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

TL;DR

This paper introduces a bandit-based framework for mobile wireless charging in IoT networks, addressing real-time constraints and dynamic channels with beamforming, and providing algorithms that adaptively optimize charging strategies.

Contribution

It presents a novel online bandit algorithms for real-time, beamforming-enabled wireless charging in IoT, considering dynamic channels and strict deadlines, with proven regret bounds.

Findings

Algorithms rapidly approach theoretical performance bounds.

Effectively track and adapt to dynamic channel states.

Achieve efficient charging schedules under real-time constraints.

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.