Natural phenomena show that many creatures form massive social groups and move in regular patterns. However, previous works concentrate on finding the movement patterns of each single object or all objects. In this paper, we have a tendency to first propose an efficient distributed mining algorithm to jointly determine a group of moving objects and see their movement patterns in wireless sensor networks. Afterward, we have a tendency to propose a compression algorithm, called 2P2D, that exploits the obtained group movement patterns to reduce the number of delivered knowledge. The compression algorithm includes a sequence merge and an entropy reduction phases. In the sequence merge phase, we tend to propose a Merge algorithm to merge and compress the situation data of a cluster of moving objects. In the entropy reduction part, we have a tendency to formulate a Hit Item Replacement (HIR) problem and propose a Replace algorithm that obtains the optimal resolution. Moreover, we have a tendency to devise three replacement rules and derive the most compression ratio. The experimental results show that the proposed compression algorithm leverages the group movement patterns to cut back the number of delivered knowledge effectively and efficiently.

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