Abstract
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.
Highlights
Wireless technologies offer new perspectives in the field of telecommunications and computer networks
T-RLE is applied for the same example: Variables (1 3,5&7,9&11,13&) Repetition (3) Total: 17 Regarding the characters that have more than 2 occurrences, the T-RLE functions to the RLE algorithm
We proposed a T-RLE algorithm that fits with this kind of data, especially in regards to numbers that have 1 or 2 occurrences
Summary
Wireless technologies offer new perspectives in the field of telecommunications and computer networks. The intrinsic characteristics of this new generation of microsensors (tiny, processing capacity, wireless communication, low cost, diversity of sensors (optical, thermal, multimedia, etc.)) have opened up new and varied application perspectives for sensor networks in many fields (military, home automation, environmental, etc.) [3] They raise in the same proportions, many research problems as much by the potential applications that they suggest by the various constraints that they impose. Data compression methods reduce the size of the data before transmitting it over the wireless channel, which result in a reduction in total power consumption. These energy savings due to compression translate directly by extending the lifetime of nodes and the network in general.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
