Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Adaptive filters for continuous queries over distributed data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Near-Optimal Node Clustering in Wireless sensor Networks for Environment Monitoring
AINA '07 Proceedings of the 21st International Conference on Advanced Networking and Applications
An energy-efficient K-hop clustering framework for wireless sensor networks
EWSN'07 Proceedings of the 4th European conference on Wireless sensor networks
Distributed, hierarchical clustering and summarization in sensor networks
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Distributed spatial clustering in sensor networks
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
PAQ: time series forecasting for approximate query answering in sensor networks
EWSN'06 Proceedings of the Third European conference on Wireless Sensor Networks
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Nowadays, many sensor network applications are not only interested in the raw data of a single sensor node, but also the overview distribution features of network-wide sensory data. Data-centric clustering can provide an overview of the sensory data distribution. However most data-centric clustering researches are based on snapshot data rather than evolving data, which is not feasible as data evolves. In this paper, we propose a novel clustering method in wireless sensor networks called MCC: Model-based Continuous Clustering. In MCC, we do the clustering in a continuous way to make every node belong to the most suitable cluster at every epoch which guarantees that the clustering structure can represent the upto-date data distribution to the most extent. Moreover, we build a model for every sensor node based on its recent data to capture the data evolving trend. MCC is based on these models. The extensive experiments on real-datasets show the effectiveness and efficiency of MCC.