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Artificial Intelligence and Machine Learning in Cyber–Physical Systems
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School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
School of Software, Dalian University of Technology, Dalian 116621, China
School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China
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Artificial Intelligence and Machine Learning in Cyber–Physical Systems

Abstract submission deadline
31 October 2025
Manuscript submission deadline
31 December 2025
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3166

Topic Information

Dear Colleagues,

We are pleased to invite you to submit your work to the Special Topic titled “Artificial Intelligence and Machine Learning in Cyber–Physical Systems”, which will be published in Future Internet, Eng, Sensors, Electronics, Information, Sustainability, and AI. This Topic aims to integrate AI and ML techniques into Cyber–Physical Systems (CPS). These systems, which bridge the physical and digital worlds, are critical in domains such as manufacturing, healthcare, transportation, and smart cities. AI and ML enhance CPS by enabling real-time data analysis, decision making, and automation, thus increasing the intelligence and adaptability of such systems. With the increasing need for green technology, sustainable solutions, such as reducing energy consumption and promoting resource-efficient designs, will also be welcomed. This Special Topic aims to explore cutting-edge research and applications that address the challenges and opportunities in this field. In this Topic, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: 1. AI-driven optimization and control in CPS; 2. Security and privacy in AI-powered CPS; 3. Resource-aware AI algorithms for green CPS; 4. AI-enabled solutions for sustainable smart cities and societies; 5. Applications of deep learning in autonomous CPS; 6. Predictive maintenance and fault diagnosis in CPS; 7. AI and ML for smart manufacturing and Industry 4.0; 8. AI-enabled IoT and edge computing in CPS. We look forward to receiving your contributions.

Dr. Wei Wang
Dr. Junxin Chen
Dr. Jinyu Tian
Topic Editors

Keywords

  • cyber–physical systems
  • artificial intelligence
  • machine learning
  • real-time data processing
  • autonomous systems

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Electronics
electronics 		2.6 5.3 2012 16.4 Days CHF 2400 Submit
Eng
eng 		- 2.1 2020 21.5 Days CHF 1200 Submit
Future Internet
futureinternet 		2.8 7.1 2009 16.9 Days CHF 1600 Submit
Information
information 		2.4 6.9 2010 16.4 Days CHF 1600 Submit
Sensors
sensors 		3.4 7.3 2001 18.6 Days CHF 2600 Submit
Sustainability
sustainability 		3.3 6.8 2009 19.7 Days CHF 2400 Submit
AI
ai 		3.1 7.2 2020 18.9 Days CHF 1600 Submit

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Published Papers (2 papers)

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22 pages, 450 KiB  
Article
Energy-Efficient Federated Learning for Internet of Things: Leveraging In-Network Processing and Hierarchical Clustering
by M. Baqer
Future Internet 2025, 17(1), 4; https://doi.org/10.3390/fi17010004 - 26 Dec 2024
Cited by 1 | Viewed by 1229
Abstract
Federated learning (FL) has emerged as a promising solution for the Internet of Things (IoT), facilitating distributed artificial intelligence while ensuring communication efficiency and data privacy. Traditional methods involve transmitting raw sensory data from IoT devices to servers or base-stations for processing, resulting [...] Read more.
Federated learning (FL) has emerged as a promising solution for the Internet of Things (IoT), facilitating distributed artificial intelligence while ensuring communication efficiency and data privacy. Traditional methods involve transmitting raw sensory data from IoT devices to servers or base-stations for processing, resulting in significant communication overhead. This overhead not only increases energy consumption but also diminishes device longevity within IoT networks. By focusing on model updates rather than raw data transmission, FL reduces the volume of data communicated to the base-station; however, FL still faces challenges due to the multiple communication rounds required for convergence. This research introduces an innovative approach that leverages the in-network processing capabilities of IoT devices by integrating a hierarchical clustering routing protocol with FL. This approach enhances energy efficiency through single-round pattern recognition, minimizing the need for multiple communication rounds to achieve convergence. It is envisaged that the proposed approach will prolong the lifespan of IoT devices and maintain high accuracy in event detection, all while ensuring robust data privacy. Full article
(This article belongs to the Topic Artificial Intelligence and Machine Learning in Cyber–Physical Systems)
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23 pages, 3198 KiB  
Article
Quantitative Modeling and Predictive Analysis of Chemical Oxygen Demand in Wastewater Treatment Systems Utilizing Long Short-Term Memory Neural Network
by Xuanzhen Meng and Yan Zhang
Sustainability 2024, 16(23), 10359; https://doi.org/10.3390/su162310359 - 27 Nov 2024
Viewed by 941
Abstract
In the realm of water resource management, meticulous monitoring and control methodologies are quintessential to the refinement of wastewater treatment processes. This research elucidates an avant-garde methodology for forecasting the Chemical Oxygen Demand (COD), an instrumental indicator of water quality, by harnessing the [...] Read more.
In the realm of water resource management, meticulous monitoring and control methodologies are quintessential to the refinement of wastewater treatment processes. This research elucidates an avant-garde methodology for forecasting the Chemical Oxygen Demand (COD), an instrumental indicator of water quality, by harnessing the capabilities of long short-term memory (LSTM) neural networks in conjunction with Internet of Things (IoT) paradigms. The efficacy of the LSTM model is juxtaposed with that of an advanced Deep Belief Network (DBN), as well as contemporary models like a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) hybrid model and a Transformer-based model, employing data sourced from a wastewater treatment facility located in Changsha. The empirical findings show that notwithstanding the comparable training durations used, the LSTM model exhibits a preeminent error rate of merely 7%, thus surpassing the DBN model (which has an error rate of 35%), the CNN-LSTM model (registering a 22% error rate), and the Transformer-based model (with a 17% error rate) in its predictive precision. This research underscores the potential of integrating an astute wastewater control system with IoT and LSTM models, thereby hinting at prospective enhancements in the sustainability and operational efficacy of wastewater treatment installations. Full article
(This article belongs to the Topic Artificial Intelligence and Machine Learning in Cyber–Physical Systems)
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