A Survey on Edge and Edge-Cloud Computing Assisted Cyber-Physical Systems

Abstract : 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 paper, 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 literatures 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

 EXISTING SYSTEM :

 ? The challenge is not limited to the use of the next generation of intelligent equipment that natively support AI features, but mainly to adapt traditional approaches and embed them in the existing constrained device platforms. ? In this sense, it is clear that there should exist a balance of the computational resources among each one of these layers, and just as important as, an appropriate interconnection of them. ? In spite of the existence of several frameworks to assist and automate several data analysis tasks, usually being able to find ML models that outperform the ones developed by hand, most problems still require domain knowledge which refrains the complete process automation.

 DISADVANTAGE :

 ? The proposed solution to this problem as indicated in their study suggests that each IoT domain generates and maintains security keys for IoT devices belong to each domain. ? To solve this problem, the study adopted hierarchy-based key management, where services and applications credentials are composed of multiple keys based on the level of the application in the hierarchical schema. ? Providing access control strategy that meets the growing requirements of these networks is a challenging problem. ? Due to the constrained resources and distributed, heterogeneous, and scalable architecture of EC-assisted IoT networks, deployment of security services and applications over these networks represents one of the main challenging problems.

 PROPOSED SYSTEM :

 • In light of the defense for cyber security, the privacy computing framework proposed in allows private information exchanges, extensive permission of private information flow in a multi-interface scenario. • To deal with those challenges, a Trust Management System is proposed where the FC-CPS devices are the trustor and fog assisted nodes are the trustee. • In, a distributed platform for cloud and fog integrated sCPS(PsCPS) is proposed to relax challenges of such kind of integration. • The multi-vendor Mobile Edge Computing platforms proposed in motivates a brand new value chain, novel business opportunities and use cases that is totally out of imagination before.

 ADVANTAGE :

 ? To be more specific, FC enables repeatable structure in the EC concept, such that network developers can push computation capabilities out of the cloud computing layer to the EC servers in order to enable a robust and scalable performance. ? The authors in conduct a survey to analyze how EC can assist the performance of IoT networks. ? The performance of EC and cloud computing architectures are also compared in some IoT applications, such as smart transportation, smart city, and smart grid. ? EC-assisted IoT networks have enhanced network performance in terms of reduced latency (both communication and computation), reduced bandwidth usage, reduced device power consumption, and reduced packet data overhead.