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STING: A Statistical Information Grid Approach to Spatial Data Mining
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ACM SIGCOMM Computer Communication Review
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ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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Proceedings of the 2003 ACM SIGMOD international conference on Management of data
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ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Exploiting Correlated Attributes in Acquisitional Query Processing
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Distance indexing on road networks
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
International Journal of Network Management
Group-based intrusion detection system in wireless sensor networks
Computer Communications
Clustering Data Streams in Optimization and Geography Domains
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
An autonomy-oriented computing approach to community mining in distributed and dynamic networks
Autonomous Agents and Multi-Agent Systems
MCC: model-based continuous clustering in wireless sensor networks
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
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Spatially correlated multi-modal wireless sensor networks: a coalitional game theoretic approach
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Energy efficient data gathering using prediction-based filtering in wireless sensor networks
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ACM Transactions on Sensor Networks (TOSN)
Qute: quality-of-monitoring aware sensing and routing strategy in wireless sensor networks
Proceedings of the fourteenth ACM international symposium on Mobile ad hoc networking and computing
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Sensor networks monitor physical phenomena over large geographic regions. Scientists can gain valuable insight into these phenomena, if they understand the underlying data distribution. Such data characteristics can be efficiently extracted through spatial clustering, which partitions the network into a set of spatial regions with similar observations. The goal of this paper is to perform such a spatial clustering, specifically δ-clustering, where the data dissimilarity between any two nodes inside a cluster is at most δ. We present an in-network clustering algorithm ELink that generates good δ-clusterings for both synchronous and asynchronous networks in $O(\sqrt{N} {\rm log}N)$ time and in O(N) message complexity, where N denotes the network size. Experimental results on both real world and synthetic data sets show that ELink’s clustering quality is comparable to that of a centralized algorithm, and is superior to other alternative distributed techniques. Furthermore, ELink performs 10 times better than the centralized algorithm, and 3-4 times better than the distributed alternatives in communication costs. We also develop a distributed index structure using the generated clusters that can be used for answering range queries and path queries. The query algorithms direct the spatial search to relevant clusters, leading to performance gains of up to a factor of 5 over competing techniques.