Application of IOT on Manufacturing, Communication and Engineering, 2nd Volume

Topic Information

Dear Colleagues,

The 2025 IEEE 7th Eurasia Conference on IoT, Communication and Engineering (ECICE 2025) will be held in Yunlin, Taiwan, from the 14 to 16 November 2025, and it will provide a unified communication platform for researchers working on IoT and advanced manufacturing. The booming economic development in Asia, particularly in leading manufacturing industries including auto-mobile, machinery, computer, communication, consumer product, flat panel displays, semiconductor and micro/nano areas, has attracted increasing attention from universities, research institutions, and many industrial corporations. This conference aims to provide a broad international forum for world researchers, engineers, and professionals working in the areas of IOT and manufacturing to discuss and exchange various scientific, technical, and management aspects across the wide spectrum of the society. The theme of the conference is set as smart manufacturing, focusing on new and emerging technologies. This Topic “Application of IOT on Manufacturing, Communication and Engineering” includes eight journals, Applied Science, Journal of Low Power Electronics and Applications, Journal of Sensor and Actuator Networks, Coatings, Sensors, Future Internet, Vehicles, and Applied Mechanics, who publish excellent papers about related fields. This enables interdisciplinary collaboration of science and engineering technologists in the academic and industrial fields, as well as enabling networking internationally. Papers with innovative ideas or research results in all aspects of advanced manufacturing are encouraged for submission.

Topics of interest include the following:

• Internet and IOT technology;
• Communication science and engineering;
• Computer science and information technology;
• Computational science and engineering;
• Electrical and electronic engineering;
• Mechanical and automation engineering;
• Advanced machining and forming processes;
• Micro- and nano-fabrication;
• Surface manufacturing processes;
• Gear manufacturing;
• Bio-medical manufacturing;
• Precision engineering measurement;
• Robotics and automation;
• Additive manufacturing technology;
• Smart manufacturing technology for Industry 4.0;
• Environmental sustainability.

Prof. Dr. Teen-­Hang Meen
Prof. Dr. Chun-Yen Chang
Prof. Dr. Charles Tijus
Prof. Dr. Cheng-Fu Yang
Dr. Shu-Han Liao
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Mechanics applmech	1.8	3.5	2020	25.6 Days	CHF 1400	Submit
Applied System Innovation asi	3.4	9.0	2018	21.3 Days	CHF 1600	Submit
Journal of Low Power Electronics and Applications jlpea	1.7	4.6	2011	21.8 Days	CHF 1800	Submit
Journal of Sensor and Actuator Networks jsan	4.8	11.3	2012	24.4 Days	CHF 2000	Submit

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Published Papers (7 papers)

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38 pages, 6788 KB  

Open AccessArticle

An Autonomous Guided Vehicle System for Smart Campus with Optimal Path Planning and Voice Interaction Using YOLO Network and LiDAR

by Ching-Ta Lu, Yi-Ping Li, Tsai-Ching Huang, Qiu-Yu Chen, Zong-Wei Huang, Shih-Chang Huang, Tian-Sin Yang, Yen-Yu Lu and Yuan-Yu Tsai

Appl. Syst. Innov. 2026, 9(9), 183; https://doi.org/10.3390/asi9090183 - 31 Aug 2026

Viewed by 359

Abstract

Navigating large, unfamiliar campuses can be challenging for visitors, even with campus maps available. To address this issue, this study proposes an intelligent, autonomous campus navigation system to help users reach their destinations efficiently. The proposed system integrates computer vision, LiDAR, speech recognition, [...] Read more.

Navigating large, unfamiliar campuses can be challenging for visitors, even with campus maps available. To address this issue, this study proposes an intelligent, autonomous campus navigation system to help users reach their destinations efficiently. The proposed system integrates computer vision, LiDAR, speech recognition, global positioning, and path-planning technologies to provide accurate, user-friendly guidance in complex environments. A YOLO-based neural network performs real-time building recognition, while a speech recognition module interprets users’ spoken destination requests and commands. GPS data are mapped to campus map coordinates to improve localization accuracy, and Dijkstra’s algorithm computes optimal navigation paths. All components are integrated into a graphical user interface that provides real-time visual feedback, including recognized building names and current location. Experimental results demonstrate that the proposed system achieves reliable building recognition, accurate speech understanding, and effective route planning, significantly reducing navigation time for users unfamiliar with the campus. The proposed framework not only enhances smart campus navigation but also shows strong potential for extension to other large-scale environments such as hospitals and shopping malls. Full article

