fatemi Aghda, “An improved cluster formation process in wireless sensor network to decrease energy consumption,” Wireless Networks, vol. 27, pp. 1077-1087, 2021.
  5. 5.
    Y. Li, & Y. Tian, “A lightweight and secure three-factor authentication protocol with adaptive privacy-preserving property for wireless sensor networks,” IEEE Systems Journal, vol. 16, no. 4, pp. 6197-6208, 2022.
  6. 6.
    M. Elshrkawey, S. M. Elsherif, & M. E Wahed, “An enhancement approach for reducing the energy consumption in wireless sensor networks,” Journal of King Saud University-Computer and Information Sciences, vol. 30, no. 2, pp. 259-267, 2018.
  7. 7.
    T. Ahmad, M. Haque, & A. M. Khan, “An energy-efficient cluster head selection using artificial bee’s colony optimization for wireless sensor networks,” Advances in nature-inspired computing and applications, pp. 189-203, 2019.
  8. 8.
    P. Subramanian, J. M. Sahayaraj, S. Senthilkumar, & D. S. Alex, “A hybrid grey wolf and crow search optimization algorithm-based optimal cluster head selection scheme for wireless sensor networks,” Wireless Personal Communications, vol. 113, no. 2, pp. 905-925, 2020.
  9. 9.
    D. Chandirasekaran, & T. Jayabarathi, “Cat swarm algorithm in wireless sensor networks for optimized cluster head selection: a real time approach,” Cluster Computing, vol. 22, pp. 11351-11361, 2019.
  10. 10.
    T. A. Alghamdi, “Energy efficient protocol in wireless sensor network: optimized cluster head selection model,” Telecommunication Systems, vol. 74, no. 3, pp. 331-345, 2020.
  11. 11.
    W. Osamy, A. M. Khedr, A. Salim, A. I. Al Ali, & A. A. El-Sawy, “Coverage, deployment and localization challenges in wireless sensor networks based on artificial intelligence techniques: a review,” IEEE Access, vol. 10, pp. 30232-30257, 2022.
  12. 12.
    M. Faris, M. N. Mahmud, M. F. M. Salleh, & A. Alnoor, “Wireless sensor network security: A recent review based on state-of-the-art works,” International Journal of Engineering Business Management, vol. 15, pp. 18479790231157220, 2023.
  13. 13.
    C. Dai, & Z. Xu, “A secure three-factor authentication scheme for multi-gateway wireless sensor networks based on elliptic curve cryptography,” Ad Hoc Networks, vol. 127, pp. 102768, 2022.
  14. 14.
    Y. Han, H. Hu, & Y. Guo, “Energy-aware and trust-based secure routing protocol for wireless sensor networks using adaptive genetic algorithm,” IEEE Access, vol. 10, pp. 11538-11550, 2022.
  15. 15.
    B. Pitchaimanickam, & G. Murugaboopathi, “A hybrid firefly algorithm with particle swarm optimization for energy efficient optimal cluster head selection in wireless sensor networks,” Neural Computing and Applications, vol. 32, pp. 7709-7723, 2020.
  16. 16.
    P. Joshi, & A. S. Raghuvanshi, “A multi-objective metaheuristic approach based adaptive clustering and path selection in iot enabled wireless sensor networks,” International Journal of Computer Networks and Applications, vol. 8, no. 5, pp. 566-584, 2021.
  17. 17.
    P. Kathiroli, & K. Selvadurai, “Energy efficient cluster head selection using improved Sparrow Search Algorithm in Wireless Sensor Networks,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 10, pp. 8564-8575, 2022.
  18. 18.
    R. Ramalingam, S. Basheer, P. Balasubramanian, M. Rashid, & G. Jayaraman, “EECHS-ARO: Energy-efficient cluster head selection mechanism for livestock industry using artificial rabbits’ optimization and wireless sensor networks,” Electronic Research Archive, vol. 31, no. 6, 2023.
  19. 19.
    L. Yang, D. Zhang, L. Li, & Q. He, “Energy efficient cluster-based routing protocol for WSN using multi-strategy fusion snake optimizer and minimum spanning tree,” Scientific Reports, vol. 14, no. 1, pp. 16786, 2024.
  20. 20.
