Edge intelligence‐enabled cyber‐physical systems

With the advent of the Internet of everything era, people’s demand for intelligent Internet of Things (IoT) devices is steadily increasing. A more intelligent cyber-physical system (CPS) is needed to meet the diverse business requirements of users, such as ultra-reliable low-latency communication, high quality of services (QoS), and quality of experience (QoE).1 Edge intelligence (EI) is recognized by academia and industry as one of the key emerging technologies for the CPS, which provides the ability to analyze data at the edge rather than sending it to the cloud for analysis, and will be a key enabler to realize a world of a trillion hyperconnected smart sensing devices.2 As a distributed intelligent computing paradigm in which computation is largely or completely performed at distributed nodes, EI provides for the rapid development of artificial intelligence (AI) and edge computing resources to support real-time insight and analysis for applications in CPS, which brings memory, computing power and processing ability closer to the location where it is needed, reduces the volumes of data that must be moved, the consequent traffic, and the distance the data must travel.3 As an emerging intelligent computing paradigm, EI can accelerate content delivery and improve the QoS of applications, which is attracting more and more research attentions from academia and industry because of its advantages in throughput, delay, network scalability and intelligence in CPS.4 EI-enabled CPS integrates and merges multiple disciplines. EI-enabled CPS embodies distributed cooperation and learning intelligence, and provides fast intelligent autonomous response at the edge. Thus, it is an example of the emerging paradigm of distributed intelligent systems and has become one of the most popular trends in smart industry, agriculture, healthcare, home, transportation and so forth.5 Since EI-enabled CPS provides novel distributed computing and processing ability and enables rapid machine-to-machine communication and machine-to-human interaction, EI assisted IoT takes localized processing farther away from the network right down to the sensor by pushing the computing processes even closer to the data sources, which also provides multidisciplinary novel solutions and interactions to improve QoS and QoE.6 It is clear that EI-enabled CPS promotes a large class of applications and has emerged with a great potential to change our lives and improve user’s QoE. However, EI also brings us new challenges, such as costs, communications, data moving and management, security and privacy issues. The objective of this special issue is to present a collection of high-quality research papers that report the latest research advances addressing the related challenges and perspectives in the area of EI-enabled CPS. Our goal is to shed light on the multifaceted aspects of this emerging system, including system model and architecture, hardware accelerators, network virtualization, computation offloading and mobility management, intelligent learning techniques, data sensing, fusion, moving and management, multimedia analytics and processing, low-cost communication, QoS and QoE improvements, security and privacy, as well as novel applications, case studies and test beds. After a rigorous and careful review process performed by reviewers and the Guest Editors, 8 papers, from an open call and the 13th IEEE/ACM International Conference on Utility and Cloud Computing (UCC2020), have been accepted addressing the subjects classified in the following categories: (i) data sensing, management and resource allocation; (ii) privacy and security; and (iii) novel applications.