TMOALO: A Sensor Node Load Balancing Approach for Power Internet of Things Networks

Abstract The improvement of service quality (QoS) and network lifetime extension are identified as urgent issues due to the widespread adoption of wireless sensor networks (WSNs) in Power Internet of Things (PIoT). During the secondary deployment phase, a typical multi-objective optimization challenge is constituted by how to maximize area coverage while the node movement distance is minimized. Therefore, a novel Tabu-based Multi-Objective Ant Lion Optimization algorithm (TMOALO) is proposed to address this challenge. Initially, a dynamic tabu search operator is designed to prevent the algorithm from being trapped in local optima, and thus the global search capability is enhanced. Subsequently, a layered elite preservation framework is introduced to ensure that high-quality solutions are effectively retained, whereby population diversity is increased. Furthermore, a nonlinear adaptive step-size control regulator is developed to optimize step adjustments, through which the stability and efficiency of the search process are improved. Experiments on a PIoT simulation platform demonstrate that the proposed TMOALO algorithm outperforms conventional methods like MALO, NSGA-II, and MOPSO, achieving an 11.005% increase in area coverage and a 4.118-meter reduction in sensor node movement. These results confirm its superiority in multi-objective optimization.