Energy Optimization in IoT Network Through Metaheuristic Approach

The expansion of the Internet of Things (IoT) network introduces several challenges, including security, energy management, and resource allocation. This paper concentrates primarily on managing the energy of IoT devices, assuming that the IoT nodes are deployed in remote and hostile environments. The sensor nodes in the IoT network are devices with limited resources. Proper energy management of these nodes can extend the network’s lifespan. In this context, we propose a model that integrates the ant colony optimization with the traveling salesman problem with low iteration policy. The proposed Low Iterative ACO with TSP (LIAT) algorithm allocates the resources in the IoT network to all nodes while minimizing cost or energy. Simulation results indicate that the LIAT algorithm outperform then traditional ACO and IACAEO algorithms with 71.2% and $\mathbf{7 6. 3 \%}$ respectively.