Lean Sensing: Exploiting Contextual Information for Most Energy-Efficient Sensing


Cyber-physical technologies enable event-driven applications, which monitor in real-time the occurrence of certain inherently stochastic incidents. Those technologies are being widely deployed in cities around the world and one in all their vital aspects is energy consumption, as they're mostly battery powered. The most representative samples of such applications these days is smart parking. Since parking sensors are devoted to detect parking events in nearly-real time, strategies like data aggregation aren't like minded to optimize energy consumption. Furthermore, data compression is pointless, as events are basically binary entities. Therefore, this paper introduces the concept of Lean Sensing, which enables the comfort of sensing accuracy at the advantage of improved operational costs. To this finish, this paper departs from the concept of instantaneous randomness and it explores the correlation structure that emerges from it in complicated systems. Then, it examines the utilization of this system-wide aggregated contextual information to optimize power consumption, so going in the other method; from the system-level representation to individual device power consumption. The mentioned techniques embrace customizing the info acquisition to temporal correlations (i.e, to adapt sensor behavior to the expected activity) and inferring the system-state from incomplete information based on spatial correlations. These techniques are applied to real-world smart-parking application deployments, aiming to guage the impact that a range of system-level optimization methods have on devices power consumption.

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