Scalable Data Chunk Similarity for Mobile Node Data Using Group Pattern Object
Compressive Sensing, Data Gathering, Random Walk, Wireless Sensor Network
Spatial co-location pattern mining is an interesting and important task in spatial data mining which discovers the subsets of spatial features frequently observed together in nearby geographic space. However, the traditional framework of mining prevalent co-location patterns produces numerous redundant co-location patterns, which makes it hard for users to understand or apply. To address this issue, in this paper we study the problem of reducing redundancy in a collection of prevalent co-location patterns by utilizing the spatial distribution information of co-location instances In this project considered the redundancy reduction problem of the spatial prevalent co-locations by applying distribution information from co-location instances. It is worth mentioning that the proposed method not only solves the redundancy reduction problem but also provides high efficiency. There are several interesting directions that we are considering for future work: (1) Compression of the spatial prevalent co-locations (to get fewer co-locations but more usability, i.e., a set of representative co-locations); (2) Ordering of the spatial prevalent co-locations; and (3) Reducing the redundancy of prevalent co-locations found in incrementally updated data. In proposed study analysis a natural phenomena show that many creatures form large social groups and move in regular patterns. However, previous works focus on finding the movement patterns of each single object or all objects. This thesis proposes an efficient distributed mining algorithm to jointly identify a group of moving objects and discover their movement patterns in wireless sensor networks. Afterward, a compression algorithm, called (2 phase and 2D) 2P2D is proposed, which utilizes the discovered group movement patterns shared by the transmitting node and the receiving node to compress data and thereby reduces the amount of delivered data.
Article Details
Unique Paper ID: 148153

Publication Volume & Issue: Volume 5, Issue 12

Page(s): 589 - 596
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