04-09-2014, 11:49 AM
EXPLORING APPLICATION-LEVEL SEMANTICS FOR DATA COMPRESSION
DATA COMPRESSION.pptx (Size: 110.35 KB / Downloads: 8)
ABSTRACT
sociNatural phenomena show that many creatures form large al groups and move in regular patterns.
However previous work focus on finding the movement patterns of each single object or all objects.
We first propose an efficient distributing 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.
The experimental results show that the proposed compression algorithm exploits the group movement patterns to reduce the amount of delivered data effectively and efficiently.
Existing System
Discovering the group movement patterns is more difficult than finding the patterns of a single object or all objects, because we need to jointly identify a group of objects and discover their aggregated group movement patterns.
The constrained resource of WSNs should also be considered in approaching the moving object clustering problem.
Thus, unnecessary and redundant data may be delivered, leading to much more power consumption because data transmission needs more power than data processing in Wireless Sensor Networks (WSNs).
Proposed System
We have proposed a clustering algorithm to find the group relationships for query and data aggregation efficiency.
We first introduce our distributed mining algorithm to identify the moving objects and their group movement patterns in wireless sensor network.
Then, based on the discovered group movement patterns, we propose a novel compression algorithm to tackle the group data compression problem.
Compared with Existing here we remove redundancy and unnecessary 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.
CONCLUSION
First we propose a distributed mining algorithm to discover group movement patterns.
After we propose a compression algorithm called 2P2D algorithm, which comprises a sequence merge phase and an entropy reduction phase.
In the sequence mergephase, we propose the Merge algorithm to merge the location sequences of a group of moving objects with the goal of reducing the overall sequence length.
In the entropy reduction phase, we formulate the HIR problem and propose a Replace algorithm to tackle the HIR problem
Our experimental results show that the proposed compression algorithm effectively reduces the amount of delivered data