Event Correlations in Sensor Networks
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
Efficient mining of association rules from wireless sensor networks
ICACT'09 Proceedings of the 11th international conference on Advanced Communication Technology - Volume 1
Anomaly detection in monitoring sensor data for preventive maintenance
Expert Systems with Applications: An International Journal
Dispersion-based prediction framework for estimating missing values in wireless sensor networks
International Journal of Sensor Networks
A decentralized approach for mining event correlations in distributed system monitoring
Journal of Parallel and Distributed Computing
Mining associated sensor patterns for data stream of wireless sensor networks
Proceedings of the 8th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks
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In this paper, we propose a comprehensive framework for mining Wireless Ad-hoc Sensor Networks (WASNs) that is able to extract patterns regarding the sensors' behaviors. The main goal of determining behavioral patterns is to use these to generate rules that will improve the WASN's Quality of Service by participating in the resource management process or compensating for the undesired side effects of wireless communication. The proposed framework consists of a formal definition of sensor behavioral patterns and sensor association rules, a novel representation structure named the Positional Lexicographic Tree (PLT) that is able to compress the data gathered for the mining process and thus allows the fast and efficient mining of sensor behavioral patterns, as well as a distributed data extraction mechanism to prepare the data required for mining sensor behavioral patterns. To report on the performance of the mining framework, several experiments have been conducted to evaluate the PLT structure and the proposed data extraction mechanism.