(This article belongs to the Topic Application of IOT on Manufacturing, Communication and Engineering, 2nd Volume)

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14 pages, 468 KB  

Open AccessArticle

Longitudinal Behavioural Analysis of Industrial IoT Network Traffic Using Passive Monitoring

by Henrique Santos and Pedro Magalhães

J. Sens. Actuator Netw. 2026, 15(4), 62; https://doi.org/10.3390/jsan15040062 - 2 Aug 2026

Viewed by 387

Abstract

Industrial Internet of Things (IIoT) production environments rely on automated communication between control systems and embedded devices while operating under strict availability constraints that limit the deployment of conventional IT security controls. Despite extensive research on intrusion detection systems, empirical studies based on [...] Read more.

Industrial Internet of Things (IIoT) production environments rely on automated communication between control systems and embedded devices while operating under strict availability constraints that limit the deployment of conventional IT security controls. Despite extensive research on intrusion detection systems, empirical studies based on long-term observations of real industrial networks remain scarce. This paper presents a longitudinal 92-day passive monitoring study of a production-line IIoT network comprising 22 monitored devices. A containerised instance of Zeek was deployed in promiscuous mode to collect flow-level and application-layer telemetry without interfering with operations. The resulting dataset contains more than 41.5 million network flows and 520.5 million packets, represented by 48.48 GB of structured Zeek logs. The results reveal highly deterministic communication patterns dominated by periodic HTTP polling between a central server and distributed devices. In particular, the hourly mean HTTP response size remained highly stable at 132.76 bytes, with a standard deviation of 1.37 bytes and a coefficient of variation of 1.0%. Although no confirmed malicious activity was observed, transient deviations were identified and attributed to planned production stoppages restart periods, which caused temporary traffic reductions and short-lived packet bursts. These findings demonstrate that production-line IIoT networks can exhibit predictable behaviour regimes suitable for statistical anomaly detection. The study contributes a longitudinal empirical characterisation of a real operational IIoT network, a reproducible methodology for behavioural baseline extraction using passive telemetry, and practical insights for safe monitoring deployment. Full article

(This article belongs to the Topic Application of IOT on Manufacturing, Communication and Engineering, 2nd Volume)

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21 pages, 765 KB  

Open AccessArticle

An Algorithm for Structural Optimization of Intelligent Radio Communication System

by Serhii Dupelych, Igor Korobiichuk, Volodymyr Dziubenko, Viktor Bovsunovskyi and Oleksandr Shkatula

Appl. Syst. Innov. 2026, 9(7), 155; https://doi.org/10.3390/asi9070155 - 21 Jul 2026

Viewed by 392

Abstract

This article develops a structural optimization algorithm for designing intelligent and adaptive radio communication systems. In modern tactical crisis operations characterized by high environmental dynamics and intense electromagnetic countermeasures, as well as in emergency response networks, static communication planning approaches prove increasingly ineffective. [...] Read more.

This article develops a structural optimization algorithm for designing intelligent and adaptive radio communication systems. In modern tactical crisis operations characterized by high environmental dynamics and intense electromagnetic countermeasures, as well as in emergency response networks, static communication planning approaches prove increasingly ineffective. The paper introduces a stable normalization method for performance estimates and a mechanism for rapid algorithmic adaptation to shifting operational priorities. A key feature of the proposed approach is an adaptive objective function based on the weighted sum of normalized criteria for survivability, noise immunity, and reliability. The use of weighting coefficients enables rapid adjustment of optimization priorities in response to extreme conditions, whilst the application of a normalization method based on threshold values ensures the stability and comparability of evaluations. It is practical that the proposed algorithm will enable the synthesis of functionally stable and robust structures, thereby enhancing the reliability of command and control of forces and assets in both mission-critical operations and civil crisis situations. Full article

(This article belongs to the Topic Application of IOT on Manufacturing, Communication and Engineering, 2nd Volume)

