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Belief Propagation Techniques for Cooperative Localization in Wireless Sensor Networks
Technical University of Madrid, Spain. (Signal Processing Applications Group)ORCID iD: 0000-0001-7158-1525
Technical University of Madrid, Spain. (Signal Processing Applications Group)
2011 (English)In: Handbook of Position Location: Theory, Practice, and Advances / [ed] Seyed A. (Reza) Zekavat, R. Michael Buehrer, Hoboken, NJ, USA: John Wiley & Sons, 2011, 837-869 p.Chapter in book (Other academic)
Abstract [en]

A number of applications in wireless sensor networks (WSN) require sensor nodes to obtain their absolute or relative positions. Equipping every sensor with a GPS receiver may be expensive, energy prohibitive and limited to outdoor applications. Therefore, we consider cooperative localization where each sensor node with unknown location obtains its location by cooperating with neighboring sensor nodes. In this chapter, we apply probabilistic inference to the problem of cooperative localization. These techniques are capable to obtain, not only location estimates, but also a measure of the uncertainty of those estimates. Since these methods are computationally very expensive, we need to use message-passing methods, which are also known as belief propagation (BP) methods. BP is a way of organizing the global computation of marginal beliefs in terms of smaller local computations within the graph. It is one of the best-known probabilistic methods for distributed inference in statistical physics, articial intelligence, computer vision, error-correcting codes, localization, etc. The whole computation takes a time proportional to the number of links in the graph, which is significantly less than the exponentially large time that would be required to compute marginal probabilities naively. However, due to the presence of nonlinear relationships and highly non-Gaussian uncertainties the standard BP algorithm is undesirable. Nevertheless, a particle-based approximation via nonparametric belief propagation (NBP) makes BP acceptable for localization in sensor networks. In this chapter, after an introduction to cooperative localization, we describe BP/NBP techniques and its generalizations (GBP) for the loopy networks. Due to the poor performance of BP/NBP methods in loopy networks, we describe three improved methods: GBP based on Kikuchi approximation (GBP-K), nonparametric GBP based on junction tree (NGBP-JT), and NBP based on spanning trees (NBP-ST). The last one (NBP-ST) is currently a unique method which is computationally feasible in a large-scale WSN.

Place, publisher, year, edition, pages
Hoboken, NJ, USA: John Wiley & Sons, 2011. 837-869 p.
Keyword [en]
belief propagation techniques, graphical models, probabilistic inference to cooperative localization, NBP based on spanning trees, generalized belief propagation, message passing, junction tree
National Category
Engineering and Technology Signal Processing Communication Systems
URN: urn:nbn:se:liu:diva-81439DOI: 10.1002/9781118104750.ch25ISBN: 9780470943427ISBN: 9781118104750OAI: diva2:552483
Available from: 2012-09-14 Created: 2012-09-14 Last updated: 2013-10-01

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Savic, Vladimir
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