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Intelligent Optimization, Decision-Making and Privacy Preservation in Cyber–Physical Systems
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College of Science, Nanjing Forestry University, Nanjing 210037, China
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
College of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China
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Intelligent Optimization, Decision-Making and Privacy Preservation in Cyber–Physical Systems

Abstract submission deadline
31 May 2025
Manuscript submission deadline
31 August 2025
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1357

Topic Information

Dear Colleagues,

With the rapid advancements in technologies like 5G/6G communication and artificial intelligence, cyber-physical systems (CPSs) play a vital role in diverse applications like smart grids, autonomous vehicles, and industrial automation. CPSs integrate data transmission channels with physical devices, employing a 5C hierarchical architecture (connection, cyber, conversion, cognition, and configuration) and intelligent perception technology. This integration enhances the real-time optimization of computing and communication resources using mathematical models and computational algorithms.

Intelligent optimization, decision-making and privacy-preserving problems are crucial aspects that aim to improve efficiency, reliability and security in CPSs. This promotes the motivation for investigating machine learning, artificial intelligence and advanced optimization algorithms to control CPSs. This Topic aims to bring together researchers and practitioners from academia and industry to present the latest advancements in intelligent optimization, decision-making and privacy-preserving in CPSs. We also invite contributions that explore the application of advanced mathematical tools in CPSs. Topics of interest include, but are not limited to:

  1. Intelligent optimization and security control in CPSs and its industrial application;
  2. Advanced privacy-preserving algorithms for CPSs and its industrial application;
  3. Application of statistical methods and big data processing and analysis in CPSs for smart grids;
  4. Security optimization and privacy-preserving in intelligent transportation CPSs;
  5. AI-based big data analysis and decision-making in power CPSs;
  6. Distributed privacy-preserving estimation in CPSs;
  7. Advanced mathematical modeling and analysis in intelligent complex network systems within CPSs.

Prof. Dr. Lijuan Zha
Prof. Dr. Jinliang Liu
Prof. Dr. Jian Liu
Topic Editors

Keywords

  • cyber-physical system
  • intelligent optimization
  • privacy-preserving
  • decision-making
  • security control

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci 		2.5 5.3 2011 18.4 Days CHF 2400 Submit
Automation
automation 		- 2.9 2020 24.1 Days CHF 1000 Submit
Computers
computers 		2.6 5.4 2012 15.5 Days CHF 1800 Submit
Electronics
electronics 		2.6 5.3 2012 16.4 Days CHF 2400 Submit
Sensors
sensors 		3.4 7.3 2001 18.6 Days CHF 2600 Submit
Journal of Cybersecurity and Privacy
jcp 		- 5.3 2021 26.9 Days CHF 1000 Submit
Mathematics
mathematics 		2.3 4.0 2013 18.3 Days CHF 2600 Submit

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Published Papers (1 paper)

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Article
Personalized Federated Learning Scheme for Autonomous Driving Based on Correlated Differential Privacy
by Yuan Tian, Yanfeng Shi, Yue Zhang and Qikun Tian
Sensors 2025, 25(1), 178; https://doi.org/10.3390/s25010178 - 31 Dec 2024
Viewed by 415
Abstract
In the era of big data, advanced data processing devices and smart sensors greatly benefit us in many areas. As for each individual user, data sharing can be an essential part of the process of data collection and transmission. However, the issue of [...] Read more.
In the era of big data, advanced data processing devices and smart sensors greatly benefit us in many areas. As for each individual user, data sharing can be an essential part of the process of data collection and transmission. However, the issue of constant attacks on data privacy arouses huge concerns among the public. This work proposes a personalized federated learning method associated with correlated differential privacy for autonomous driving. First, instead of transmitting raw data to the server following collection, a device that employs federated learning can perform calculations to obtain the training model at each node. Second, we specifically perform a correlated classification analysis to encrypt data that share high relevance, which can minimize the system cost. Then, correlated differential privacy is utilized to achieve the preservation of data privacy before sharing. In contrast to the traditional differential privacy, the proposed solution guarantees enhanced privacy to meet the demands of customization. The experimental results show that our scheme is more refined in terms of user heterogeneity and the utility of data than others without violating privacy. Full article
(This article belongs to the Topic Intelligent Optimization, Decision-Making and Privacy Preservation in Cyber–Physical Systems)
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