IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
K-Nearest Neighbor Search for Moving Query Point
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Semantic Caching in Location-Dependent Query Processing
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Location-based spatial queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Networks
ICNP '03 Proceedings of the 11th IEEE International Conference on Network Protocols
Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
An energy efficient clustering scheme of mobile sink node in wireless sensor networks
CSECS'08 Proceedings of the 7th conference on Circuits, systems, electronics, control and signal processing
Joint multiple target tracking and classification in collaborative sensor networks
IEEE Journal on Selected Areas in Communications
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Sensor Network is being used in a variety of areas. As Sensor Network nodes are evolving into a mobile environment, we should seek an appropriate method to set up clusters and select cluster headers. In this article, we suggest a dynamic prediction clustering algorithm, which uses directions, angles and hops among the dynamic skyline query properties. This proposed algorithm is one that builds clusters and selects cluster headers on the basis of mobile sensor nodes. It can reduce the waste of unnecessary energy of mobile sensor nodes which are caused by the occurrence of "Adv" message. And in proportion to the density of sensor nodes for efficient clustering, this algorithm builds dynamic clusters, and extends the network life cycle by reducing the average energy of sensor nodes by 2.4 times. Also keeping dynamic clusters and maximizing the hop counts within clusters, the algorithm has reduced the average energy consumption of a node by 14%.