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.