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

Angelo Coluccia

International Journal of Distributed Sensor Networks, 2015, vol. 11, issue 6, 521948

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.

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1155/2015/521948 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:11:y:2015:i:6:p:521948

DOI: 10.1155/2015/521948

Access Statistics for this article

More articles in International Journal of Distributed Sensor Networks
Bibliographic data for series maintained by SAGE Publications ().