A
Arman Anzanpour
Researcher at University of Turku
Publications - 32
Citations - 1825
Arman Anzanpour is an academic researcher from University of Turku. The author has contributed to research in topics: Wearable computer & Early warning score. The author has an hindex of 16, co-authored 29 publications receiving 1329 citations. Previous affiliations of Arman Anzanpour include Information Technology University & University of California.
Papers
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Journal ArticleDOI
Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things
Amir M. Rahmani,Tuan Nguyen Gia,Behailu Negash,Arman Anzanpour,Iman Azimi,Mingzhe Jiang,Pasi Liljeberg +6 more
TL;DR: This paper proposes to exploit the concept of Fog Computing in Healthcare IoT systems by forming a Geo-distributed intermediary layer of intelligence between sensor nodes and Cloud and presents a prototype of a Smart e-Health Gateway called UT-GATE.
Journal ArticleDOI
HiCH: Hierarchical Fog-Assisted Computing Architecture for Healthcare IoT
Iman Azimi,Arman Anzanpour,Amir M. Rahmani,Tapio Pahikkala,Marco Levorato,Pasi Liljeberg,Nikil Dutt +6 more
TL;DR: The proposed hierarchical computing architecture, HiCH, is a novel computing architecture suitable for hierarchical partitioning and execution of machine learning based data analytics, and a closed-loop management technique capable of autonomous system adjustments with respect to patient’s condition.
Journal ArticleDOI
On the Feasibility of Attribute-Based Encryption on Internet of Things Devices
Moreno Ambrosin,Arman Anzanpour,Mauro Conti,Tooska Dargahi,Sanaz Rahimi Moosavi,Amir M. Rahmani,Pasi Liljeberg +6 more
TL;DR: A thorough evaluation confirms that adopting attribute-based encryption in the IoT is indeed feasible, considering well-known IoT platforms--specifically, Intel Galileo Gen 2, Intel Edison, Raspberry Pi 1 Model B, and Raspberry Pi Zero.
Proceedings ArticleDOI
Empowering healthcare IoT systems with hierarchical edge-based deep learning
Iman Azimi,Janne Takalo-Mattila,Arman Anzanpour,Amir M. Rahmani,Juha-Pekka Soininen,Pasi Liljeberg +5 more
TL;DR: This paper investigates the feasibility of deploying the Convolutional Neural Network (CNN) based classification model as an example of deep learning methods in a hierarchical computing architecture, and demonstrates a real-time health monitoring for a case study on ECG classifications.
Book ChapterDOI
Leveraging Fog Computing for Healthcare IoT
Behailu Negash,Tuan Nguyen Gia,Arman Anzanpour,Iman Azimi,Mingzhe Jiang,Tomi Westerlund,Amir M. Rahmani,Amir M. Rahmani,Pasi Liljeberg,Hannu Tenhunen +9 more
TL;DR: This chapter focuses on a smart e-health gateway implementation for use in the Fog computing layer, connecting a network of such gateways, both in home and in hospital use.