    H. Hu, Y. Han, M. Yao, & X. Song, “Trust based secure and energy efficient routing protocol for wireless sensor networks,” IEEE access, vol. 10, pp. 10585-10596, 2021.
  21. 21.
    V. Kavidha, & S. Ananthakumaran, “Novel energy-efficient secure routing protocol for wireless sensor networks with Mobile sink,” Peer-to-Peer Networking and Applications, vol. 12, pp. 881-892, 2019.
  22. 22.
    M. Rathee, S. Kumar, A. H. Gandomi, K. Dilip, B. Balusamy, & R. Patan, “Ant colony optimization-based quality of service aware energy balancing secure routing algorithm for wireless sensor networks. IEEE Transactions on Engineering Management, vol. 68, no. 1, pp. 170-182, 2019.
  23. 23.
    E. P. K. Gilbert, K. Baskaran, E. B. Rajsingh, M. Lydia, & A. I. Selvakumar, “Trust aware nature inspired optimised routing in clustered wireless sensor networks,” International Journal of Bio-Inspired Computation, vol. 14, no. 2, pp. 103-113, 2019.
  24. 24.
    A. Saleh, P. Joshi, R. S. Rathore, & S. S. Sengar, “Trust-aware routing mechanism through an edge node for IoT-enabled sensor networks,” Sensors, vol. 22, no. 20, pp. 7820, 2022.
  25. 25.
    S. Hriez, S. Almajali, H. Elgala, M. Ayyash, & H. B. Salameh, “A novel trust-aware and energy-aware clustering method that uses stochastic fractal search in IoT-enabled wireless sensor networks,” IEEE Systems Journal, vol. 16, no. 2, pp. 2693-2704, 2021.
  26. 26.
    M. Hosseinzadeh, O. H. Ahmed, J. Lansky, S. Mildeova, M. S. Yousefpoor, E. Yousefpoor, & A. M. Rahmani, “A cluster-tree-based trusted routing algorithm using Grasshopper Optimization Algorithm (GOA) in Wireless Sensor Networks (WSNs),” Plos one, vol. 18, no. 9, pp. e0289173, 2023.
  27. 27.
    Cherappa, V., Thangarajan, T., Meenakshi Sundaram, S. S., Hajjej, F., Munusamy, A. K., & Shanmugam, R. (2023). Energy-efficient clustering and routing using ASFO and a cross-layer-based expedient routing protocol for wireless sensor networks. Sensors, 23(5), 2788.
  28. 28.
    Roberts, M. K., & Ramasamy, P. (2023). An improved high performance clustering based routing protocol for wireless sensor networks in IoT. Telecommunication Systems, 82(1), 45-59.
  29. 29.
    Ramalingam, S., Dhanasekaran, S., Sinnasamy, S. S., Salau, A. O., & Alagarsamy, M. (2024). Performance enhancement of efficient clustering and routing protocol for wireless sensor networks using improved elephant herd optimization algorithm. Wireless Networks, 30(3), 1773-1789.
  30. 30.
    Hu, H., Fan, X., & Wang, C. (2024). Energy efficient clustering and routing protocol based on quantum particle swarm optimization and fuzzy logic for wireless sensor networks. Scientific reports, 14(1), 18595.
  31. 31.
    M. Ezhilarasi, L. Gnanaprasanambikai, A. Kousalya, & M. Shanmugapriya, “A novel implementation of routing attack detection scheme by using fuzzy and feed-forward neural networks,” Soft Computing, vol. 27, no. 7, pp. 4157-4168, 2023.
  32. 32.
    A. Pathak, I. Al-Anbagi, & H. J. Hamilton, “An adaptive QoS and trust-based lightweight secure routing algorithm for WSNs,” IEEE Internet of Things Journal, vol. 9, no. 23, pp. 23826-23840, 2022.
  33. 33.
    I. Ashraf, Y. Park, S. Hur, S. W. Kim, R. Alroobaea, Y. B. Zikria, & S. Nosheen, “A survey on cyber security threats in IoT-enabled maritime industry,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 2, pp. 2677-2690, 2022.
  34. 34.
    Y. Fu, D. Liu, J. Chen, & L. He, “Secretary bird optimization algorithm: a new metaheuristic for solving global optimization problems,” Artificial Intelligence Review, vol. 57, no. 5, pp. 1-102, 2024.