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 mhawes Al-naseri · International Journal of Computational and Electronic Aspects in Engineering · 2025
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.