Leveraging live machine learning and deep sleep to support a self-adaptive efficient configuration of battery powered sensors

Sensor networks empower Internet of Things (IoT) applications by connecting them to physical world measurements. However, the necessary use of limited bandwidth networks and battery-powered devices makes their optimal configuration challenging. An over-usage of periodic sensors (i.e. too frequent measurements) may easily lead to network congestion or battery drain effects, and conversely, a lower usage is likely to cause poor measurement quality. In this paper we propose a middleware that continuously generates and exposes to the sensor network an energy-efficient sensors configuration based on data live observations. Using a live learning process, our contributions dynamically act on two configuration points: (i) sensors sampling frequency, which is optimized based on machine-learning predictability from previous measurements, (ii) network usage optimization according to the frequency of requests from deployed software applications. As a major outcome, we obtain a self-adaptive platform with an extended sensors battery life while ensuring a proper level of data quality and freshness. Through theoretical and experimental assessments, we demonstrate the capacity of our approach to constantly find a near-optimal tradeoff between sensors and network usage, and measurement quality. In our experimental validation, we have successfully scaled up the battery lifetime of a temperature sensor from a monthly to a yearly basis.