Comparative Analysis of Heuristic-based Energy Efficient Protocol for M2M Communications in Green IoT

There is a huge development in the Internet of Things (IoT) that causes massive deployment of sensors and thus automatic reconfigurable energy-efficient protocol for Green IoT is required. Machine-to-Machine (M2M) interactions are one of the core features of IoT. The M2M means the exchange of data among two or more entities, objects or machines, which do not require human interaction. To tackle the sustainability constraints of present IoT models, few developing models are thought to have been promised with new models. However, the fully usage of these models from communication, computing, and data processing for enhancing the energy efficiency of IoT still faces several basic defects. This paper tactics to plan for the comparative analysis on diverse heuristic approaches incorporated with Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol for energy-efficient M2M communication in Green IoT. The minimization of energy consumption by nodes in Green IoT is the main objective of the model. The algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Dargonfly Algorithm (DA), and Deer Hunting Optimization Algorithm (DHOA) is adopted making the comparison part in energy-efficient Green IoT. This energy-efficient model maximizes the network lifetime finally.