Adaptive Budgeted Multi-Armed Bandits for IoT with Dynamic Resource Constraints

TL;DR

This paper introduces a novel adaptive learning framework for IoT devices that manages fluctuating resource constraints over time, balancing performance and compliance through a decaying violation budget.

Contribution

It proposes a Budgeted UCB algorithm with theoretical guarantees for sublinear regret and constraint violations in dynamic IoT environments.

Findings

Faster adaptation in wireless communication simulations

Better constraint satisfaction than standard methods

Achieves sublinear regret and logarithmic violations

Abstract

Internet of Things (IoT) systems increasingly operate in environments where devices must respond in real time while managing fluctuating resource constraints, including energy and bandwidth. Yet, current approaches often fall short in addressing scenarios where operational constraints evolve over time. To address these limitations, we propose a novel Budgeted Multi-Armed Bandit framework tailored for IoT applications with dynamic operational limits. Our model introduces a decaying violation budget, which permits limited constraint violations early in the learning process and gradually enforces stricter compliance over time. We present the Budgeted Upper Confidence Bound (UCB) algorithm, which adaptively balances performance optimization and compliance with time-varying constraints. We provide theoretical guarantees showing that Budgeted UCB achieves sublinear regret and logarithmic constraint violations over the learning horizon. Extensive simulations in a wireless communication setting show that our approach achieves faster adaptation and better constraint satisfaction than standard online learning methods. These results highlight the framework's potential for building adaptive, resource-aware IoT systems.