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Achieving Fault Tolerance in Data Aggregation in Wireless Sensor Networks

This paper identifies faulty sensor(s) in a polynomial-based data aggregation scenario, TREG proposed in our earlier work. In TREG, function approximation is performed over the entire range of data and only coefficients of a polynomial (P) are passed instead of aggregated data. Performing further mathematical operations on the calculated P can identify the maximum (max) and minimum (min) values of the sensed attribute and their locations. Therefore, if any sensor reports a data value outside the [min, max] range, it can be identified as a faulty sensor. We achieve the following goals: (1) uncorrelated readings from a specific sensor helps in detecting a faulty sensor, (2) faulty sensors are detected near the source and isolated preventing them from affecting the accuracy of the overall aggregated data and reducing the overall delay. Results show that a faulty sensor can be detected with an average accuracy of 94 %. With increase in node density, accuracy in faulty sensor detection improves as more nodes are able to report the information to their nearest tree node.