An efficient cluster head election based on optimized genetic algorithm for movable sinks in IoT enabled HWSNs

The most crucial design constraint on the Internet of Things (IoT) enabled wireless sensor networks (WSNs) is energy dissipation. The inefficient data collection by the resource constraint sensors becomes a major roadblock in energy preservation to achieve network longevity. The energy of nodes has to be utilized in an efficient manner which helps in increasing the longevity of WSNs. Clustering is a technique that can utilize the energy of the sensors efficiently by maintaining load balancing among the sensors for increasing the lifetime and scalability of the networks. In this paper, the energy consumption of the network is improved by considering the Genetic Algorithm evolutionary computing technique. The proposed OptiGACHS protocol describes the improved cluster head (CH) selection procedure by incorporating criteria of distance, density, energy, and heterogeneous node's capability for developing fitness function. The OptiGACHS protocol operates with single, multiple static, and multiple movable sinks to have an impartial comparative examination. Multiple movable sinks are proposed to shorten transmission distance between the sink and CH and also pact with the hot-spot problem. A deployment strategy for nodes is also discussed for energy and distance optimization during network operation. It is observed from simulations that the proposed OptiGACHS protocol outperforms existing protocols. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper presents an optimized clustering protocol based on genetic algorithm to improve energy consumption in wireless sensor networks, enhancing efficiency and longevity. By efficiently selecting cluster heads and using multiple movable sinks, the proposed protocol outperforms existing ones in simulations.