Sensing, Compression and Recovery for Wireless Sensor Networks: Monitoring Framework Design

We address the problem of compressing large and distributed signals monitored by a Wireless Sensor Network (WSN) and recovering them through the collection of a small number of samples (sub-sampling) at the Data Collection Point (DCP). To this end, we propose a novel framework, namely, SCoRe1: Sensing, Compression and Recovery through ON-line Estimation for WSNs. SCoRe1 is very general as it does not require ad-hoc parameter tuning by the user and is able to self-adapt to unpredictable changes in the signal statistics. A feedback control loop is accounted for to estimate, in an on-line fashion, the signal reconstruction error and to react accordingly in order to keep such error bounded. For the actual recovery of the sub-sampled signal, our framework accommodates diverse interpolation techniques, by framing them into the same general algorithm. As a further original contribution of this paper, we integrate a novel signal interpolation method based on Compressive Sensing (CS) into our framework. Specifically, this technique exploits Principal Component Analysis (PCA) for on-line learning of the signal statistics and CS for recovering the sub-sampled signal through convex optimization. Also, we perform an extensive validation of the proposed framework when used in conjunction with CS as well as with standard interpolation techniques, testing its performance for real world signals. Besides validating our framework, these results have the merit of shedding new light on the performance limits of CS when used as a recovery tool in WSNs. Finally, we note that although SCoRe1 is proposed for WSNs, it can be readily applied to other types of network infrastructures that require the approximation of large and distributed signals datasets showing spatial and/or temporal correlation