Authors:
Ch. Rambabu,Srilakshmi Kaza,Syamala Yarlagadda,P.Anil Kumar,DOI NO:
https://doi.org/10.26782/jmcms.2025.12.00002Keywords:
Ant Lion Optimization Algorithm,Multi-Tier Spherical Grid Routing,Network Simulator,Pillar K-means Clustering,Wireless Sensor Networks (WSN),Abstract
The primary factor influencing the wireless sensor network (WSN) is the energy consumption of the sensor node. One of the key factors influencing WSN energy consumption is the high power consumption and packet delivery ratio needed for WSN processing. The suggested Energy Efficient Spherical Grid Routing (EESGR) protocol reduces the node's energy consumption to meet the requirements. To choose the cluster heads, the WSN is clustered into a collection of nodes using the pillar k-means clustering method defined in the proposed protocol. One optimization algorithm inspired by nature, the ant lion, is used to create cluster heads for assessing energy consumption in WSNs. The behavior concept of the ant lion is utilized for choosing the best nodes for the selection of the cluster head. The multi-tier spherical grid routing proposed in the paper is used to grid the cluster head generated by the ant-lion optimization algorithm to evaluate the total energy consumed for processing the sensor network. The overall performance of this method is evaluated in Network Simulator 2 (NS2). The proposed method improves performance in throughput, end-to-end delay, packet delivery ratio, and energy consumption compared to the existing techniques.Refference:
I. Azharuddin and Jana, “Particle swarm optimization for maximizing lifetime of wireless sensor networks”, Computers and Electrical Engineering, vol. 51, pp. 26-42, 2016.
II. Ch. Rambabu, V.V.K.D.V. Prasad and K. Satya Prasad, “A Visiting Center based Energy Efficient Data Collection Method for WSN”, International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878, vol. 8, issue 3, pp.5152-5158, September 2019.
III. Darabkh and Zomot, “An improved cluster head selection algorithm for wireless sensor networks”, Proceedings of the 14th International Wireless Communications and Mobile Computing Conference (IWCMC) IEEE, pp. 65-70, 2018.
IV. El sayed, Sabbeh and Riad, “A distributed fault tolerance mechanism for self-maintenance of clusters in wireless sensor networks”, Arabian Journal for Science and Engineering, vol. 43, issue 12, pp.6891-6907, 2018.
V. G. Xie and F. Pan, “Cluster-based routing for the mobile sink in wireless sensor networks with obstacles”, IEEE Access, vol. 4, pp. 2019-2028, 2016.
VI. Huang, Hong, Zhao and Yuan, “An energy-efficient multi-hop routing protocol based on grid clustering for wireless sensor networks”, Cluster Computing, vol. 20, issue 4, pp.3071-3083, 2017.
VII. Jafarali Jassbi and Moridi, “Fault tolerance and energy efficient clustering algorithm in wireless sensor networks: FTEC”, Wireless Personal Communications, vol. 107, issue 1, pp.373-391, 2019.
VIII. Jiang, C.J., Shi, W.R. and Tang, “Energy-balanced unequal clustering protocol for wireless sensor networks”, The Journal of China Universities of Posts and Telecommunications, vol. 17, issue 4, pp.94-99, 2010.
IX. Kaur and Kumar, “Particle swarm optimization-based unequal and fault tolerant clustering protocol for wireless sensor networks”, IEEE Sensors Journal, vol. 18, issue 11, pp.4614-4622, 2018.
X. Lohani and Varma, “Energy efficient data aggregation in mobile agent based wireless sensor network”, Wireless Personal Communications, vol. 89, issue 4, pp.1165-1176, 2016.
XI. R. Ahmad, R. Wazirali, and T. Abu-Ain, “Machine learning for wireless sensor networks security: An overview of challenges and issues,” Sensors, vol. 22, no. 13, p. 4730, Jun. 2022.
XII. S. Kumari and A. K. Tyagi, “Wireless sensor networks: An introduction,” in Digital Twin and Blockchain for Smart Cities. Beverly, MA, USA : Scrivener Publishing, 2024, pp. 495–528.
XIII. Wang, J., Cao, J., Ji, S. and Park, “Energy-efficient cluster-based dynamic routes adjustment approach for wireless sensor networks with mobile sinks”, The Journal of Supercomputing, vol. 73, issue 7, pp.3277-3290,2017.
XIV. Wang, J., Cao, J., Sherratt, R.S. and Park, “An improved ant colony optimization-based approach with mobile sink for wireless sensor networks”, The Journal of Supercomputing, vol. 74, issue 12, pp.6633-6645. 2018.
XV. W. R. Heinzelman, A. Chandrakasan and H. Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks”, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, pp. 1-10, 2000.