Programming pearls
Suffix arrays: a new method for on-line string searches
SIAM Journal on Computing
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Directed diffusion: a scalable and robust communication paradigm for sensor networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Adaptive precision setting for cached approximate values
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Maté: a tiny virtual machine for sensor networks
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Exposing resource tradeoffs in region-based communication abstractions for sensor networks
ACM SIGCOMM Computer Communication Review
Towards parameter-free data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Medians and beyond: new aggregation techniques for sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Balancing energy efficiency and quality of aggregate data in sensor networks
The VLDB Journal — The International Journal on Very Large Data Bases
TinyDB: an acquisitional query processing system for sensor networks
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Efficient Online State Tracking Using Sensor Networks
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Towards optimal sleep scheduling in sensor networks for rare-event detection
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Design of a wireless sensor network platform for detecting rare, random, and ephemeral events
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Programming sensor networks using abstract regions
NSDI'04 Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation - Volume 1
A Sensor Network Architecture for Tsunami Detection and Response
International Journal of Distributed Sensor Networks - Selected Papers in Innovations and Real-Time Applications of Distributed Sensor Networks
Escalation: complex event detection in wireless sensor networks
EuroSSC'07 Proceedings of the 2nd European conference on Smart sensing and context
Multi-sensor cross correlation for alarm generation in a deployed sensor network
EuroSSC'07 Proceedings of the 2nd European conference on Smart sensing and context
Complex Event Detection in Extremely Resource-Constrained Wireless Sensor Networks
Mobile Networks and Applications
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We present a novel approach for the on-line detection of Complex Events in Wireless Sensor Networks. Complex Events are sets of data points that correspond to unusual patterns that can not be detected using threshold-based techniques. Our method uses an efficient implementation of SAX, a mature data mining algorithm, that transforms a stream of readings into a symbolic representation. Complex Event Detection is then performed via four alternative modes: (a.) multiple pattern detection using a suffix array, (b.) distance-based comparison, (c.) unknown pattern detection, and (d.) probabilistic detection. The method allows users to specify complex events as patterns or to search for interesting changes without supplying any information. The appropriateness of the approach has been verified by applying it to four sensor data sets. In addition, we have developed an efficient implementation for the TinyOS operating system, and further validated our assertions by collecting and analyzing data in real-time.