Discovering Application Level Semantics for Data Compression using Hybrid Compression Technique
B. Sasikumar1, R. Deepa2

1B. Sasikumar, Professor, Department of CSE, The Rajaas Engineering College, Vadakkankulam, Thirunelvel District.
2R. Deepa, Computer Science and Engineering, Vins Christian College of Engineerring, Nagercoil.

Manuscript received on July 01, 2012. | Revised Manuscript received on July 04, 2012. | Manuscript published on July 05, 2012. | PP: 288-294 | Volume-2, Issue-3, July 2012. | Retrieval Number: C0740062312 /2012©BEIESP
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Abstract: Based on Natural phenomena many creatures form large social groups and move in regular patterns. Traditional works focus on finding the movement patterns of each single object or all objects. This paper propose an efficient distributed mining algorithm to jointly identify a group of moving objects and discover their movement patterns in Wireless Sensor Networks (WSN). The algorithm consists of a local mining phase and a cluster ensembling phase. The local mining phase adopts the Variable Length Markov (VMM) model together with Probabilistic Suffix Tree (PST) to find the moving patterns, as well as Highly Connected Component (HCC) to partition the moving objects. The cluster ensembling phase utilizes Jaccard Similarity Coefficient and Normalized Mutual Information to combine and improve the local grouping results. The distributed mining algorithm achieves good grouping quality and robustness. Moreover this paper extends a technique called Hybrid Compression Technique (HCT) based on the location information of nodes in the WSN. HCT is formulated to reduce the amount of energy consumption and increases the lifetime of the WSN. The experimental result shows that the technique have good ability of approximation to manage the WSN, and have high data compression efficiency and leverages the group movement patterns to reduce the amount of delivered data effectively and efficiently.

Keywords: Clustering, compression, hybrid, patterns, semantics, wireless sensor networks.