LiM-AHP-G-C: Life Time Maximizing based on Analytical Hierarchal Process and Genetic Clustering protocol for the Internet of Things environment

Contemporary smart sensing paradigms, that are provided via diverse Internet of Things (IoT) mechanisms, propagate across considerable domains of daily life, such as health, agriculture, transportation, trade sectors to urban environments and smart cities. The new era of revolution in information technology will rely on processing and analyzing big data that are gathered by tremendous numbers of intelligent sensors, which are disseminated in the surrounding regions. However, most of these devices suffer from restrictions on power resources and processing capabilities, which in turn will enforce stringent restrictions on the network operations. In view of this, the development of novel and efficient energy algorithms for IoT paradigm is a challenging issue bearing in mind that the performance of this state-of-the-art network paradigm cannot be handled effectively by the existing techniques or solutions that are utilized in wireless sensor networks. To meet the requirements of maximizing the IoT network lifetime, we address in this work the challenge of IoT networks as of embedding energy-constrained devices by proposing a novel protocol, namely, Life Time Maximizing Based on Analytical Hierarchal Process and Genetic Clustering (LiM-AHP-G-C) protocol. In particular, the proposed protocol presents a novel optimal clustering algorithm for un-rechargeable battery-powered IoT devices, an efficient IoT heads selection algorithm, a heuristic method for optimal hop selection, and a model for avoiding intra- and inter-cluster interferences for IoT networks. The simulation results show that our proposed protocol outperforms the other existing works in terms of network lifetime, resource utilization, and scalability metrics.