eprintid: 1466272 rev_number: 36 eprint_status: archive userid: 608 dir: disk0/01/46/62/72 datestamp: 2015-04-16 16:04:48 lastmod: 2021-10-10 22:47:55 status_changed: 2015-04-16 16:04:48 type: article metadata_visibility: show item_issues_count: 0 creators_name: Haj Chhadé, H creators_name: Abdallah, F creators_name: Gning, A creators_name: Julier, S creators_name: Mougharbel, I title: Non Parametric Distributed Inference in Sensor Networks Using Box Particles Messages ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F48 keywords: Interval analysis, Distributed Systems,Graphical models,Bayesian inference, Belief propagation, Wireless sensor networks, Calibration. note: Copyright © The Author(s) 2014. This article is published with open access at Springerlink.com. This article is distributed under the terms of the Creative Commons Attribution License which permits any use,distribution, and reproduction in any medium, provided the original author(s) and the source are credited. abstract: This paper deals with the problem of inference in distributed systems where the probability model is stored in a distributed fashion. Graphical models provide powerful tools for modeling this kind of problems. Inspired by the box particle filter which combines interval analysis with particle filtering to solve temporal inference problems, this paper introduces a belief propagation-like message-passing algorithm that uses bounded error methods to solve the inference problem defined on an arbitrary graphical model. We show the theoretic derivation of the novel algorithm and we test its performance on the problem of calibration in wireless sensor networks. That is the positioning of a number of randomly deployed sensors, according to some reference defined by a set of anchor nodes for which the positions are known a priori. The new algorithm, while achieving a better or similar performance, offers impressive reduction of the information circulating in the network and the needed computation times. date: 2014-09-01 official_url: http://dx.doi.org/10.1007/s11786-014-0200-2 vfaculties: VENG oa_status: green full_text_type: pub primo: open primo_central: open_green verified: verified_manual elements_source: Scopus elements_id: 969559 doi: 10.1007/s11786-014-0200-2 lyricists_name: Gning, El lyricists_name: Julier, Simon lyricists_id: EHAGN52 lyricists_id: SJULI23 full_text_status: public publication: Mathematics in Computer Science volume: 8 number: 3-4 pagerange: 455 - 478 issn: 1661-8270 citation: Haj Chhadé, H; Abdallah, F; Gning, A; Julier, S; Mougharbel, I; (2014) Non Parametric Distributed Inference in Sensor Networks Using Box Particles Messages. Mathematics in Computer Science , 8 (3-4) 455 - 478. 10.1007/s11786-014-0200-2 <https://doi.org/10.1007/s11786-014-0200-2>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/1466272/1/Julier.art%253A10.1007%252Fs11786-014-0200-2.pdf