Intelligent Optimization, Decision-Making and Privacy Preservation in Cyber–Physical Systems

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

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Automation automation	2.0	4.1	2020	30.9 Days	CHF 1200	Submit
Computers computers	4.2	7.5	2012	17.5 Days	CHF 1800	Submit
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
Journal of Cybersecurity and Privacy jcp	-	9.1	2021	21.5 Days	CHF 1200	Submit
Mathematics mathematics	2.2	4.6	2013	17.3 Days	CHF 2600	Submit
Sensors sensors	3.5	8.2	2001	17.8 Days	CHF 2600	Submit

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Published Papers (8 papers)

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21 pages, 20116 KB  

Open AccessArticle

Hierarchical Data-Driven and PSO-Based Energy Management of Hybrid Energy Storage Systems in DC Microgrids

by Sujatha Banka and D. V. Ashok Kumar

Automation 2026, 7(2), 50; https://doi.org/10.3390/automation7020050 - 13 Mar 2026

Viewed by 480

Abstract

In the era of renewable dominated grids, integration of dynamic loads such as EV charging stations have increased the operational challenges in multifolds, particularly in DC microgrids (DC MGs). Traditional battery-dominated grid energy management strategies (EMSs) are often not capable of handling fast [...] Read more.

In the era of renewable dominated grids, integration of dynamic loads such as EV charging stations have increased the operational challenges in multifolds, particularly in DC microgrids (DC MGs). Traditional battery-dominated grid energy management strategies (EMSs) are often not capable of handling fast transients due to the limitations of battery electrochemistry. To overcome this limitation, a hierarchical hybrid energy management strategy is proposed that uses the combination of data-driven and metaheuristic algorithms. The designed optimization framework consists of particle swarm optimization (PSO) and a neural network (NN) implemented in the central controller of a 4-bus ringmain DC MG. An efficient decoupling of fast and slow storage dynamics is performed, where the supercapacitor (SC) is optimized using the NN and the battery is optimized using PSO. This selective optimization reduces the computational overhead on the PSO making it more feasible for real-time implementation. The designed hybrid PSO-Neural EMS framework is initially designed on MATLAB and further validated on a real-time hardware setup. Robustness of the control scheme is verified with various case studies, such as renewable intermittency, dynamic loading and partial shading scenarios. An effective optimization of the SC in both transient and heavy load scenarios are observed. LabVIEW interfacing is used for MODBUS-based interaction with PV emulators and DC-DC converters. Full article

(This article belongs to the Topic Intelligent Optimization, Decision-Making and Privacy Preservation in Cyber–Physical Systems)

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27 pages, 2393 KB  

Open AccessArticle

A Hybrid Consensus Optimization Algorithm for Blockchain in Supply Chain Traceability

by Yuhua Xu, Yixin Lei, Lianzhe Tang, Xin Li and Zhixin Sun

Electronics 2026, 15(1), 77; https://doi.org/10.3390/electronics15010077 - 24 Dec 2025

Cited by 1 | Viewed by 852

Abstract

As supply chains expand in scale and the number of participating nodes increases, existing consensus algorithms are increasingly showing limitations in scalability, communication complexity, and handling complex network environments. To address the shortcomings of blockchain consensus mechanisms in master node selection, scalability, and [...] Read more.

As supply chains expand in scale and the number of participating nodes increases, existing consensus algorithms are increasingly showing limitations in scalability, communication complexity, and handling complex network environments. To address the shortcomings of blockchain consensus mechanisms in master node selection, scalability, and communication complexity in supply chain traceability scenarios, this paper proposes a blockchain hybrid consensus optimization algorithm named Node Rating-Based and Grouping Raft cluster Practical Byzantine Fault Tolerance (NG-RPBFT) for supply chain traceability. This algorithm builds a multi-index comprehensive rating model for nodes to comprehensively evaluate consensus nodes, reasonably groups consensus nodes, adopts an inter-group and intra-group dual consensus mechanism to achieve efficient data synchronization, and introduces Brotli data compression technology to optimize message load, effectively enhancing system performance. Experimental results confirm that this algorithm significantly improves the scalability of the consensus mechanism and performs exceptionally well in consensus efficiency, making it suitable for complex application scenarios such as supply chain traceability under CPS scenarios. Full article

(This article belongs to the Topic Intelligent Optimization, Decision-Making and Privacy Preservation in Cyber–Physical Systems)

