Environment-Aware Mobile Charging with Attenuation Effects

Wireless Rechargeable Sensor Networks (WRSNs) have emerged as a promising solution to address the energy limitations of wireless sensor networks. However, existing charging optimization methods often assume idealized free-space propagation, neglecting real-world attenuation effects such as variable path loss, shadowing, and multipath fading. This results in overestimated charging efficiency, inaccurate power predictions, and reduced network reliability. In this paper, we focus on optimizing mobile charging and formalize the charging utility maximization problem with attenuation effects (termed OPTIC problem). First, we propose an environment-aware charging model that comprehensively incorporates statistical path loss variations, stochastic shadowing, and multipath fading effects. Then, we construct an attenuation map that partitions the environment into regions with distinct physical characteristics and corrects the theoretical model through statistical analysis of spatial attenuation variations. Additionally, we discretize the charging region and reformulate the OPTIC problem as a constrained submodular maximization problem, which can be solved by a proposed algorithm with an approximation guarantee. Extensive simulations and field experiments demonstrate that our algorithm improves charging utility by 44.3% on average.