Future wireless networks expect to accommodate a huge number of Internet of Things (IoT) devices and provision low latency, context-aware services and applications in a flexible and efficient manner. To meet these peculiarities, one of the key approaches is mobile edge computing (MEC), which brings storage and computation resources in the proximity of mobile devices, to offer the end-users low-latency and high-bandwidth access to both information and computation resources. In addition, edge servers can facilitate data analytics to explore the hidden value of the collected data from IoT devices to support real-time decision making and provide value-added services.

MEC systems have to deal with many issues and real-time challenges. It should support various services with distinct quality of service (QoS) requirements in terms of latency, data rate, reliability, and so on. In addition, the service demands exhibit spatial and temporal dynamics due to bursty requests and user mobility. It is of importance to manage the heterogeneous communication and device type, storage, and computational resources that jointly affect the perceived performance of users. Artificial Intelligence (AI) is also an emerging technology that has helped many other technologies in predicting future events and outages. The use of AI to support MEC quests in managing IoT devices is highly on-demand. The objective of this special session is to promote the benefits of MEC systems in IoT by addressing the aforementioned issues. Topics of interest include but are not limited to:

• Performance analysis of MEC systems
• Cooperative caching and business models
• Joint optimization of heterogeneous resource
• Dynamic caching update
• ML Algorithms and AI systems for MEC systems
• Wireless Internet of Things
• Virtual resource management
• Tasks offloading
• Interaction between edge and cloud
• Green MEC systems
• Service mitigation in MEC systems
• Protocols for cooperative edge systems
• Security and privacy in MEC systems
• Prototype platforms and trial deployment
• Big data aided MEC systems
• Mobility- and Quality of Information-aware service caching
• Multimedia and QoE management in MEC systems
• Connected Vehicles IoT systems
• Blockchain Technology assisted MEC in IoT

Workshop Co-Chairs

• Moayad Aloqaily, xAnanlytics Inc., Canada and Al Ain University, UAE
• Virginia Pilloni, University of Cagliari, Italy
• Danda B Rawat, Howard University, US
• Ning Zhang, University of Windsor, Canada

Technical Program Committee

• Qi Zhang, Aarhus University
• Giovanni Pau, Kore University of Enna
• Ammar Muthanna, Saint-Petersburg State University of Telecommunications
• Mohammad Hammoudeh, Manchester Metropolitan University
• Zhaohui Yang, King's College London
• Enrico Ferrera, Links Foundation
• Emad Abd-Elrahman, NTI-Egypt
• Luca Chiaraviglio, University of Rome Tor Vergata
• Safa Otoum, University of Ottawa, Canada
• Ismaeel Al Ridhawi, Kuwait College of Science, Kuwait
• Rasheed Hussain, Innopolis, Russia
• Ouns Bouachir, Zayed University, UAE
• Amjad Gawanmeh, University of Dubai, UAE
• Lewis Tseng, Boston College, USA
• Yaser Jararweh, Duquesne University, USA
• Haya Elayan, xAnalytics Inc., Canada
• Mohammad Alsmirat, Sharjah University, UAE
• Müge Erel-Özçevik, Celal Bayar University, Turkey

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Theme: The Impact of Artificial Intelligence on IoT

Name of the session:

Workshop on Mobile Edge Computing and Data Analytics for IoT (MECDA-IoT 2021)

Organized on:

29th June, 2021 at 10:30 – 11:30 EDT

Technical program of the session

10:30 – 10:40	Introduction by session chairs
10:40 – 11:05	[Q&A with paper presenter 1] – Asynchronous Hybrid Deep Learning (AHDL): a Deep Learning Based Resource Mapping in DVFS Enabled Mobile MPSoCs
11:05 – 11:30	[Q&A with paper presenter 2] – Environmental Sound Classification with Tiny Transformers in Noisy Edge Environments

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