Abstract
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.
Highlights
Introduction and MotivationsIn the last years, sensor networks have started to be deployed for an increasing number of different applications [1]
Typical applications are sensing/ estimation of some parameters [3, 4] such as temperature, pollution level [5], electromagnetic exposure [6, 7], or field reconstruction [8, 9]. Such problems are important in environmental monitoring [10], ecology [11], meteorology, agriculture, and related fields as reported in a number of case studies [12, 13]; see [14] and references therein
I = 1, . . . , N, Sensor node Fusion center Possible links Monitored field the measurement at sensor i relative to an unknown local parameter mi; that is, xi = mi + εi, (1)
Summary
Dipartimento di Ingegneria dell’Innovazione, Universitadel Salento, Via Monteroni, 73100 Lecce, Italy. 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. 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 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
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