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

arXiv: https://arxiv.org/abs/2505.02640 · HTML: https://mlsanity.com/paper/2505.02640/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 5 May 2025.

## Builds on (most cited first, 4 of 4)

- [Bandits with Knapsacks](https://mlsanity.com/paper/1305.2545/) (2013)
- [Online Convex Optimization with Time-Varying Constraints](https://mlsanity.com/paper/1702.04783/) (2017)
- [Stage-wise Conservative Linear Bandits](https://mlsanity.com/paper/2010.00081/) (2020)
- [Bandit Convex Optimization for Scalable and Dynamic IoT Management](https://mlsanity.com/paper/1707.09060/) (2017)

## Built on by (most cited first, 1 of 1)

- [Socio-technical aspects of Agentic AI](https://mlsanity.com/paper/2601.06064/) (2025)

## Abstract (arXiv)

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.