Abstract

Cyber–Physical–Social Systems (CPSS) integrate the cyber, physical, and social spaces together. One of the ultimate goals of cyber–physical–social systems is to make our lives more convenient and intelligent by providing prospective and personalized services for users. To achieve this goal, a wide range of data in CPSS are employed as the starting point for research, since the data contain the users’ historical behavior trajectory and the users’ demand preference. Generated and collected from social and physical spaces and integrated into the cyberspace, CPSS data are complex and heterogeneous, recording all aspects of users’ lives in the forms of image, audio, video, and text. Generally, the collected or generated data in CPSS satisfy the 4Vs (volume, variety, velocity, and veracity) of big data. Thus, knowing how to deal with CPSS big data efficiently is the key to providing services for users. From another perspective, CPSS big data are specified as the global historical data and the local real-time data. Cloud computing, as a powerful paradigm for implementing the data-intensive applications, has an irreplaceable role in processing global historical data. On the other hand, with the increasing computing capacity and communication capabilities of mobile terminal devices, fog-edge computing, as an important and effective supplement of cloud computing, has been widely used to process local real-time data. Therefore, the question of how to systematically and efficiently process CPSS big data (including both the global historical data and the local real-time data) in CPSS has become the key for providing services. The goal of this Special Section is therefore to provide insights and views into the area of Cloud-Fog-Edge Computing in CPSS, as well as to provide directions for research in the field.

Highlights

  • In the article ‘‘Dynamic computation offloading based on graph partitioning in mobile edge computing,’’ by Li et al, a multiuser computation offloading problem for mobile-edge computing is studied

  • In the article titled ‘‘Cross-modal retrieval for Cyber–Physical–Social Systems (CPSS) data,’’ by Zhong et al, a nonlinear discrete cross-modal hashing method based on concise binary classification, called NDCMH, is proposed to fully investigate the nonlinear relationship embedding discrete optimization as well as the hashing functions learning for CPSS data

  • The article titled ‘‘Statistical behavior guided block allocation in hybrid cache-based edge computing for cyber– physical–social systems,’’ by Shen et al, proposes a statistical behavior guided block allocation (SBOA) scheme to process CPSS data so as to enhance the performance of hybrid cache

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Summary

Introduction

In the article ‘‘Dynamic computation offloading based on graph partitioning in mobile edge computing,’’ by Li et al, a multiuser computation offloading problem for mobile-edge computing is studied. IEEE ACCESS SPECIAL SECTION EDITORIAL: CLOUD-FOG-EDGE COMPUTING IN CYBER–PHYSICAL–SOCIAL SYSTEMS (CPSS)

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