Energy efficient and latency aware adaptive compression in wireless sensor networks

Wireless sensor networks are composed of a few to several thousand sensors deployed over an area or on specific objects to sense data and report that data back to a sink either directly or through a series of hops across other sensor nodes. There are many applications for wireless sensor networks including environment monitoring, wildlife tracking, security, structural heath monitoring, troop tracking, and many others. The sensors communicate wirelessly and are typically very small in size and powered by batteries. Wireless sensor networks are thus often constrained in bandwidth, processor speed, and power. Also, many wireless sensor network applications have a very low tolerance for latency and need to transmit the data in real time. Data compression is a useful tool for minimizing the bandwidth and power required to transmit data from the sensor nodes to the sink; however, compression algorithms often add a significant amount of latency or require a great deal of additional processing. The following papers define and analyze multiple approaches for achieving effective compression while reducing latency and power consumption far below what would be required to process and transmit the data uncompressed. The algorithms target many different types of sensor applications from lossless compression on a single sensor to error tolerant, collaborative compression across an entire network of sensors to compression of XML data on sensors. Extensive analysis over many different real-life data sets and comparison of several existing compression methods show significant contribution to efficient wireless sensor communication --Abstract, page iv

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