A Lossless Compression Approach Based on Delta Encoding and T-RLE in WSNs

The sending/receiving of data (data communication) is the most power consuming in wireless sensor networks (WSN) since the sensor nodes are depending on batteries not generally rechargeable characterized by limited capacity. Data compression is among the techniques that can help to reduce the amount...

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Bibliographic Details
Published in:Wireless communications and mobile computing 2020, Vol.2020 (2020), p.1-10
Main Authors: Saidani, Abdeldjalil, Mansouri, Deloula, Jianwen, Xiang
Format: Article
Language:English
Subjects:
Algorithms
Bandwidths
Communication
Compression ratio
Data communication
Data compression
Data dictionaries
Data storage
Data transmission
Datasets
Energy conservation
Energy consumption
Humidity
Nodes
Optimization
Power consumption
Power management
Rechargeable batteries
Sensors
Wireless networks
Wireless sensor networks
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Summary:The sending/receiving of data (data communication) is the most power consuming in wireless sensor networks (WSN) since the sensor nodes are depending on batteries not generally rechargeable characterized by limited capacity. Data compression is among the techniques that can help to reduce the amount of the exchanged data between wireless sensor nodes resulting in power saving. Nevertheless, there is a lack of effective methods to improve the efficiency of data compression algorithms and to increase nodes’ energy efficiency. In this paper, we proposed a novel lossless compression approach based on delta encoding and two occurrences character solving (T-RLE) algorithms. T-RLE is an optimization of the RLE algorithm, which aims to improve the compression ratio. This method will lead to less storage cost and less bandwidth to transmit the data, which positively affects the sensor nodes’ lifetime and the network lifetime in general. We used real deployment data (temperature and humidity) from the sensor scope project to evaluate the performance of our approach. The results showed a significant improvement compared with some traditional algorithms.
ISSN:1530-8669
1530-8677
DOI:10.1155/2020/8824954