Learning probabilistic automata with variable memory length
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Journal of the ACM (JACM)
A clustering algorithm based on graph connectivity
Information Processing Letters
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Mining Temporal Moving Patterns in Object Tracking Sensor Networks
UDM '05 Proceedings of the International Workshop on Ubiquitous Data Management
Adaptive Tracking in Distributed Wireless Sensor Networks
ECBS '06 Proceedings of the 13th Annual IEEE International Symposium and Workshop on Engineering of Computer Based Systems
On Mining Moving Patterns for Object Tracking Sensor Networks
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Efficient In-Network Moving Object Tracking in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
Location tracking in a wireless sensor network by mobile agents and its data fusion strategies
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Routing techniques in wireless sensor networks: a survey
IEEE Wireless Communications
DCTC: dynamic convoy tree-based collaboration for target tracking in sensor networks
IEEE Transactions on Wireless Communications
Mining trajectory profiles for discovering user communities
Proceedings of the 2009 International Workshop on Location Based Social Networks
QS-STT: QuadSection clustering and spatial-temporal trajectory model for location prediction
Distributed and Parallel Databases
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In this paper, we investigate and utilize the characteristic of the group movement of objects to achieve energy conservation in the inherently resource-constrained wireless object tracking sensor network (OTSN). We propose a novel mining algorithm that consists of a global mining and a local mining to leverage the group moving pattern. We use the VMM model together with Probabilistic Suffix Tree (PST) in learning the moving patterns, as well as Highly Connected Component (HCS) that is a clustering algorithm based on graph connectivity for moving pattern clustering in our mining algorithm. Based on the mined out group relationship and the group moving patterns, a hierarchically prediction-based query algorithm and a group data aggregation algorithm are proposed. Our experiment results show that the energy consumption in terms of the communication cost for our system is better than that of the conventional query/update based OTSN, especially in the case that on-tracking objects have the group moving characteristics.