Bio-Inspired Based Base Station Placement in IoT Sensor Network for Energy Efficiency and Latency Minimization

Abstract

The Internet of Things (IoT) is one of the latest technologies that connects objects, people and surroundings by means of intelligent networks so that it is able to collect and transmit data in real-time. Of the many applications of the IoT, some rely on autonomous devices deployed in complex or even hostile environments to provide continuous monitoring or observation. Wireless sensor networks (WSNs) are widely used in this context. However, these networks face a crucial challenge: extending their lifespan by optimising the parameters that have a direct impact on energy consumption, in particular the position of the base station, the placement of which has a significant influence on the energy load of the sensors. The use of bio-inspired approaches, based on the behaviour of natural phenomena, is a promising way forward. These methods can be used to generate adaptive and resilient solutions to complex optimisation problems. In this context, we propose three solutions aimed at reducing energy consumption while maintaining good performance in terms of latency. The first approach is based on a hybrid between the Particle Swarm Optimisation (PSO) algorithm and the Genetic Algorithm (GA), exploiting both the exploration capabilities of the PSO and the controlled randomness of the GA to avoid local optima. Next, we introduce an improved version of the Whale Optimisation Algorithm (WOA), enriched by a personalised initialisation of the population. Finally, a hybrid approach combining WOA and PSO is proposed to benefit from their respective advantages. Our contributions are evaluated using extensive simulations and compared with other bio-inspired algorithms. The results obtained are promising in terms of energy efficiency and network lifetime extension, while maintaining good performance in terms of latency.