Clustering and Representation of Time-Varying Industrial Wireless Channel Measurements

Published: October 14, 2019

Author(s)

Mohamed T. Hany, Richard 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, -1
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
Created October 14, 2019, Updated August 06, 2019