A Low-Complexity Approach for Improving the Accuracy of Sensor Networks

The paper addresses the problem of improving the accuracy of the measurements collected by a sensor network, where simplicity and cost-effectiveness are of utmost importance. An adaptive Bayesian approach is proposed to this aim, which allows improving the accuracy of the delivered estimates with no significant increase in computational complexity. Remarkably, the resulting cooperative algorithm does not require prior knowledge of the (hyper)parameters and is able to provide a “denoised” version of the monitored field without losing accuracy in detecting extreme (less frequent) values, which can be very important for a number of applications. A novel performance metric is also introduced to suitably quantify the capability to both reduce the measurement error and retain highly-informative characteristics at the same time. The performance assessment shows that the proposed approach is superior to a low-complexity competitor that implements a conventional filtering approach.