Energy Efficient Distributed Grouping and Scaling for Real-Time Data Compression in Sensor Networks
Wireless sensor networks possess significant limitations in storage, bandwidth, and power. This has led to the development of several compression algorithms designed for sensor networks. Many of these methods exploit the correlation often present between the data on different sensors in the network. Most of these algorithms require collecting a great deal of data before compressing which introduces an increase in latency that cannot be tolerated in real-time systems. We propose a distributed method for collaborative compression of correlated sensor data. The compression can be lossless or lossy with a parameter for maximum tolerable error. Error rate can be adjusted dynamically to increase compression under heavy load. Performance evaluations show comparable compression ratios to centralized methods and a decrease in latency and network bandwidth compared to some recent approaches.
T. Szalapski and S. K. Madria, "Energy Efficient Distributed Grouping and Scaling for Real-Time Data Compression in Sensor Networks," Proceedings of the IEEE 33rd International Performance Computing and Communications Conference (2014, Austin, TX), Institute of Electrical and Electronics Engineers (IEEE), Dec 2014.
The definitive version is available at https://doi.org/10.1109/PCCC.2014.7017073
IEEE 33rd International Performance Computing and Communications Conference, IPCCC 2014 (2014: Dec. 5-7, Austin, TX)
Keywords and Phrases
Bandwidth; Bandwidth compression; Compaction; Digital storage; Energy efficiency; Interactive computer systems; Real time systems; Wireless sensor networks; Collaborative; Compression algorithms; Distributed methods; Energy efficient; Heavy loads; Network bandwidth; Real time; Sensor data; Data compression; Collaborative; Compression; Real-time; Wireless sensor network
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Article - Conference proceedings
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