APEX: Automated Parameter Exploration for Low-Power Wireless Protocols

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 limited testbed availability, whereas non-exhaustive unguided searches rarely deliver satisfactory results. In this article, we present APEX, a framework enabling an automated and informed parameter exploration for low-power wireless protocols and allowing convergence to the best 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 parameter combination. After developing a prototype of APEX, we demonstrate its effectiveness by parametrizing two IEEE 802.15.4 protocols across a wide range of application requirements. Our results show that APEX can return the best parameter set with up to 10.6×, 4.5×, 4.3×, and 3.25× less testbed trials than traditional solutions based on exhaustive search, greedy approaches, support vector regression and reinforcement learning, respectively.