Recursive Position Estimation in Sensor Networks
ICNP '01 Proceedings of the Ninth International Conference on Network Protocols
Distributed deviation detection in sensor networks
ACM SIGMOD Record
Fault Tolerance in Collaborative Sensor Networks for Target Detection
IEEE Transactions on Computers
IEEE Transactions on Computers
RUGGED: RoUting on finGerprint Gradients in sEnsor Networks
ICPS '04 Proceedings of the The IEEE/ACS International Conference on Pervasive Services
Boundary recognition in sensor networks by topological methods
Proceedings of the 12th annual international conference on Mobile computing and networking
Boundary estimation in sensor networks: theory and methods
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Cooperative boundary detection for spectrum sensing using dedicated wireless sensor networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
Efficient predictive monitoring of wireless sensor networks
International Journal of Autonomous and Adaptive Communications Systems
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With an increasing acceptance of Wireless Sensor Networks (WSNs), the health of individual sensor is becoming critical in identifying important events in the region of interest. One of the key challenges in detecting event in a WSN is how to detect it accurately transmitting minimum information providing sufficient details about the event. At the same time, it is also important to devise a strategy to handle multiple events occurring simultaneously. In this paper, we propose a Polynomial-based scheme that addresses these problems of Event Region Detection (PERD) by having a aggregation tree of sensor nodes. We employ a data aggregation scheme, TREG (proposed in our earlier work) to perform function approximation of the event using a multivariate polynomial regression. Only coefficients of the polynomial (P) are passed instead of aggregated data. PERD includes two components: event recognition and event report with boundary detection. This can be performed for multiple simultaneously occurring events. We also identify faulty sensor(s) using the aggregation tree. 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. Since PERD is implemented over a polynomial tree on a WSN in a distributed manner, it is easily scalable and computation overhead is marginal. Results reveal that event(s) can be detected by PERD with error in detection remaining almost constant achieving a percentage error within a threshold of 10% with increase in communication range. Results also show that a faulty sensor can be detected with an average accuracy of 94% and it increases with increase in node density.