Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Design Space Exploration for Energy-Efficient Secure Sensor Network
ASAP '02 Proceedings of the IEEE International Conference on Application-Specific Systems, Architectures, and Processors
Fjording the Stream: An Architecture for Queries Over Streaming Sensor Data
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
A Context-Aware Approach to Conserving Energy in Wireless Sensor Networks
PERCOMW '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications Workshops
Movement-based group awareness with wireless sensor networks
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
Online algorithms for mining inter-stream associations from large sensor networks
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
SO_MAD: SensOr Mining for Anomaly Detection in Railway Data
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
Anomaly detection in monitoring sensor data for preventive maintenance
Expert Systems with Applications: An International Journal
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Frequent radio transmissions among sensors, or from sensors to the basestation, have always been a major energy drain. One of the approaches to reduce the data transmitted to the basestation is to shift the bulk of data processing to networked sensor nodes; for instance, sensors to send only data aggregates to reduce the overall amount of data exchanged. Sensor nodes, however, are quite limited in terms of their energy and processing power, and as such, traditional centralised data mining algorithms are infeasible to be directly implemented on sensors. In this paper, we modify APRIORI to find strong rules from sensor readings in a sensor network and using these rules, autonomously control sensor network operations or supplement sensor operations with a rule knowledge base. For example, triggers activated from the rules could be used to sleep sensors or reduce data transmissions to conserve sensor energy. Our work here includes a detailed implementation of a lightweight rule learning algorithm for a resource-constrainted sensor network, with simulation results for a group node setup running the algorithm.