International Journal of Computer Networks and Applications (IJCNA)

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ISSN : 2395-0455

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International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Energy Efficient Cluster Based Routing Using Multiobjective Improved Golden Jackal Optimization Algorithm in Wireless Sensor Networks

Author NameAuthor Details

Poornima, T R Muhibur Rahman, Nagaraj B Patil

Poornima[1]

T R Muhibur Rahman[2]

Nagaraj B Patil[3]

[1]Department of Computer Science and Engineering, Ballari Institute of Technology and Management (Affiliated to Visvesvaraya Technological University, Belagavi), Ballari, Karnataka, India.

[2]Department of Computer Science and Engineering, Ballari Institute of Technology and Management (Affiliated to Visvesvaraya Technological University, Belagavi, Ballari, Karnataka, India.

[3]Department of Computer Science and Engineering, Government Engineering College (Affiliated to Visvesvaraya Technological University, Belagavi), Huvinahadagali, Karnataka, India.

Cite this Article

DOI: 10.22247/ijcna/2024/19

Pages : 304-315

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Abstract

In recent decades, the Wireless Sensor Networks (WSNs) have played a prominent role in different fields because of cost efficiency and energy efficiency. However, sensor nodes deployed in WSNs are generated by batteries which may drain all their energy after a certain period. The process of clustering assists in enhancing network lifespan thereby minimizing an energy consumption. A lifetime expectancy of WSNs can be improvised by selecting the optimal Cluster Head (CH) and optimal shortest path to transmit data packets. A maintenance of energy efficiency in WSN is a challenging process due to constrained sources that cannot be operated for a longer time. So, this research focuses on energy efficiency and introduces the Multiobjective Improved Golden Jackal Optimization Algorithm (MIGJOA). The MIGJOA helps to choose CHs and optimal routing path to transmit data. A fitness objectives like the distance among neighbor node and Base Station (BS), distance between BS and CH, node degree, and mean node energy are employed as fitness functions to select optimal CHs. The efficiency of the suggested technique is assessed with Adaptive Blackhole Tuna Swarm Optimization (ABTSO), Hybrid African Vultures Cuckoo Search Optimization (HAVCSO), Butterfly Optimization Algorithm-Ant Colony Optimization (BOA-ACO) based on alive nodes, normalized energy and consumption of average energy. The alive nodes of proposed approach when a number of rounds is 2500 is 97 whereas the alive node count in the existing BOA-ACO is 89.

