Data streams: algorithms and applications
Foundations and Trends® in Theoretical Computer Science
Data Stream Based Algorithms For Wireless Sensor Network Applications
AINA '07 Proceedings of the 21st International Conference on Advanced Networking and Applications
Information fusion for wireless sensor networks: Methods, models, and classifications
ACM Computing Surveys (CSUR)
Efficient Similarity Search over Future Stream Time Series
IEEE Transactions on Knowledge and Data Engineering
A Cost-Based Approach to Adaptive Resource Management in Data Stream Systems
IEEE Transactions on Knowledge and Data Engineering
Sensor stream reduction for clustered wireless sensor networks
Proceedings of the 2008 ACM symposium on Applied computing
Principal component filter banks for optimal multiresolutionanalysis
IEEE Transactions on Signal Processing
IEEE Communications Magazine
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This work proposes and evaluates a sampling algorithm based on wavelet transforms with Coiflets basis to reduce the data sensed in wireless sensor networks applications. The Coiflets basis is more computationally efficient when data are smooth, which means that, data are well approximated by a polynomial function. As expected, this algorithm reduces the data traffic in wireless sensor network and, consequently, decreases the energy consumption and the delay to delivery the sensed information. The main contribution of this algorithm is the capability to detect some event by adjusting the sampling dynamically. In order to evaluate the algorithm, we compare it with a static sampling strategy considering a real sensing data where an external event is simulated. The results reveal the efficiency of the proposed method by reducing the data without loosing its representativeness, including when some event occurs. This algorithm can be very useful to design energy-efficient and time-constrained sensor networks when it is necessary to detect some event.