"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.
Madria, Sanjay Kumar
McMillin, Bruce M.
Sarangapani, Jagannathan, 1965-
Ph. D. in Computer Science
Missouri University of Science and Technology
Journal article titles appearing in thesis/dissertation
- Energy-efficient real-time data compression in wireless sensor networks
- Tinypack XML: real time XML compression for wireless sensor networks
- On compressing data in wireless sensor networks for energy efficiency and real time delivery
- Energy efficient distributed grouping and scaling for real-time data compression in sensor networks
- Toward energy efficient multistream collaborative compression in wireless sensor networks
xv, 159 pages
© 2014 Thomas Mark Daniel Szalapski, All rights reserved.
Dissertation - Open Access
Wireless sensor networks
Data compression (Computer science) -- Adaptive control systems
Energy consumption -- Computer programs
Electronic OCLC #
Szalapski, Thomas Mark Daniel, "Energy efficient and latency aware adaptive compression in wireless sensor networks" (2014). Doctoral Dissertations. 2331.