Algorithms for clustering data
Algorithms for clustering data
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Online Data Mining for Co-Evolving Time Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Adaptive filters for continuous queries over distributed data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Compressing historical information in sensor networks
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Power-conserving computation of order-statistics over sensor networks
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
BRAID: stream mining through group lag correlations
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Data Streams: Models and Algorithms (Advances in Database Systems)
Data Streams: Models and Algorithms (Advances in Database Systems)
A prediction error-based hypothesis testing method for sensor data acquisition
ACM Transactions on Sensor Networks (TOSN)
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Near-optimal observation selection using submodular functions
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Online distributed sensor selection
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
Distributed dynamic data driven prediction based on reinforcement learning approach
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Sensor nodes have limited local storage, computational power, and battery life, as a result of which it is desirable to minimize the storage, processing and communication from these nodes during data collection. The problem is further magnified by the large volumes of data collected. In real applications, sensor streams are often highly correlated with one another or may have other kinds of functional dependencies. For example, a group of sound sensors in a given geographical proximity may pick almost the same set of signals. Clearly, since there are considerable functional dependencies between different sensors, there are huge redundancies in the data collected by sensors. These redundancies may also change as the data evolve over time. In this paper, we discuss real time algorithms for reducing the volume of the data collected in sensor networks. The broad idea is to determine the functional dependencies between sensor streams efficiently in real time, and actively collect the data only from a minimal set of sensors. The remaining sensors collect the data passively at low sampling rates in order to detect any changing trends in the underlying data. We present real time algorithms in order to minimize the power consumption in reducing the data collected and show that the resulting data retains almost the same amount of information at a much lower cost.