Quantized variational filtering for bayesian inference in wireless sensor networks

Majdi Mansouri, Hichem Snoussi, Jing Teng, Ouachani Ilham, Cédric Richard

Research output: Chapter in Book/Report/Conference proceedingChapter


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.

Original languageEnglish
Title of host publicationVisual Information Processing in Wireless Sensor Networks
Subtitle of host publicationTechnology, Trends and Applications
PublisherIGI Global
Number of pages26
ISBN (Print)9781613501535
Publication statusPublished - 18 Apr 2014


ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Mansouri, M., Snoussi, H., Teng, J., Ilham, O., & Richard, C. (2014). Quantized variational filtering for bayesian inference in wireless sensor networks. In Visual Information Processing in Wireless Sensor Networks: Technology, Trends and Applications (pp. 224-249). IGI Global. https://doi.org/10.4018/978-1-61350-153-5.ch011