Meta-Heuristic MOALO Algorithm for Energy-Aware Clustering in the Internet of Things

Boosting data transmission rate in IoT with minimized energy is the research issue under consideration in recent days. The main motive of this paper is to transmit the data in the shortest paths to decrease energy consumption and increase throughput in the IoT network. Thus, in this paper, the authors consider delay, traffic rate, and density in designing a multi-objective energy-efficient routing protocol to reduce energy consumption via the shortest paths. First, the authors propose a cluster head picking approach that elects optimal CH. It increases the effective usage of nodes energy and eventually results in prolonged network lifetime with enhanced throughput. The data transmission rate is posed as a fitness function in the multi-objective ant lion optimizer algorithm (MOALOA). The performance of the proposed algorithm is investigated using MATLAB and achieved high convergence, extended lifetime, as well as throughput when compared to representative approaches like E-LEACH, mACO, MFO-ALO, and ALOC.