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.
Wireless sensor networks possess significant limitations in storage, bandwidth, and power. Additionally, real-time sensor networks cannot tolerate high latency. While some good compression algorithms exist specific to sensor networks, there remains a need for methods that do not introduce additional latency. This paper introduces a compression scheme which reduces storage, bandwidth, and power while also minimizing latency. Our Huffman style compression scheme exploits temporal locality and delta compression to provide better bandwidth utilization, thus reducing latency for real time applications.
Wireless sensor networks possess significant limitations in storage, bandwidth, and power. Additionally, real-time sensor networks cannot tolerate high latency. While some good compression algorithms exist specific to sensor networks, in this paper we present an energy-efficient method with highcompression ratio that reduces latency, storage and bandwidth usage further in comparison with some other recently proposed algorithms. Our Huffman style compression scheme exploits temporal locality and delta compression to provide better bandwidth utilization in the network, thus reducing latency for real time applications. Our performance evaluations show comparable compression ratios and energy savings with a significant decrease in latency compared to some other existing approaches.
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