AI-Based Algorithms in IoT-Edge Computing

Dear Colleagues,

In the 5G era, Internet-of-Things (IoT) applications will increasingly become part of people’s daily lives. IoT-Edge Computing (IEC) is a promising technology to facilitate the progress of the Internet-of-Things in the 5G era. The IEC equipment is deployed in proximity to IoT users to provide computation with low latency. The efficiency and effectiveness of IoT edge computing are strongly correlated to the features of user behaviors. The dynamics and variety of user behaviors will influence the decision-making of operators and equipment deployment of IEC from all digitally-connected environments. Thus, a holistic user behaviors analysis is desirable for improving the efficiency and effectiveness of IoT edge computing.

Artificial intelligence (AI) algorithms have recently been adapted to various research domains, including computer vision, natural language processing, voice recognition, etc. In addition, AI-based algorithms in line with IoT-edge computing have made a key breakthrough and technical direction in achieving high efficiency and adaptability in a variety of new applications, such as smart wearable devices in healthcare, smart automotive industry, recommender systems, and financial analysis. Recently, AI algorithms emerged in the edge networking and IoT application domain. The design and application of AI techniques/algorithms for edge IoT network management, operations, and automation can improve the way we address networking today, such as topology discovery, network measurement, network monitoring, network modeling, network control, and so on. On the other hand, network design and optimization for AI applications address a complementing topic, namely the support of AI-based systems through novel networking techniques, including new architectures and performance models for IoT edge computing. The networking research community looks upon all of these challenges as opportunities in the Machine Learning era, showing edge computing applications in the IoT.

The main aim of this Special Issue is to integrate novel approaches efficiently, focusing on the performance evaluation and the comparison with existing solutions of AI-enabled algorithms on IoT edge computing.

Topics of interest include, but are not limited to, the following scope:

• AI-enabled algorithms for edge computing architectures, frameworks, platforms, and protocols for IoT;
• Machine learning techniques in edge computing for IoT;
• Edge network architecture and optimization for AI applications at scale;
• AI Algorithms for dynamic and large-scale topology discovery;
• AI algorithms for wireless network resource management and control;
• Energy-efficient edge network operations via AI algorithms;
• Deep learning and reinforcement learning in network control and management;
• Self-learning and adaptive networking protocols and algorithms;
• AI modeling and performance analysis in edge computing for IoT.

Prof. Dr. Arun Kumar Sangaiah
Guest Editor

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