The International Arab Journal of Information Technology (IAJIT)


TSO Clustered Protocol to Extend Lifetime of IoT Based Mobile Wireless Sensor Networks

Mobile Wireless Sensor Networks (MWSNs) energy utilization is the most important trouble in recent years various research works going related to it. Clustering approaches are most proficient methods to accomplish the energy utilization. Cluster Heads (CHs) determination is a significant task in MWSNs as it utilizes huge energy while receiving, broadcasting, capturing the data from IoT nodes and broadcast it to the Basestation (BS). Inappropriate choice of CHs utilizes energy so that diminishes network existence. An energy resourceful network with appropriate optimization methodology is to be espoused to determine the CHs. A clustered methodology is proposed based on Tiger Swarm Optimization (TSO) approach to diminish the energy spending throughout cluster formation and broadcast stage. TSO clustered approach is established to consider parameters as intra cluster remoteness among of sensors to CH and lingering energy of sensors. The approach is experimented broadly on diverse environments, unstable sensors and CHs. The proposed TSO is evaluated with Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO) and Multi-objective Hybrid Genetic Algorithm (MHGA) based on data delivery, delay, lingering energy are simulated in ns2.

  1. Adumbabu I. and Selvakumar K., “Energy Efficient Routing and Dynamic Cluster Head Selection Using Enhanced Optimization Algorithms for Wireless Sensor Networks,” Energies, vol. 15, no. 21, 2022.
  2. Amutha J., Sandeep S., and Sharma S., “An Energy Efficient Cluster Based Hybrid Optimization Algorithm with Static Sink and Mobile Sink Node for Wireless Sensor Networks,” Expert Systems with Applications, vol. 203, pp. 117334, 2022.
  3. Arya G., Bagwari A., and Chauhan D., “Performance Analysis of Deep Learning Based Routing Protocol for an Efficient Data Transmission in 5G WSN Communication,” IEEE Access, vol. 10, pp. 9340-9356, 2022. doi. 10.1109/ACCESS.2022.3142082
  4. Bhaskarwar R. and Pete D., “Energy Efficient Cluster-Based Routing Scheme Using Type-2 Fuzzy Logic In Underwater Wireless Sensor Networks” International Journal of Communication Networks and Distributed Systems, vol. 28, no. 5, pp. 499-5, 2022.
  5. Canli H. and Toklu S., “AVL Based Settlement Algorithm and Reservation System for Smart Parking Systems in IoT-based Smart Cities,” The International Arab Journal of Information Technology, vol. 19, no. 5, pp. 793-801, 2022.
  6. Gad A., “Particle Swarm Optimization Algorithm and its Applications: A Systematic Review,” Archives of Computational Methods in Engineering, vol. 29, pp. 2531-2561, 2022.
  7. Huang X., Zeng T., and Li M., “A Particle Swarm Optimization Algorithm with Gradient Perturbation and Binary Tree Depth First Search Strategy,” Journal of Mathematics, vol. 2022, 2022.
  8. Huibin X. and Mengjia Z., “An Energy-Efficient Clustering Routing for Wireless Sensor Networks Based on Energy Consumption Optimization,” International Journal of Digital Multimedia Broadcasting, vol. 2022, 2022.
  9. Ismail A., Wang X., Hawbani A., Alsamhi S., and Aziz S., “Routing Protocols Classification for Underwater Wireless Sensor Networks Based on Localization and Mobility,” Wireless Networks, vol. 28, no. 2, pp. 797-826, 2022.
  10. Jaffri Z., Asif M., Khan W., Ahmad Z., Akhtar Z., Ullah K., and Ali M., “TEZEM: A New Energy-Efficient Routing Protocol for Next-Generation Wireless Sensor Networks,” International Journal of Distributed Sensor Networks, vol. 18, no. 6, 2022. DOI:10.1177/15501329221107246
  11. Kiruba D. and Benita J., “A Survey of Secured Cluster Head: SCH Based Routing Scheme for IOT Based Mobile Wireless Sensor Network,” ECS Transactions, vol. 107, no. 1, pp. 16725-16745, 2022. doi.10.1149/10701.16725ecst.
  12. Kumar V., Jayapandian N., and Balasubramanie P., “Energy-Efficient Cluster in Wireless Sensor Network Using Life Time Delay Clustering Algorithms,” Computer Systems Science and Engineering, vol. 43, no. 1, pp. 77-86, 2022.
