Adaptive mean center of mass particle swarm optimizer for auto-localization in 3D wireless sensor networks

Abstract

Wireless Sensor Networks (WSNs) have become instrumental in environmental monitoring, healthcare, agriculture, and industrial automation. In WSNs, the precise localization of sensor nodes is crucial for informed decision-making and network efficiency. This study explores localization in the context of WSNs, focusing on the 6LoWPAN and Zigbee protocols. These protocols are vital for integrating WSNs into the Internet of Things (IoT). We highlight the significance of spatial node distribution and WSNs' challenges, such as resource limitations and signal interference. We emphasize range-based methods due to their accuracy. We propose the Adaptive Mean Center of Mass Particle Swarm Optimizer (AMCMPSO) to address these. Inspired by the center of mass principle, this algorithm adapts parameters for enhanced localization on regular and irregular surfaces. AMCMPSO leverages the principle of the center of mass and mean values to enhance the efficiency of sensor node localization. The algorithm incorporates adaptive parameters, including inertia weight and acceleration coefficients, to improve search efficiency and convergence speed. Our simulations demonstrate the superior performance of AMCMPSO, with an average improvement rate of 99.86%. Moreover, the localization error is consistently below 1.34 cm, ensuring precise spatial awareness. In 3D environments, AMCMPSO consistently delivers coverage rates exceeding 87%, even in challenging scenarios.