Intelligent IoT Systems with Mobile/Multi-Access Edge Computing (MEC)

Dear Colleagues,

New technologies such as mobile and multi-access edge computing (MEC) have gained significant attention in recent years, offering flexible and viable solutions that overcome the limitations of centralized cloud computing. Indeed, by enabling computation at the network edge, MEC realizes a series of advantages for applications, including  (i) context awareness; (ii) geo-distributed capabilities; and (iii) low latency.

Within this broad context, edge computing can take different forms subject to the exact definition of the edge, i.e., computation can be performed at a regional level, at (5G) mobile network base stations or even directly on an end device. This is possible due to advances in several domains including microelectronics, as well as artificial intelligence (AI) and machine learning (ML), together facilitating the emergence of a multitude of applications that increasingly bridge the gap between the cyber and the physical worlds. Examples include, but are not limited to, augmented reality (AR), virtual reality (VR), digital twins (DT), virtual simulations, manufacturing/Industry 4.0, connected automated mobility (CAM)/ automotive, etc.

In this Special Issue, original research articles and reviews focusing on the support of intelligent IoT solutions and edge computing are welcome. Research areas may include (but are not limited to) the following:

• Novel system architectures for the support of intelligent IoT applications at the edge;
• New programming models for intelligent IoT applications in the compute continuum;
• Novel intelligent IoT applications/services on top of MEC platforms;
• Experimental performance evaluation/trials of MEC-enabled intelligent IoT applications;
• Service/resource management and orchestration solutions for the support of intelligent IoT services/applications at the edge;
• Applications of (distributed) machine learning (ML) techniques for the edge, e.g., federated learning, split learning, etc.;
• Software optimizations for the support of AI/ML workloads at the edge, e.g., Tiny ML applications.

We look forward to receiving your contributions. 

Dr. Evangelos Maltezos
Dr. Konstantinos Katsaros
Dr. Eleftherios Ouzounoglou
Dr. Angelos Amditis
Guest Editors

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