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by Sihyeong Park on 2017-11-14 17:37:30

Date : 2017. 11. 20 (Mon) 17:00

Locate : EB5. 533

Presenter : Sihyeong Park


Title : HiCH: Hierarchical Fog-Assisted Computing Architecture for Healthcare IoT

ACM Transactions on Embedded Computing Systems (TECS) - Special Issue ESWEEK 2017, CASES 2017, CODES + ISSS 2017 and EMSOFT 2017 TECS 
Volume 16 Issue 5s, October 2017 
Article No. 174 


Author : Iman Azimi University of Turku, Turku, Finland, Arman Anzanpour University of Turku, Turku, Finland, Amir M. Rahmani University of California Irvine and TU Wien, Irvine, CA, USA, Tapio Pahikkala University of Turku, Turku, Finland, Marco Levorato University of California Irvine, Irvine, CA, USA, Pasi Liljeberg University of Turku, Turku, Finland, Nikil Dutt University of California Irvine, Irvine, CA, US

Abstract : The Internet of Things (IoT) paradigm holds significant promises for remote health monitoring systems. Due to their life- or mission-critical nature, these systems need to provide a high level of availability and accuracy. On the one hand, centralized cloud-based IoT systems lack reliability, punctuality and availability (e.g., in case of slow or unreliable Internet connection), and on the other hand, fully outsourcing data analytics to the edge of the network can result in diminished level of accuracy and adaptability due to the limited computational capacity in edge nodes. In this paper, we tackle these issues by proposing a hierarchical computing architecture, HiCH, for IoT-based health monitoring systems. The core components of the proposed system are 1) a novel computing architecture suitable for hierarchical partitioning and execution of machine learning based data analytics, 2) a closed-loop management technique capable of autonomous system adjustments with respect to patient’s condition. HiCH benefits from the features offered by both fog and cloud computing and introduces a tailored management methodology for healthcare IoT systems. We demonstrate the efficacy of HiCH via a comprehensive performance assessment and evaluation on a continuous remote health monitoring case study focusing on arrhythmia detection for patients suffering from CardioVascular Diseases (CVDs).

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