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PUBLICATIONS

Clustering and Representation of Time-Varying Industrial Wireless Channel Measurements

Published

Author(s)

Mohamed Hany, Rick Candell, Yongkang Liu

Abstract

The wireless devices in cyber-physical systems (CPS) play a primary role in transporting the information flows within such systems. Deploying wireless systems in industry has many advantages due to the lower cost, ease of scale, and flexibility due to the absence of cabling. However, industrial wireless deployments in various industrial environments require having the proper models for the industrial wireless channels. In this work, we propose and assess an algorithm for characterizing measured channel impulse response (CIR) of time-varying wireless industrial channels. The proposed algorithm performs data processing, clustering, and averaging for measured CIRs. We have deployed dynamic time warping (DTW) distance metric to measure the similarity among CIRs. Then, an affinity propagation (AP) machine learning clustering algorithm is deployed for CIR grouping. Finally, we obtain the average CIR of various data clusters as a representation for the cluster. The algorithm is then assessed over industrial wireless channel measurements in various types of industrial environments.
Proceedings Title
45th Annual Conference of the IEEE Industrial Electronics Society
Conference Dates
October 14-17, 2019
Conference Location
Lisbon, PT
Conference Title
Annual Conference of the IEEE Industrial Electronics Society (IECON 2019)
Pub Type
Conferences

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Keywords

industrial wireless, wireless systems deployment, cyber-physical systems, wireless channel modeling, clustering, affinity propagation, channel impulse response
Wireless (RF) and Factory communications

Citation

Hany, M. , Candell, R. and Liu, Y. (2019), Clustering and Representation of Time-Varying Industrial Wireless Channel Measurements, 45th Annual Conference of the IEEE Industrial Electronics Society, Lisbon, PT, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=928119 (Accessed May 5, 2024)

Created October 13, 2019, Updated October 12, 2021