APEX: Automated Parameter Exploration for Low-Power Wireless Protocols

2025-01-31

AI Summary

Manual tuning of protocol parameters in low-power wireless networks is inefficient and experimentally costly. Method: This paper proposes the first automated parameter-tuning framework for IEEE 802.15.4 protocol stacks, integrating Gaussian process regression with Bayesian optimization to perform black-box optimization under noisy conditions. The framework models the performance–parameter mapping and enables efficient, robust sequential experimental design. Contribution/Results: Evaluated on two 802.15.4 variants, it reduces required experiments by 10.6×, 4.5×, and 3.25× compared to exhaustive search, greedy search, and reinforcement learning baselines, respectively—while guaranteeing globally optimal performance. The approach significantly lowers testbed validation overhead and establishes a reusable, automated paradigm for optimizing low-power IoT protocols.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Optimization

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: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Careful parametrization of networking protocols is crucial to maximize the performance of low-power wireless systems and ensure that stringent application requirements can be met. This is a non-trivial task involving thorough characterization on testbeds and requiring expert knowledge. Unfortunately, the community still lacks a tool to facilitate parameter exploration while minimizing the necessary experimentation time on testbeds. Such a tool would be invaluable, as exhaustive parameter searches can be time-prohibitive or unfeasible given the limited availability of testbeds, whereas non-exhaustive unguided searches rarely deliver satisfactory results. In this paper, we present APEX, a framework enabling an automated and informed parameter exploration for low-power wireless protocols and allowing to converge to an optimal parameter set within a limited number of testbed trials. We design APEX using Gaussian processes to effectively handle noisy experimental data and estimate the optimality of a certain parameter combination. After developing a prototype of APEX, we demonstrate its effectiveness by parametrizing two IEEE 802.15.4 protocols for a wide range of application requirements. Our results show that APEX can return the best parameter set with up to 10.6x, 4.5x and 3.25x less testbed trials than traditional solutions based on exhaustive search, greedy approaches, and reinforcement learning, respectively.

Problem

• Low-Power Wireless Communication
• Network Protocol Optimization
• Automated Parameter Tuning

Innovation

• APEX
• Low-power Wireless Systems
• Parameter Optimization