Index Terms

Cluster-Based Routing

Multi-Objective Improved Golden Jackal Optimization

Energy Efficiency

Life Expectancy

Wireless Sensor Network

Reference

  1. 1.
    B. M. Sahoo, H. M. Pandey, and T. Amgoth, “GAPSO-H: A hybrid approach towards optimizing the cluster based routing in wireless sensor network,” Swarm and Evolutionary Computation, vol. 60, p. 100772, 2021.
  2. 2.
    X. Xue, R. Shanmugam, S. Palanisamy, O. I. Khalaf, D. Selvaraj, and G. M. Abdulsahib, “A Hybrid Cross Layer with Harris-Hawk-Optimization-Based Efficient Routing for Wireless Sensor Networks,” Symmetry, vol. 15, no. 2, p. 438, 2023.
  3. 3.
    E. Heidari, A. Movaghar, H. Motameni, and B. Barzegar, “A novel approach for clustering and routing in WSN using genetic algorithm and equilibrium optimizer,” International Journal of Communication Systems, vol. 35, no. 10, p. e5148, 2022.
  4. 4.
    S. Kumar and R. Agrawal, “A hybrid C-GSA optimization routing algorithm for energy-efficient wireless sensor network,” Wireless Networks, vol. 29, no. 5, pp. 2279–2292, 2023.
  5. 5.
    M. Rami Reddy, M. L. Ravi Chandra, P. Venkatramana, and R. Dilli, “Energy-Efficient Cluster Head Selection in Wireless Sensor Networks Using an Improved Grey Wolf Optimization Algorithm,” Computers, vol. 12, no. 2, p. 35, 2023.
  6. 6.
    S. Bharany, S. Sharma, N. Alsharabi, E. Tag Eldin, and N. A. Ghamry, “Energy-efficient clustering protocol for underwater wireless sensor networks using optimized glowworm swarm optimization,” Frontiers in Marine Science, vol. 10, p. 1117787, 2023.
  7. 7.
    Z. Wang, H. Ding, B. Li, L. Bao, Z. Yang, and Q. Liu, “Energy Efficient Cluster Based Routing Protocol for WSN Using Firefly Algorithm and Ant Colony Optimization,” Wireless Personal Communications, vol. 125, no. 3, pp. 2167–2200, 2022.
  8. 8.
    J. Sumathi and R. L. Velusamy, “A review on distributed cluster based routing approaches in mobile wireless sensor networks,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 1, pp. 835–849, 2021.
  9. 9.
    R. Mishra and R. K. Yadav, “Energy Efficient Cluster-Based Routing Protocol for WSN Using Nature Inspired Algorithm,” Wireless Personal Communications, vol. 130, no. 4, pp. 2407–2440, 2023.
  10. 10.
    G. Natesan, S. Konda, R. De Prado, and M. Wozniak, “A Hybrid Mayfly-Aquila Optimization Algorithm Based Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks,” Sensors, vol. 22, no. 17, p. 6405, 2022.
  11. 11.
    M. Kingston Roberts and J. Thangavel, “An improved optimal energy aware data availability approach for secure clustering and routing in wireless sensor networks,” Transactions on Emerging Telecommunications Technologies, vol. 34, no. 3, p. e4711, 2023.
  12. 12.
    K. Lakshmanna, N. Subramani, Y. Alotaibi, S. Alghamdi, O. I. Khalafand, and A. K. Nanda, “Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks,” Sustainability, vol. 14, no. 13, p. 7712, 2022.
  13. 13.
    V. Cherappa, T. Thangarajan, S. S. Meenakshi Sundaram, F. Hajjej, A. K. Munusamy, and R. Shanmugam, “Energy-Efficient Clustering and Routing Using ASFO and a Cross-Layer-Based Expedient Routing Protocol for Wireless Sensor Networks,” Sensors, vol. 23, no. 5, p. 2788, 2023.
  14. 14.
    A. Balamurugan, S. Janakiraman, M. D. Priya, and A. C. J. Malar, “Hybrid Marine predators optimization and improved particle swarm optimization-based optimal cluster routing in wireless sensor networks (WSNs),” China Communications, vol. 19, no. 6, pp. 219–247, 2022.
  15. 15.
    P. C. S. Rao, P. Lalwani, H. Banka, and G. S. N. Rao, “Competitive swarm optimization based unequal clustering and routing algorithms (CSO-UCRA) for wireless sensor networks,” Multimedia Tools and Applications, vol. 80, no. 17, pp. 26093–26119, 2021.
  16. 16.
    R. Sheeja, M. M. Iqbal, and C. Sivasankar, “Multi-objective-derived energy efficient routing in wireless sensor network using adaptive black hole-tuna swarm optimization strategy,” Ad Hoc Networks, vol. 144, p. 103140, 2023.
  17. 17.
    A. Asha, R. A, N. Verma, and I. Poonguzhali, “Multi?objective?derived energy efficient routing in wireless sensor networks using hybrid African vultures?cuckoo search optimization,” International Journal of Communication Systems, vol. 36, no. 6, p. e5438, 2023.
  18. 18.
    A. Srinivasa Gowda and N. M. Annamalai, “Hybrid salp swarm–firefly algorithm?based routing protocol in wireless multimedia sensor networks,” International Journal of Communication Systems, vol. 34, no. 3, p. e4633, 2021.
  19. 19.
    C. Jothikumar, K. Ramana, V. D. Chakravarthy, S. Singh, and I.-H. Ra, “An Efficient Routing Approach to Maximize the Lifetime of IoT-Based Wireless Sensor Networks in 5G and Beyond,” Mobile Information Systems, vol. 2021, pp. 1–11, 2021.
  20. 20.
    P. Maheshwari, A. K. Sharma, and K. Verma, “Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization,” Ad Hoc Networks, vol. 110, p. 102317, 2021.
  21. 21.
    S. S. Kalburgi and M. Manimozhi, “Taylor-spotted hyena optimization algorithm for reliable and energy-efficient cluster head selection based secure data routing and failure tolerance in WSN,” Multimedia Tools and Applications, vol. 81, no. 11, pp. 15815–15839, 2022.
  22. 22.
    S. Ramalingam, S. Dhanasekaran, S. S. Sinnasamy, A. O. Salau, and M. Alagarsamy, “Performance enhancement of efficient clustering and routing protocol for wireless sensor networks using improved elephant herd optimization algorithm,” Wireless Networks, 2024.
  23. 23.
    S. Al-Otaibi, V. Cherappa, T. Thangarajan, R. Shanmugam, P. Ananth, and S. Arulswamy, “Hybrid K-medoids with energy-efficient sunflower optimization algorithm for wireless sensor networks,” Sustainability, vol. 15, no. 7, p. 5759, 2023.
  24. 24.
    S. Janakiraman, “Energy efficient clustering protocol using hybrid bald eagle search optimization algorithm for improving network longevity in WSNs,” Multimedia Tools and Applications, 2024.
  25. 25.
    R. Govardanagiri and V. Sanjeevulu, “Hybrid Grasshopper and Improved Bat Optimization Algorithms-based clustering scheme for maximizing lifetime of Wireless Sensor Networks (WSNs),” International Journal of Intelligent Engineering & Systems, vol. 15, no. 3, pp. 536–546, 2022.
  26. 26.
    N. Chopra and M. Mohsin Ansari, “Golden jackal optimization: A novel nature-inspired optimizer for engineering applications,” Expert Systems with Applications, vol. 198, p. 116924, Jul. 2022.
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