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27 pages, 519 KB  

Open AccessArticle

Dual-Algorithm Framework for Privacy-Preserving Task Scheduling Under Historical Inference Attacks

by Exiang Chen, Ayong Ye and Huina Deng

Computers 2025, 14(12), 558; https://doi.org/10.3390/computers14120558 - 16 Dec 2025

Viewed by 563

Abstract

Historical inference attacks pose a critical privacy threat in mobile edge computing (MEC), where adversaries exploit long-term task and location patterns to infer users’ sensitive information. To address this challenge, we propose a privacy-preserving task scheduling framework that adaptively balances privacy protection and [...] Read more.

Historical inference attacks pose a critical privacy threat in mobile edge computing (MEC), where adversaries exploit long-term task and location patterns to infer users’ sensitive information. To address this challenge, we propose a privacy-preserving task scheduling framework that adaptively balances privacy protection and system performance under dynamic vehicular environments. First, we introduce a dynamic privacy-aware adaptation mechanism that adjusts privacy levels in real time according to vehicle mobility and network dynamics. Second, we design a dual-algorithm framework composed of two complementary solutions: a Markov Approximation-Based Online Algorithm (MAOA) that achieves near-optimal scheduling with provable convergence, and a Privacy-Aware Deep Q-Network (PAT-DQN) algorithm that leverages deep reinforcement learning to enhance adaptability and long-term decision-making. Extensive simulations demonstrate that our proposed methods effectively mitigate privacy leakage while maintaining high task completion rates and low energy consumption. In particular, PAT-DQN achieves up to 14.2% lower privacy loss and 19% fewer handovers than MAOA in high-mobility scenarios, showing superior adaptability and convergence performance. Full article

(This article belongs to the Topic Intelligent Optimization, Decision-Making and Privacy Preservation in Cyber–Physical Systems)

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14 pages, 525 KB  

Open AccessArticle

A Fault-Tolerant Proportional-Integral-Derivative Load Frequency Control for Power Systems Operating Under a Random Event-Triggered Scheme

by Chenyu Ling, Junyi Luo, Kaibo Shi and Yanjun Liu

Electronics 2025, 14(12), 2443; https://doi.org/10.3390/electronics14122443 - 16 Jun 2025

Viewed by 1027

Abstract

This paper proposes a novel fault-tolerant proportional-integral-derivative (PID) control method for load frequency control (LFC) in power systems, addressing performance degradation caused by controller failures. Firstly, a unified fault model for actuators is established, and a fault-tolerant PID control strategy is developed. Subsequently, [...] Read more.

This paper proposes a novel fault-tolerant proportional-integral-derivative (PID) control method for load frequency control (LFC) in power systems, addressing performance degradation caused by controller failures. Firstly, a unified fault model for actuators is established, and a fault-tolerant PID control strategy is developed. Subsequently, a random event-triggering scheme (RETS) is introduced, utilizing a Poisson random probability sampling period to minimize redundant network communication resource usage. The stability of the closed-loop system is rigorously verified through Lyapunov function analysis. Finally, the effectiveness of the proposed control method and the superiority of RETS are demonstrated via an illustrative case study on an isolated power system. Full article

(This article belongs to the Topic Intelligent Optimization, Decision-Making and Privacy Preservation in Cyber–Physical Systems)

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23 pages, 319 KB  

Open AccessArticle

Conditions for Guaranteeing Non-Overshooting Control of Nonlinear Systems with Full-State Constraints

by Xiang-Qin Xiang and Chi Zhang

Appl. Sci. 2025, 15(11), 5816; https://doi.org/10.3390/app15115816 - 22 May 2025

Viewed by 773

Abstract

In this paper, the problem of non-overshooting tracking control (NOTC) for a class of nonlinear systems with full-state constraints (FSCs) is studied. Firstly, this paper introduces the mapping constraint function to solve the FSC control problem and transform the controlled system into a [...] Read more.

