Artificial Intelligence and Machine Learning in Cyber–Physical Systems

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

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit
Eng eng	2.4	3.2	2020	19.7 Days	CHF 1400	Submit
Future Internet futureinternet	3.6	8.3	2009	17 Days	CHF 1600	Submit
Information information	2.9	6.5	2010	18.6 Days	CHF 1800	Submit
Sensors sensors	3.5	8.2	2001	19.7 Days	CHF 2600	Submit
Sustainability sustainability	3.3	7.7	2009	19.3 Days	CHF 2400	Submit
AI ai	5.0	6.9	2020	20.7 Days	CHF 1600	Submit

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Published Papers (3 papers)

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All Electronics Eng Future Internet Information Sensors Sustainability AI

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17 pages, 1488 KB  

Open AccessArticle

PG-Mamba: An Enhanced Graph Framework for Mamba-Based Time Series Clustering

by Yao Sun, Dongshi Zuo and Jing Gao

Sensors 2025, 25(16), 5043; https://doi.org/10.3390/s25165043 - 14 Aug 2025

Viewed by 312

Abstract

Time series clustering finds wide application but is often limited by data quality and the inherent limitations of existing methods. Compared to high-dimensional structured data like images, the low-dimensional features of time series contain less information, and endogenous noise can easily obscure important [...] Read more.

Time series clustering finds wide application but is often limited by data quality and the inherent limitations of existing methods. Compared to high-dimensional structured data like images, the low-dimensional features of time series contain less information, and endogenous noise can easily obscure important patterns. When dealing with massive time series data, existing clustering methods often focus on mining associations between sequences. However, ideal clustering results are difficult to achieve by relying solely on pairwise association analysis in the presence of noise and information scarcity. To address these issues, we propose a framework called Patch Graph Mamba (PG-Mamba). For the first time, the spatio-temporal patterns of a single sequence are explored by dividing the time series into multiple patches and constructing a spatio-temporal graph (STG). In this graph, these patches serve as nodes, connected by both spatial and temporal edges. By leveraging Mamba-driven long-range dependency learning and a decoupled spatio-temporal graph attention mechanism, our framework simultaneously captures temporal dynamics and spatial relationships and, thus, enabling the effective extraction of key information from time series. Furthermore, a spatio-temporal adjacency matrix reconstruction loss is introduced to mitigate feature space perturbations induced by the clustering loss. Experimental results demonstrate that PG-Mamba outperforms state-of-the-art methods, offering new insights into time series clustering tasks. Across the 33 datasets of the UCR time series archive, PG-Mamba achieved the highest average rank of 3.606 and secured the most first-place rankings (13). Full article

(This article belongs to the Topic Artificial Intelligence and Machine Learning in Cyber–Physical Systems)

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22 pages, 450 KB  

Open AccessArticle

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 2 | Viewed by 2577

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 KB  

Open AccessArticle

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

Cited by 2 | Viewed by 1228

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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