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In recent years, the investigations on cyber-physical systems (CPS) have become increasingly popular in both academia and industry. A primary obstruction against the booming deployment of CPS applications lies in how to process and manage large amounts of generated data for decision making. To tackle this predicament, researchers advocate the idea of coupling edge computing, or edge-cloud computing into the design of CPS. However, this coupling process raises a diversity of challenges to the quality-of-services (QoS) of CPS applications. In this article, we present a survey on edge computing or edge-cloud computing assisted CPS designs from the QoS optimization perspective. We first discuss critical challenges in service latency, energy consumption, security, privacy, and reliability during the integration of CPS with edge computing or edge-cloud computing. Afterwards, we give an overview on the state-of-the-art works tackling different challenges for QoS optimization, and present a systematic classification during outlining literature for highlighting their similarities and differences. We finally summarize the experiences learned from surveyed works and envision future research directions on edge computing or edge-cloud computing assisted CPS optimization.
This Special Section on ‘`Cloud-Edge Computing for Cyber-Physical Systems and Internet-of-Things’' is oriented to the dissemination of a few of those latest research and innovation results, covering many aspects of design, optimization, implementation, and evaluation of emerging cloud-edge solutions for CPS and IoT applications. The selected high-quality contributions cover a broad range of novel technologies and application scenarios in CPS and IoT. We hope that these accepted papers will produce long-lasting impacts, as well as stimulating and encouraging the international community to work on this exciting and impactful topic.
The Industrial Revolution, which originally involved the change from an agrarian and handicraft economy to a market dominated by factory mechanization during the early 18th century, has profoundly shaped the world. It has progressed through four disruptive phases: Industry 1.0 through Industry 4.0. Industry 1.0 encompassed early automation, while Industry 2.0 began at the end of the 19th century, when enormous technological advances were made, such as mass production, electrification, and new modes of transportation. Industry 3.0 began during the 1970s, a decade that gave rise to the electronics, telecommunications, and computing that enable full automation and robotics. Industry 4.0 kicked off at the dawn of the third millennium, marked by the ubiquitous use of Internet technologies, which have radically transformed how people, society, and industry interact.
Industrial agents (IAs) [1] are multiagent-based systems (MASs) [2] that, for many years, have been advocated as a promising and realistic solution for an emerging set of industrial challenges. In the past, MASs fell into the scope of enterprise agility [3]-[8], and now, more than ever, pertain to the industrial digital transformation and sustainability spheres. MAS technology is being applied to several industrial applications in the cyber-physical system (CPS) context, namely, in smart production, smart electric grids, smart logistics, and smart health care [9]. To understand the future potential of IAs, one must first have a sufficiently concise view of the past and present efforts, i.e., understand their early applications and current directions. Such a view is necessary because, over the last decades, the concept of the IA has proven to be a bit of a moving target, adjusting to the needs, visions, and technologies of each era.
Industry 4.0 vision and its mandated digital transformation are radically reshaping the way business is carried out and the way overall industrial processes and collaborations are operating. In this work, the objective is to analyze the current level of adoption of Industry 4.0, via the footprint available in industrial and academic research works. The analysis performed reveals insights on how Industry 4.0 has impacted and is still influencing research and innovation in industrial systems, services, and business approaches. It also reveals pertinent trends on key enabling features, technologies and challenges associated with this 4th industrial revolution, mainly focusing on the pathways for wider industrial adoption of Industry 4.0-compliant technologies and solutions.
Future industrial systems and applications are expected to be complex constellations of cyberphysical systems (CPSs) where intel l igent networked embedded devices play a pivotal role toward the realization of new sophisticated industrial scenarios. The prevalence of multifaceted devices enables new avenues for monitoring at large scale via Internet of Things (IoT) technologies, and, when coupled with the real-time analysis of massive amounts of data, it results in new insights that can enhance decision-making processes and provide a competitive business advantage. How to collect, process, analyze, and interpret big data is a challenge that affects all industries, and, if effectively addressed, it would offer numerous operational benefits. This article discusses some of the main architectural issues related to collecting and handling big data for analysis linked to IoT and cloud technologies in the industrial context. The aim is to provide a high-level introductory view of this topic, underpinned with examples from popular frameworks, and discuss open research questions and future directions.