Aims and Scope
Recently, the Internet of Things (IoT) technologies have made their entrance into the healthcare domain. It is now providing many opportunities to develop Smart healthcare solutions with more intelligent and prediction capabilities both for daily life (home/office) and in-hospitals. In most of such Smart healthcare IoT systems, numerous IoT devices and sensors are being used to monitor users’ healthcare status and transmit the data directly to remote cloud data centers. Such combination of cloud computing and IoT (Cloud-IoT) enables the resource-constrained IoT devices to get the benefit from Cloud’s high-performance computing and massive storage infrastructure for real-time processing, storing, visualization, and analysis of IoT data.
However, currently such Cloud-IoT system is facing increasing difficulty to handle the healthcare Big data that IoT generates from various healthcare applications and services. It has become challenging to ensure low latency and network bandwidth consumption, scalability, reliability, mobility, and energy efficiency of healthcare IoT devices while moving all data to the cloud. To cope with these challenges, a recent trend is to deploy an edge computing infrastructure between IoT healthcare system and cloud computing. This new paradigm termed as Edge-of-Things (EoT) computing, operates closer to the IoT data source and allows computing, storage and service supply to be moved from Cloud to the local edge devices such as Smart phones, Smart gateways or routers and local PCs. These edge devices can offer computing, intelligence and storage capabilities on a smaller scale in real-time. Thus, EoT paradigm enables accurate healthcare service delivery with low response time. It helps to avoid delays and network failures that may interrupt or delay the decision process and healthcare service delivery.
However, the successful utilization of edge-of–things computing in a Smart healthcare system is still challenging. There exist several issues that need to be addressed such as novel network architecture and middleware platform for Edge-of-Things in healthcare system considering emerging technologies such as 5G wireless networks, software defined network and semantic computing; edge analytics for healthcare Big data; novel security and privacy methods; social intelligence into the edge node to host healthcare applications; and context-aware service management on the edge with effective quality of service (QoS) support and other issues.
Topics of Interest
This special issue targets a mixed audience of researchers, and practionars from both academia and healthcare industry to share and exchange new ideas, approaches, theories and practice to resolve the challenging issues of utilizing the Edge-of-Things technology for improving the efficiency, sustainability and reliability of smart healthcare systems. Therefore, the suggested topics of interest for this special issue include:
Novel Edgecomputing architecture for Smart healthcare monitoring system
Distributed Deep Learning on Edge devices for Smart healthcare data analysis
Energy-efficient data offloading and computing over Edge for Smart mobile healthcare
Techniques, algorithms and methods of processing smart healthcare data over Edge devices
Cognitive Edge computing for Smart healthcare system
New communications and networking protocols for Edge computing in Smart healthcare system
Programming models and toolkits for supporting Edge Computing for Smart healthcare system
Trust, privacy and security issues in Edge computing for Smart healthcare
Simulation, emulation and testbed support of Smart healthcare systems over Edge computing
Autonomic resource management on Edge devices for Smart healthcare
Mobility and context-aware information processing in edge computing for healthcare applications
Emerging Smart healthcare services and applications over Edge computing
Manuscript Due: Dec 30, 2017
First notification: March30, 2018
Revised paper due: May15, 2018
Notification of re-review: June15, 2018
Final manuscript due: June 30, 2018
Expected publication date: October 2018
All submissions have to be prepared according to the Guide for Authors published in the Journal website at http://www.journals.elsevier.com/computers-and-electrical-engineering/. Authors should submit their papers at https://www.evise.com/profile/#/COMPELECENG/loginby selecting “SI-eoth” from the “Issues” pull-down menu during the submission process. Submitted papers must not have been previously published or be under consideration for publication elsewhere. A submission based on one or more papers that appeared elsewhere has to comprise major value-added extensions over what appeared previously (at least 30% new material). Authors are requested to attach to the submitted paper their relevant, previously published articles and a summary document explaining the enhancements made in the journal version.
Mohammad Mehedi Hassan, King Saud University, Riyadh Saudi Arabia (firstname.lastname@example.org)
Min Chen, Huazhong University of Science and Technology, Wuhan, China (email@example.com)
A.M. Goscinski, Deakin University, Burwood, Australia ( firstname.lastname@example.org)