An early resource characterisation of wi-fi sensing on residential gateways

Abstract

Recent research has successfully shown brand new models with Wi-Fi signals explaining space dynamics, assessing social environments, and even tracking people's posture, gesture and emotion. However, these models are seldom used in real execution and operating environments, i.e., on residential gateways with networking tasks. In this paper, we present the first, albeit preliminary, measurement study of common Wi-Fi sensing models on a residential gateway. This investigation aims to understand the performance characteristics, resource requirements, and execution bottlenecks for Wi-Fi sensing when being used in parallel with communication tasks. Based on our findings, we propose two optimisation techniques - i) dynamic sampling and ii) dynamic planning of inference execution - for optimum Wi-Fi sensing performance without compromising the quality of communication service. The results and insights lay an empirical foundation for the development of optimisation methods and execution environments that enable sensing models to be more readily integrated into next-generation residential gateways.