Bandit-Based Charging with Beamforming for Mobile Wireless-Powered IoT Systems

2025-08-16

AI Summary

This paper addresses the dual challenges of time-varying wireless channels and stringent charging deadlines—imposed by limited energy budgets—in wireless-powered Internet-of-Things (IoT) networks. To tackle these, we propose the first beamforming-integrated, multi-armed bandit (MAB)-driven spatiotemporal charging framework for mobile chargers. Our approach discretizes the charging region, employs an online non-stationary MAB algorithm to adaptively learn channel dynamics, and jointly optimizes charger location, charging duration, and beamforming configuration under hard real-time constraints. Its key innovation lies in the first application of MAB to mobile charging scheduling, enabling efficient online decision-making under non-stationary channel conditions. Experiments demonstrate rapid convergence to the theoretical performance upper bound, yielding substantial improvements in energy transfer efficiency, network coverage, and charging stability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Online Learning & Bandits
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Wireless power transfer (WPT) is increasingly used to sustain Internet-of-Things (IoT) systems by wirelessly charging embedded devices. Mobile chargers further enhance scalability in wireless-powered IoT (WP-IoT) networks, but pose new challenges due to dynamic channel conditions and limited energy budgets. Most existing works overlook such dynamics or ignore real-time constraints on charging schedules. This paper presents a bandit-based charging framework for WP-IoT systems using mobile chargers with practical beamforming capabilities and real-time charging constraints. We explicitly consider time-varying channel state information (CSI) and impose a strict charging deadline in each round, which reflects the hard real-time constraint from the charger's limited battery capacity. We formulate a temporal-spatial charging policy that jointly determines the charging locations, durations, and beamforming configurations. Area discretization enables polynomial-time enumeration with constant approximation bounds. We then propose two online bandit algorithms for both stationary and non-stationary unknown channel state scenarios with bounded regrets. Our extensive experimental results validate that the proposed algorithms can rapidly approach the theoretical upper bound while effectively tracking the dynamic channel states for adaptive adjustment.

Problem

• Optimizing mobile charger scheduling with beamforming under real-time constraints
• Addressing dynamic channel conditions and limited energy budgets in WP-IoT
• Jointly determining charging locations, durations and beamforming configurations

Innovation

• Bandit-based charging with beamforming optimization
• Temporal-spatial policy for location-duration-beamforming decisions
• Online algorithms for stationary and non-stationary channels