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Distributed Detection with Non-identical Wireless Sensors for Industrial Applications
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering. Department of Electronics, Mathematics and Natural Sciences, University of Gävle, Sweden.ORCID iD: 0000-0001-8387-3779
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Electronics..ORCID iD: 0000-0001-5429-7223
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0002-3599-5584
2019 (English)In: 2019 IEEE International Conference on Industrial Technology (ICIT), IEEE, 2019, p. 1403-1408Conference paper, Published paper (Refereed)
Abstract [en]

There has been very little exploration when it comes to design distributed detection techniques and data fusion rules with non-identical sensors. This concept can be utilized in many possible applications within industrial automation, surveillance and safety. Here, for a global event, some of the sensors/detectors in the network can observe the full set of the hypotheses, whereas the remaining sensors infer more than one hypotheses as a single hypothesis. The local decisions are sent to the decision fusion center (DFC) over a multiple access wireless channel. In this paper, a fusion rule based on minimization of variance of the local mis-detection is proposed. The presence of sensors with limited detection capabilities is found to have a positive impact on the overall system performance, both in terms of probability of detection and transmit power consumption. Additionally, when the DFC is equipped with a large antenna array,the overall transmit power consumption can be reduced without sacrificing the detection performance.

Place, publisher, year, edition, pages
IEEE, 2019. p. 1403-1408
Series
IEEE International Conference on Industrial Technology, ISSN 2643-2978
Keywords [en]
Wireless sensor networks, Distributed detection
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-259010DOI: 10.1109/ICIT.2019.8755012ISI: 000490548300221Scopus ID: 2-s2.0-85069036657ISBN: 978-1-5386-6376-9 (print)OAI: oai:DiVA.org:kth-259010DiVA, id: diva2:1350543
Conference
2019 IEEE International Conference on Industrial Technology (ICIT), February 13-15, 2019, Melbourne, Australia
Note

QC 20191108

Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2020-01-22Bibliographically approved

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Panigrahi, Smruti Ranjan

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