De Paola, A, Lo Re, G, Milazzo, F and Ortolani, M (2013) QoS-Aware Fault Detection in Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 9 (9). ISSN 1550-1329

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Wireless sensor networks (WSNs) are a fundamental building block of many pervasive applications. Nevertheless the use of such technology raises new challenges regarding the development of reliable and fault-tolerant systems. One of the most critical issues is the detection of corrupted readings amidst the huge amount of gathered sensory data. Indeed, such readings could significantly affect the quality of service (QoS) of the WSN, and thus it is highly desirable to automatically discard them. This issue is usually addressed through “fault detection” algorithms that classify readings by exploiting temporal and spatial correlations. Generally, these algorithms do not take into account QoS requirements other than the classification accuracy. This paper proposes a fully distributed algorithm for detecting data faults, taking into account the response time besides the classification accuracy. We adopt the Bayesian networks to perform classification of readings and the Pareto optimization to allow QoS requirements to be simultaneously satisfied. Our approach has been tested on a synthetic dataset in order to evaluate its behavior with respect to different values of QoS constraints. The experimental evaluation produced good results, showing that our algorithm is able to greatly reduce the response time at the cost of a small reduction in classification accuracy.

Item Type: Article
Additional Information: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Depositing User: Symplectic
Date Deposited: 01 Apr 2019 10:34
Last Modified: 04 Apr 2019 12:55

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