  13. Li S., Xu H., Zhao S., Han S., and Yan L., “An Adaptive Multi-Zone Geographic Routing Protocol for Underwater Acoustic Sensor Networks,” Wireless Networks, vol. 28, no. 1, pp. 209-223, 2022.
  14. Maghawry A., Hodhod R., Omar Y., and Kholief M., “An Approach for Optimizing Multi-Objective Problems Using Hybrid Genetic Algorithms,” Soft Computing, vol. 25, pp. 389-405, 2021.
  15. Malisetti N. and Pamula V., “Energy Efficient Cluster Based Routing for Wireless Sensor Networks using Moth Levy Adopted Artificial Electric Field Algorithm and Customized Grey Wolf Optimization Algorithm,” Microprocessors and Microsystems, vol. 93, pp. 104593, 2022.
  16. Natesan G., Konda S., Pérez de Prado R., and Wozniak M., “A Hybrid Mayfly-Aquila Optimization Algorithm Based Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks,” Sensors, vol. 22, no. 17, pp. 6405, 2022,
  17. Rajesh D., “Energy-Resourceful Routing by Fuzzy Based Secured CH Clustering for Smart Dust,” International Journal of Electrical and Electronics Research, vol. 10, no. 3, pp. 659-663, 2022. DOI:10.37391/ijeer.100340
  18. Rajesh D. and Jaya T., “A Mathematical Model for Energy Efficient Secured CH Clustering Protocol for Mobile Wireless Sensor Network,” Wireless Personal Communications, vol. 112, no. 1, pp. 421-438, 2020.
  19. Rajesh D. and Jaya T., “ECIGC-MWSN: Energy Capable Information Gathering in Clustered Secured CH Based Routing in MWSN,” Materials Today: Proceedings, vol. 43, pp. 3457-3462, 2021.
  20. Rajesh D. and Jaya T., “Energy Competent Cluster-Based Secured CH Routing EC2SR Protocol for Mobile Wireless Sensor Network,” Concurrency and Computation: Practice and Experience. vol. 34, no. 1, pp. e6525, 2022.
  21. Rajesh D. and Jaya T., “Exploration on Cluster Related Energy Proficient Routing in Mobile Wireless Sensor Network,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 4, pp. 93-97, 2019.
  22. Rajesh D. and Kiruba D., “A Comparative Study on Energy Efficient Secured Clustered Approaches for IOT Based MWSN,” Suranaree Journal of Science and Technology, vol. 29, no. 4, pp. 1-18, 2022.
  23. Rajesh D. and Kiruba D., “A Probability-Based Energy Competent Cluster Based Secured CH Selection Routing EC2SR Protocol for Smart Dust,” Peer-to-Peer Networking Applications, vol. 14, pp. 1976-1987, 2021.
  24. Rajesh D. and Kiruba G., “Energy Efficient Secured CH Clustered Routing (E2SCR) in Smart Dust Network,” Journal of Intelligent and Fuzzy Systems, vol. 43, no. 6, pp. 8349-8357, 2022. DOI: 10.3233/JIFS-212012
  25. Rajesh D. and Rajanna G., “Energy Aware Data Harvesting Strategy Based on Optimal Node Selection for Extended Network Lifecycle in Smart Dust,” Journal of Intelligent and Fuzzy Systems, vol. 44, no. 1, pp. 939-949, 2023. DOI: 10.3233/JIFS-221719
  26. Rajesh D. and Rajanna G., “CSCRT Protocol with Energy Efficient Secured CH Clustering For Smart Dust Network Using Quantum Key Distribution,” International Journal of Safety and Security Engineering, vol. 12, no. 4, pp. 441-448, 2022. DOI:
  27. Sharma N. and Gupta V., “A Framework for Wireless Sensor Network Optimization Using Fuzzy-Based Fractal Clustering to Enhance Energy Efficiency” Journal of Circuits, Systems and Computers, vol. 31, no. 13, pp. 2250223, 2022.
  28. Shyjith M., Maheswaran C., and Reshma V., “Optimized and Dynamic Selection of Cluster Head Using Energy Efficient Routing Protocol in WSN,” Wireless Personal Communications, vol. 116, pp. 577-599, 2021.
  29. Singh J., Deepika J., Zaheeruddin J., Bhat S., Kumararaja V., Vikram R., Amalraj J., Saravanan V., and Sakthivel S., “Energy-Efficient Clustering and Routing Algorithm Using Hybrid Fuzzy with Grey Wolf Optimization in Wireless Sensor Networks,” Security and Communication Networks, vol. 2022, pp. 9846601, 2022.
  30. Songyang Li., Haipeng Yu., and Miao M., “Cat Swarm Optimization Algorithm Based On the Information Interaction of Subgroup and the Top-N Learning Strategy,” Journal of Intelligent Systems, vol. 31, no. 1, pp. 489-500, 2022.
  31. Zanin P., Negrete L., Gelson A., and López-Lezama J., “A Multi-Objective Hybrid Genetic Algorithm for Sizing and Siting of Renewable Distributed Generation,” Applied Sciences, vol. 11, no. 16, pp. 7442, 2021.