Engineering Applications of Artificial Intelligence
Special Issue on Pushing Artificial Intelligence to Edge: Emerging Trends, Issues and Challenges
• 大类 : 工程技术 - 2区
• 小类 : 自动化与控制系统 - 3区
• 小类 : 计算机：人工智能 - 3区
• 小类 : 工程：电子与电气 - 3区
• 小类 : 工程：综合 - 2区
Driven by the Internet of Things (IoT), a new computing model - Edge computing - is currently evolving, which allows IoT data processing, storage and service supply to be moved from Cloud to the local Edge devices such as smart phones, smart gateways or routers and base stations that can offer computing and storage capabilities on a smaller scale in real-time. EoT pushes data storage, computing and controls closer to the IoT data source(s); therefore, it enables each Edge device to play its own role of determining what information should be stored or processed locally and what needs to be sent to the Cloud for further use. Thus, EoT enables IoT services to meet the requirements of low latency, high scalability and energy efficiency, as well as to mitigate the traffic burdens of the transport network.
However, current expansion of the IoT and digital transformation is generating new demands on computing and networking infrastructures across all industries (automotive, aerospace, life safety, medical, entertainment and manufacturing, etc). Hence, it is becoming challenging for Edge computing to deal with these emerging IoT environments. In order to overcome this issue, there is a need for intelligent Edge or Artificial Intelligence (AI) powered Edge computing (Edge-AI) to manage all the new data needs from these sectors. AI with its machine learning (ML) abilities can be fused into Edge to extend its power for intelligently investigating, collecting, storing and processing the large amounts of IoT data to maximize the potential of data analytics and decision-making in real time with minimum delay. There are many application areas where Edge-AI can be used, such as fall detection systems for the elderly, intelligent clothes for safety applications, smart access systems, smart camera, smart fitness systems, pet monitoring systems, self-predictive electric drives, and so on.
While researchers and practitioners have been making progress within the area of Edge-AI, still there exist several challenging issues that need to be addressed for its large-scale adoption. Some of these issues are: credibility and trust management, distributed optimization of multi-agent system in Edge, self-organization, self-configuration, and self-discovery of edge nodes, lack of standards in containerization area (Docker, Open Container Initiative etc.) for Edge-AI, security risk for the data that needs to be processed at the edge, lack of efficient scheduling algorithms to optimize AI or machine learning in Edge computing structure, new operating system for edge artificial intelligence, etc.
This special issue targets a mixed audience of researchers, academics and industries from different communities to share and exchange new ideas, approaches, theories and practice to resolve the challenging issues associated with the leveraging of intelligent Edge paradigm. Therefore, the suggested topics of interest for this special issue include, but are not limited to:
Novel middleware support for Edge intelligence
Network function virtualization technologies that leverage Edge intelligence
Trust, security and privacy issues for Edge-AI
Distributed optimization of multi agent systems for Edge intelligence
Self-organization, self-configuration, and self-discovery of Edge node
Semantic interoperability for Edge intelligence
Autonomic resource management for Edge-AI
Mobility, Interoperability and Context-awareness management for Edge-AI
Container based approach to implement AI in Edge
Applications/services for Edge artificial intelligence