Optimized Energy-Efficient Cluster Routing in IoT-Enabled Wireless Sensor Networks via Mapdiminution-Based Training and Discovery Algorithm

Alaa A. Hussain, Muthana Naser hussein, Zainab Fahad Alnaseri
International Journal of Computational and Electronic Aspects in Engineering
Volume 6: Issue 2, May 2025, pp 70-80

Author's Information
Alaa A. Hussain 1 
Corresponding Author
1College of Management and Economics, Sumer University, Rifai, Iraq
Alaa91hussain@gmail.com

Muthana Naser Hussein2
2Ministry of Education, Dhi Qar Education Directorate, Iraq

Zainab Fahad Alnaseri3
3College of computer science and information Technology, University of AL-Qadisiyah, Iraq
Research Paper -- Peer Review
First online on – 1 June 2025

Open Access article under Creative Commons License

Cite this article –Alaa A. Hussain, Muthana Naser hussein, Zainab Fahad Alnaseri “Optimized Energy-Efficient Cluster Routing in IoT-Enabled Wireless Sensor Networks via Mapdiminution-Based Training and Discovery Algorithm”, International Journal of Computational and Electronic Aspects in Engineering, RAME Publishers, vol. 6, Issue 2, pp. 70-80, 2025.
https://doi.org/10.26706/ijceae.6.2.20250403

Abstract:-
The combination of WSNs and IoT technologies produces rapid mass-production yet requires energy-efficient routing protocols to fulfill the estimated demands. These protocols need further development to maintain continuous sensor node connections during their operational periods. The design of WSNs encounters a significant problem because sensor nodes have limited energy capabilities. The placement of NSN in specific zones creates difficulties for standard battery replacement and maintenance operations. Network routing for these systems needs energy preservation as their fundamental construction requirement. This paper proposes a Mapdiminution-Based Training-Discovering Optimization Algorithm (MTDOA), a new protocol that aims at increasing energy efficiency for IoT based WSNs. The MTDOA protocol is based on a novel mapdiminution-based dimensionality reduction and a mixed metaheuristic optimization method. This model is intended to allow for a good trade-off between, global and local search during the optimization process in aspects such as selection of efficient heads of clusters and identification of energy optimal routing paths. Thus, these components are therefore combined in the algorithm to cut down on computational cost, ensure faster convergence time and overall lifespan of the sensor network. The MTDOA protocol uses dynamic adaptive training discovery to select cluster heads from nodes based on their energy levels to reduce base station data routing costs. Simulation experiments run in the laboratory showed MTDOA performs better than LEACH and DEEC protocols when measuring network lifetime and average residual energy together with packet delivery ratio. MTDOA methodology enables successful sustainability improvements in WSN networks through its combination of better IoT-based metrics and performance metrics.
Index Terms:-
Wireless Sensor Networks (WSNs), Internet of Things (IoT), Energy Efficiency, Cluster-Based Routing, ‎Metaheuristic Optimization, Mapdiminution, Training-Discovering Algorithm, Network Lifetime, ‎Residual Energy, Packet Delivery Ratio.
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