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Fast and Efficient Online Selection of Sensors for Transmitter Localization
IIIT Delhi, India.
Hitachi Energy.
IMDEA Networks Institute.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0001-6682-6559
2022 (English)In: 2022 14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022, Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 604-612Conference paper, Published paper (Refereed)
Abstract [en]

The increase in cost and usage of RF spectrum has made it increasingly necessary to monitor its usage and protect it from unauthorized use. A number of prior studies have designed algorithms to localize unauthorized transmitters using crowdsourced sensors. To reduce the cost of crowdsourcing, these studies select the most relevant sensors a priori to localize such transmitters. In this work, we instead argue for online selection to localize such transmitters. Online selection can lead to more accurate localization using limited number of sensors, as compared to selecting sensors a priori, albeit at the cost of higher latency. To account for the trade-off between accuracy and latency, we add a constraint on the number of selection rounds. For the case where the number of rounds is equal to the number of selected sensors, we propose a heuristic based on Thompson Sampling and show using trace-driven simulation that it provides 23 % better accuracy compared to a number of proposed baseline algorithms. For restricted number of rounds, we show that using conventional parallel version of the modified Thompson Sampling which selects equal number of sensors in each round results in a substantial reduction in accuracy. To this end, we propose a strategy of selecting decreasing number of sensors in subsequent rounds of the modified Parallel Thompson Sampling. Our evaluation shows that the proposed heuristic leads to only 3 % reduction in accuracy in contrast to 22 % using modified Parallel Thompson Sampling, when we select 50 sensors in 20 rounds.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2022. p. 604-612
Keywords [en]
Crowdsourcing, Economic and social effects, Heuristic algorithms, % reductions, Localisation, Online selection, Parallel version, RF-spectrum, Substantial reduction, Thompson samplings, Trace driven simulation, Trade off, Transmitters
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:kth:diva-320559DOI: 10.1109/COMSNETS53615.2022.9668385Scopus ID: 2-s2.0-85124522248OAI: oai:DiVA.org:kth-320559DiVA, id: diva2:1706451
Conference
14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022, 4-8 January 2022, Bangalore, India
Note

QC 20221026

Part of proceedings: ISBN 978-1-6654-2104-1

Available from: 2022-10-26 Created: 2022-10-26 Last updated: 2022-10-26Bibliographically approved

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Gross, James

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Output format
  • html
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  • asciidoc
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