In this paper, the problem of non-overshooting tracking control (NOTC) for a class of nonlinear systems with full-state constraints (FSCs) is studied. Firstly, this paper introduces the mapping constraint function to solve the FSC control problem and transform the controlled system into a new nonlinear system. Then, to obtain a closed-loop system that can solve the expression of tracking error, this paper transforms the n-order system into a system in which only the n-th subsystem is nonlinear by coordinate transformation, that is, subsystem 1 to subsystem $n-1$ are linear. Finally, according to the closed-loop system (CLS), the expressions of the first state of CLSs with $n=1$, $n=2$, $n=3$, and $n\ge 4$ are solved, respectively. By analyzing these expressions, a wider range of conditions with NOTC are obtained. This algorithm obtains more conditions with non-overshooting. Compared with the existing results, the algorithm in this paper reduces the conservatism. Finally, the algorithm is applied to the single-link robot system, and the effectiveness of the algorithm is verified. That is, the algorithm in this paper not only makes all signals of the CLS bounded, but also makes the overshoot of the system zero. Full article

(This article belongs to the Topic Intelligent Optimization, Decision-Making and Privacy Preservation in Cyber–Physical Systems)

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14 pages, 1516 KB  

Open AccessArticle

The Application of the SubChain Salp Swarm Algorithm in the Less-Than-Truckload Freight Matching Problem

by Yibo Sun, Lei Yue, Yi Liu, Weitong Chen and Zhe Sun

Appl. Sci. 2025, 15(8), 4436; https://doi.org/10.3390/app15084436 - 17 Apr 2025

Cited by 2 | Viewed by 978

Abstract

The less-than-truckload (LTL) freight problem is a general pain point in logistics applications. Its challenge resides in the fact that these loads cannot be shipped in a timely manner due to their relatively small volumes. Traditional LTL matching methods are challenged by delays [...] Read more.

The less-than-truckload (LTL) freight problem is a general pain point in logistics applications. Its challenge resides in the fact that these loads cannot be shipped in a timely manner due to their relatively small volumes. Traditional LTL matching methods are challenged by delays in updating logistic information and higher distribution costs. In order to solve LTL challenges, we developed a novel SubChain Salp Swarm Algorithm (SSSA) by improving the traditional Salp Swarm Algorithm with the utilization of a SubChain operation. Our method aims to find the optimal strategy for maintaining a balance between lower operating costs and customer satisfaction. Our SSSA method combines multiple disconnected SubChain points to separate individual chains to find local optima and obtain better convergence results in the final decision. We have compared our method with mainstream metaheuristic algorithms using logistics datasets from a road freight company in Hangzhou, and the results demonstrate that our method converges faster than other methods and has a lower variance. Our method solves the limitation of local optima observed in other optimization methods and improves customer service in relation to the transportation issue. Full article

(This article belongs to the Topic Intelligent Optimization, Decision-Making and Privacy Preservation in Cyber–Physical Systems)

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25 pages, 1618 KB  

Open AccessArticle

Optimizing Post-Quantum Digital Signatures with Verkle Trees and Quantum Seed-Based Pseudo-Random Generators

by Maksim Iavich and Nursulu Kapalova

Computers 2025, 14(3), 103; https://doi.org/10.3390/computers14030103 - 14 Mar 2025

Cited by 6 | Viewed by 2635

Abstract

Nowadays, quantum computing is developing at an unprecedented speed. This will pose a serious threat to the security of widely used public-key cryptosystems in the near future. Scientists are actively looking for ways to protect against quantum attacks; however, existing solutions still face [...] Read more.

Nowadays, quantum computing is developing at an unprecedented speed. This will pose a serious threat to the security of widely used public-key cryptosystems in the near future. Scientists are actively looking for ways to protect against quantum attacks; however, existing solutions still face different limitations in terms of efficiency and practicality. This paper explores hash-based digital signature schemes, post-quantum vector commitments and Verkle tree-based approaches for protecting against quantum attacks. The paper proposes an improved approach to generating digital signatures based on Verkle trees using lattice based vector commitments. In order to further reduce the memory space, the paper offers the methodology of integrating a post-quantum secure pseudo-random number generator into the scheme. Finally, the paper proposes an efficient post-quantum digital signature scheme based on Verkle trees, which minimizes memory requirements and reduces the signature size. Our proposed framework has strong resistance to quantum attacks, as well as high speed and efficiency. This study is an important contribution to the elaboration of post-quantum cryptosystems, which lays the foundation for developing secure and practical digital signature systems in the face of emerging quantum threats. Full article

(This article belongs to the Topic Intelligent Optimization, Decision-Making and Privacy Preservation in Cyber–Physical Systems)

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20 pages, 2085 KB  

Open AccessFeature PaperArticle

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

Cited by 7 | Viewed by 4561

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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