The primary focus of the chapter is to study the Bayesian inference problem in distributed WSNs with particular emphasis on the trade-off between estimation precision and energy-awareness. We propose a variational approach to approximate the particle distribution to a single Gaussian distribution, while respecting the communication constraints of WSNs. The efficiency of the variational approximation relies on the fact that the online update and the compression of the filtering distribution are simultaneously performed. In addition, the variational approach has the nice property to be model-free, ensuring robustness of signal processing. We analyze the Bayesian inference issue for several specific but representative WSN applications to elaborate the quantized variational filtering method, which can be applicable to a wider class of problems.
|Title of host publication||Visual Information Processing in Wireless Sensor Networks|
|Subtitle of host publication||Technology, Trends and Applications|
|Number of pages||26|
|Publication status||Published - 18 Apr 2014|
ASJC Scopus subject areas
- Computer Science(all)