The computational and memory resources of wireless sensor nodes are typically very limited, as the employed low-energy microcontrollers provide only hardware support for 16 bit integer operations and have very limited random access memory (RAM). These limitations prevent the application of modern signal processing techniques to pre-process the collected sensor data for energy and bandwidth efficient transmission over sensor networks.
The fractional wavelet filter overcomes these limitations through a novel fractional computation of the two-dimentsional image wavelet trasform. The fractional wavelet filter technique which computes wavelet transforms with 16 bit integers and requires less than 1.5 kByte of RAM for a 256 x 256 gray scale image. The low-memory wavelet techniques in conjunction with image coding systems for low-memory systems achieves image compression competitive to the JPEG2000 standard on resource-constrained wireless sensor nodes.
The underlying signal processing techniques as well as the fractional wavelet filter are introduced in the tutorial manuscript: Stephan Rein and Martin Reisslein. Low-Memory Wavelet Transforms for Wireless Sensor Networks: A Tutorial, IEEE Communications Surveys and Tutorials, 13(2):291-307, Second Quarter 2011. Digital Object Identifier 10.1109/SURV.2011.100110.00059 .
We make the C-code software for the fractional wavelet filter freely available: Source code for Low-Memory Wavelet Transforms for Wireless Sensor Networks as .tgz file: http://mre.faculty.asu.edu/fwf_code.tgz
The performance of the fractional wavelet filter is evaluated in detail in: Stephan Rein and Martin Reisslein. Performance Evaluation of the Fractional Wavelet Filter: A Low-Memory Image Wavelet Transform for Multimedia Sensor Networks , Ad Hoc Networks , 9(4):482-496, June 2011. doi: 10.1016/j.adhoc.2010.08.004
For an animation illustrating the operation of the fractional wavelet filter please click here.