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Segmenting Bayesian networks for intelligent information dissemination in collaborative, context-aware environments with Bayeslets

Frank, Korbinian and Roeckl, Matthias and Pfeifer, Tom and Robertson, Patrick (2013) Segmenting Bayesian networks for intelligent information dissemination in collaborative, context-aware environments with Bayeslets. Pervasive and Mobile Computing, Specia. Elsevier. DOI: 10.1016/j.pmcj.2013.11.003 ISSN 1574-1192

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Official URL: http://www.sciencedirect.com/science/article/pii/S157411921300151X

Abstract

With ever smaller processors and ubiquitous Internet connectivity, the pervasive computing environments from Mark Weiser’s vision are coming closer. For their context-awareness, they will have to incorporate data from the abundance of sensors integrated in everyday life and to benefit from continuous machine-to-machine communications. Along with huge opportunities, this also poses problems: sensor measurements may conflict, processing times of logical and statistical reasoning algorithms increase non-deterministically polynomially or even exponentially, and wireless networks might become congested by the transmissions of all measurements. Bayesian networks are a good starting point for inference algorithms in pervasive computing, but still suffer from information overload in terms of network load and computation time. Thus, this work proposes to distribute processing with a modular Bayesian approach, thereby segmenting complex Bayesian networks. The introduced “Bayeslets” can be used to transmit and process only information which is valuable for its receiver. Two methods to measure the worth of information for the purpose of segmentation are presented and evaluated. As an example for a context-aware service, they are applied to a scenario from cooperative vehicular services, namely adaptive cruise control.

Item URL in elib:https://elib.dlr.de/86501/
Document Type:Article
Title:Segmenting Bayesian networks for intelligent information dissemination in collaborative, context-aware environments with Bayeslets
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Frank, Korbiniankorbinian.frank (at) dlr.deUNSPECIFIED
Roeckl, MatthiasIn2SoftUNSPECIFIED
Pfeifer, Tomtom.pfeifer (at) tu-berlin.deUNSPECIFIED
Robertson, Patrickpatrick.robertson (at) dlr.deUNSPECIFIED
Date:2 December 2013
Journal or Publication Title:Pervasive and Mobile Computing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:Specia
DOI :10.1016/j.pmcj.2013.11.003
Editors:
EditorsEmail
Pfeifer, TomUNSPECIFIED
Publisher:Elsevier
ISSN:1574-1192
Status:Published
Keywords:Vehicle-to-vehicle; Information dissemination; Information assessment; Bayeslet; Bayesian network; Context inference; Machine-to-machine communication
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Traffic Management (old)
DLR - Research area:Transport
DLR - Program:V VM - Verkehrsmanagement
DLR - Research theme (Project):V - VABENE (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Communication and Navigation > Communications Systems
Deposited By: Frank, Korbinian
Deposited On:27 Jan 2014 16:15
Last Modified:31 Jul 2019 19:43

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