A comprehensive review and performance evaluation of modern lossless compression algorithms for real-time WSNs
Wireless sensor networks (WSN) are groups of nodes that collectively sense and control an environment. The readings of these nodes may reach a point where it becomes too big for the network to handle, where data compression becomes an option to minimize the size of data being transmitted across the...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
| Published: |
Animo Repository
2022
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/etdb_comtech/1 https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1000&context=etdb_comtech |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | Wireless sensor networks (WSN) are groups of nodes that collectively sense and control an environment. The readings of these nodes may reach a point where it becomes too big for the network to handle, where data compression becomes an option to minimize the size of data being transmitted across the network. LZMA, LZW, LEC, gzip, and bzip2 are well-known lossless compression techniques used in prior studies and are generally recommended for sensor data. Previous studies show that these algorithms yielded at least 60% in compression ratio. This study simulated data compression in a WSN using these algorithms with a Raspberry Pi, ZigBee module, and a personal computer to reproduce the functionalities of a WSN. The simulated scenarios reveal that bzip2 and LZMA are suited for sensor data compression, followed by LZW and gzip, and LEC performing the worst, with all results compared to prior studies. |
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