EXPLORING APPLICATION-LEVEL SEMANTICS FOR DATA COMPRESSION

 

EXPLORING APPLICATION-LEVEL SEMANTICS

FOR DATA COMPRESSION

Abstract

         

          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. In this paper, we first propose an efficient distributed mining algorithm to jointly identify a group of moving objects and discover their movement patterns in wireless sensor

Networks. Afterward, we propose a compression algorithm, called 2P2D, which exploits the obtained group movement patterns to reduce the amount of delivered data. The compression algorithm includes a sequence merge and an entropy reduction phases. In the sequence merge phase, we propose a Merge algorithm to merge and compress the location data of a group of moving objects. In the entropy reduction phase, we formulate a Hit Item Replacement (HIR) problem and propose a Replace algorithm that obtains the optimal solution. Moreover, we devise three replacement rules and derive the maximum compression ratio. The experimental results show that the proposed compression algorithm leverages the group movement patterns to reduce the amount of delivered data effectively and efficiently.

Existing System

Different from previous compression techniques that remove redundancy of data according to the regularity within the data, we devise a novel two-phase and

2D algorithm, called 2P2D, which utilizes the discovered group movement patterns shared by the transmitting node and the receiving node to compress data. In addition to remove redundancy of data according to the correlations

Within the data of each single object, the 2P2D algorithm further leverages the correlations of multiple objects and their movement patterns to enhance the compressibility. Specifically, the 2P2D algorithm comprises a sequence merge and an entropy reduction phases

Proposed System

Our approach reduces the amount of delivered data and, by extension, the energy consumption in WSNs. Different from previous works, we formulate a moving object clustering problem that jointly identifies a group of objects and discovers their movement patterns. The application-level semantics are useful for various applications, such as data storage and transmission, task scheduling, and network construction. To approach the moving object clustering problem, we propose an efficient distributed mining algorithm to minimize the number of groups such that members in each of the discovered groups are highly related by their movement patterns. We propose a novel compression algorithm to compress the location data of a group of moving objects with or without loss of information. We Formulate the HIR problem to minimize the entropy of location data and explore the Shannon’s theorem to solve the HIR problem. We also prove that the proposed compression algorithm obtains the optimal solution of the HIR problem efficiently.

Algorithms

Group Movement Pattern Mining

GMP Mine algorithm uses a PST to generate an object’s significant movement Patterns and computes the similarity of two objects by using simp to derive the local grouping results. The merits, of simp include its accuracy and efficiency: First, simp considers the significances of each movement pattern regarding to individual objects so that it achieves better accuracy in similarity comparison.

Cluster Ensembling

CE algorithm to combine multiple local grouping results. The algorithm solves the inconsistency problem and improves the grouping quality.