DPFTT: Distributed Particle Filter for Target Tracking in the Internet of Things

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dc.contributor.authorBoulkaboul, Sahar
dc.contributor.authorDjenouri, Djamel
dc.contributor.authorBagaa, Miloud
dc.date.accessioned2024-02-20T08:21:17Z
dc.date.available2024-02-20T08:21:17Z
dc.date.issued2023-11-07
dc.description.abstractA novel distributed particle filter algorithm for target tracking is proposed in this paper. It uses new metrics and addresses the measurement uncertainty problem by adapting the particle filter to environmental changes and estimating the kinematic (motion-related) parameters of the target. The aim is to calculate the distance between the Gaussian-distributed probability densities of kinematic data and to generate the optimal distribution that maximizes the precision. The proposed data fusion method can be used in several smart environments and Internet of Things (IoT) applications that call for target tracking, such as smart building applications, security surveillance, smart healthcare, and intelligent transportation, to mention a few. The diverse estimation techniques were compared with the state-of-the-art solutions by measuring the estimation root mean square error in different settings under different conditions, including high-noise environments. The simulation results show that the proposed algorithm is scalable and outperforms the standard particle filter, the improved particle filter based on KLD, and the consensus-based particle filter algorithm.
dc.identifier.doi10.23919/PEMWN58813.2023.10304926
dc.identifier.isbn979-8-3503-0672-9
dc.identifier.urihttps://dl.cerist.dz/handle/CERIST/1020
dc.publisherIEEE
dc.relation.ispartofseries12th IFIP/IEEE International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN); 27-29 September 2023
dc.relation.pages6p.
dc.relation.placeBerlin, Germany
dc.structureTechnologie Internet et Réseaux
dc.subjectParticle filter
dc.subjectProbability density
dc.subjectEvidence distance
dc.subjectObject Tracking
dc.subjectInternet of Things
dc.subjectWireless sensors networks
dc.titleDPFTT: Distributed Particle Filter for Target Tracking in the Internet of Things
dc.typeConference paper
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