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Amir M. Rahmani

Researcher at University of California, Irvine

Publications -  172
Citations -  4829

Amir M. Rahmani is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Efficient energy use & Computer science. The author has an hindex of 27, co-authored 149 publications receiving 3150 citations. Previous affiliations of Amir M. Rahmani include Royal Institute of Technology & Vienna University of Technology.

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Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things

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.
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End-to-end security scheme for mobility enabled healthcare Internet of Things

TL;DR: The proposed scheme consists of a secure and efficient end-user authentication and authorization architecture based on the certificate based DTLS handshake, secure end-to-end communication based on session resumption, and robust mobility based on interconnected smart gateways.
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Internet-of-Things and big data for smarter healthcare: From device to architecture, applications and analytics

TL;DR: The purpose of this special issue is to analyze the top concerns in IoT technologies that pertain to smart sensors for health care applications; particularly applications targeted at individualized tele-health interventions with the goal of enabling healthier ways of life.
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Internet of things for remote elderly monitoring: a study from user-centered perspective

TL;DR: This paper studies the IoT-enabled systems tackling elderly monitoring to categorize the existing approaches from a new perspective and to introduce a hierarchical model for elderly-centered monitoring.
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HiCH: Hierarchical Fog-Assisted Computing Architecture for Healthcare IoT

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.