Recently, we have witnessed rapid deployment of various IoT-based applications, which is leading to Industry 4.0 revolution – the backbone of the underlying network infrastructure supported by 5G and upcoming 6G technologies. Some of the IoT-based applications (e.g., autonomous vehicles, e-healthcare, and smart surveillance systems) are being used for the benefits of the societies around the world. For example, doctors can diagnosis patients remotely in e-healthcare, autonomous vehicles can deliver necessary goods to customer premises, and smart robots can be used for surveillance. Most of these applications rely on AI-based control models and techniques to make independent decisions when necessary. However, most of these applications require “extra low-latency” and “ultra-high-speed” response time to provide Quality of Service (QoS) and Quality of Experience (QoE) to end users. It is, therefore, envisaged that 5G and 6G (i.e., beyond 5G – B5G) technologies can revolutionize the aforementioned applications in their respective domains in the years to come. Hence, with such infrastructure, low round trip delay is expected for most mission-critical applications such as, e-healthcare, robotic surgery, and surveillance.
To support quick response time for mission-critical applications, quick decisions need to be made with respect to various data streams. For example, autonomous vehicles need to make quick decisions with respect to obstacles detection in their surroundings. In this context, AI-based techniques and models can be executed on edge servers, rather than remote cloud servers, to have real-time response. In B5G, various devices are expected to be inter-connected ubiquitously with the help of high communication coverage using dense deployed Heterogeneous Networks (HetNets) and machine-to-machine (M2M) communication. It creates high availability of the network resources close to the proximity of the end users, which in turn results in low latency for most of the applications. By off-loading different tasks directly to the edge servers, high performance gain can be achieved.
The aim of this special section is to invite contributions from academia and industry on the following topics:mentioned below.
AI models for network resources management using edge intelligence in 5G and 6G
AI models for security and privacy preservation using edge intelligence in B5G
AI models for location management of ubiquitous inter-connected devices using HetNets and M2M
AI techniques for power and energy management using edge intelligence in B5G
Unpublished manuscripts, or extended versions of papers presented at related conferences, are welcome. Submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if they are completely re-written or substantially extended (more than 50%), and must be referenced. All submitted papers will be peer-reviewed using the normal standards of CAEE. By submitting a paper to this issue, the authors agree to referee one paper (if asked) within the time frame of the special section.
Before submission, authors should carefully read the Guide for Authors available at https://www.elsevier.com/journals/computers-and-electrical-engineering/0045-7906/guide-for-authors Authors should submit their papers through the journal’s web submission tool at https://www.editorialmanager.com/compeleceng/default.aspx by selecting “VSI-aie” pull-down menu during the submission process. For additional questions, contact the Main Guest Editor.
Submission of manuscript: September 15, 2020
First notification: December 15,2020
Submission of revised manuscript: February 15, 2021
Notification of the re-review: April 15, 2021
Final notification: May 30, 2021
Final paper due: June 30, 20210
Publication: Q4-2021 (tentative)October 2021
Neeraj Kumar [Main Guest Editor], Thapar Institute of Engineering and Technology, Patiala, Punjab, India
Email: email@example.com, firstname.lastname@example.org
Rajkumar Buyya [Fellow IEEE], Melbourne University, Australia. Email: email@example.com
Neeraj Kumar is working as a Professor in the Department of Computer Science and Engineering, Thapar Institute of Engineering & Technology, Patiala (Pb.), India. He has published more than 400 technical research papers in leading journals and conferences having h-index of 51 with more than 8800 citations to his credit. He is editorial board members of ACM Computing Survey, IEEE Transactions on Sustainable Computing, JNCA, Elsevier, Computer Communications, Elsevier, International Journal of Communication Systems, Wiley, Security and Communication, John Wiley, and Journal of Networks and Computer Applications, Elsevier. He is Senior member of IEEE.
Rajkumar Buyya is a Redmond Barry Distinguished Professor and Director of the Cloud Computing and Distributed Systems (CLOUDS) Laboratory at the University of Melbourne, Australia. He has authored over 625 publications. He is one of the highly cited authors in computer science and software engineering worldwide (h-index=134, g-index=294, 95,100+ citations). Dr. Buyya is recognized as a “Web of Science Highly Cited Researcher” for four consecutive years since 2016, a Fellow of IEEE, and Scopus Researcher of the Year 2017 with Excellence in Innovative Research Award by Elsevier and recently (2019) received “Lifetime Achievement Awards” from two Indian universities. More details are on: http://www.buyya.com