Semantics-driven sensor configuration for energy reduction in medical sensor networks
Venue
Proceedings of the 2012 ACM/IEEE international symposium on Low power electronics and design, ACM, pp. 303-308
Publication Year
2012
Authors
James Wendt, Saro Meguerdichian, Hyduke Noshadi, Miodrag Potkonjak
BibTeX
Abstract
Traditional optimization methods for large multisensory networks often use sensor
array reduction and sampling techniques that attempt to reduce energy while
retaining full predictability of the raw sensed data. For systems such as medical
sensor networks, raw data prediction is unnecessary, rather, only relevant
semantics derived from the raw data are essential. We present a new method for
sensor fusion, array reduction, and subsampling that reduces both energy and cost
through semantics-driven system configuration. Using our method, we reduce the
energy requirements of a medical shoe by a factor of 17.9 over the original system
configuration while maintaining semantic relevance.