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17 pages, 4778 KB  

Open AccessArticle

A Low-Power LoRa-Based Multi-Nodal Wireless Sensor Network with Custom Communication Framework for Rockfall Monitoring

by Paolo Esposito, Vincenzo Stornelli and Giuseppe Ferri

J. Low Power Electron. Appl. 2026, 16(1), 7; https://doi.org/10.3390/jlpea16010007 - 17 Feb 2026

Cited by 1 | Viewed by 2091

Abstract

In this work, the authors introduce an entirely solar-powered LoRa-based WSN consisting of several nodes, two stoplights, and four cameras. The system has been used to monitor the semi-rural area of Panni (FG), Puglia, Italy. The WSN has a totally custom implementation in [...] Read more.

In this work, the authors introduce an entirely solar-powered LoRa-based WSN consisting of several nodes, two stoplights, and four cameras. The system has been used to monitor the semi-rural area of Panni (FG), Puglia, Italy. The WSN has a totally custom implementation in both the node-gateway side and the gateway-user interface side. In particular, the communication framework is entirely IoT-based, featuring both the MQTT protocol, for the direct control of apparatuses from the system user interface, and the more traditional TCP/IP protocol, implemented on NB-IoT. The proposed system is entirely solar-powered and features a 34.68 mWh/day consumption. Around a single communication session, the average power consumption inside the single node amounts to 1.4 mW. This paper gives an overview of the proposed system, with detailed explanations of each part, and measurements retrieved over a wide period to assess the functionality of the system. Full article

(This article belongs to the Topic Application of IOT on Manufacturing, Communication and Engineering, 2nd Volume)

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23 pages, 2994 KB  

Open AccessArticle

Semantic Segmentation-Based and Task-Aware Elastic Compression of Sequential Data for Aluminum Heating Furnaces

by Jie Hou, Xiaoxuan Huang, Jianping Tan, Jianqiao Liu, Xiaojie Jia and Ruining Xie

Appl. Syst. Innov. 2026, 9(1), 25; https://doi.org/10.3390/asi9010025 - 22 Jan 2026

Viewed by 725

Abstract

To address the challenges of compressing large-scale, multi-channel temperature data from aluminum alloy heating furnaces—and the limitations of traditional methods in preserving fidelity for critical tasks like energy accounting and process playback—this paper proposes an elastic, task-aware time-series compression method based on semantic [...] Read more.

To address the challenges of compressing large-scale, multi-channel temperature data from aluminum alloy heating furnaces—and the limitations of traditional methods in preserving fidelity for critical tasks like energy accounting and process playback—this paper proposes an elastic, task-aware time-series compression method based on semantic segmentation. The method automatically segments data and annotates anchor points according to key process stages and significant operational events. Data are grouped by furnace number and alloy grade into segment-level buckets. Within this structure, an enhanced PCA model is built using channel-specific weights and amplified anchor points. The optimal principal component dimension is selected automatically under explained variance constraints, with channel-wise DCT used as a fallback for small samples. Compression accuracy is evaluated using combined rRMSE metrics (overall and per temperature channel) and key event recall rate. Experiments show the method achieves an average overall rRMSE of 0.11624, a temperature channel rRMSE of 0.08860, and a compression ratio of 1.18, outperforming Standard-PCA, PAA, and RP-Gauss. Notably, the proposed method achieves 100% recall for key events during heat preservation, demonstrating superior performance. Further analysis shows performance varies significantly across process stages, furnace IDs, and alloy grades, offering valuable insights for fine-grained evaluation and real-world deployment. Full article

(This article belongs to the Topic Application of IOT on Manufacturing, Communication and Engineering, 2nd Volume)

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26 pages, 1489 KB  

Open AccessArticle

Proactive Cooling Control Algorithm for Data Centers Based on LSTM-Driven Predictive Thermal Analysis

by Jieying Liu, Rui Fan, Zonglin Li, Napat Harnpornchai and Jianlei Qian

Appl. Syst. Innov. 2026, 9(1), 21; https://doi.org/10.3390/asi9010021 - 12 Jan 2026

Cited by 1 | Viewed by 2819

Abstract

The conventional reactive cooling strategy, which relies on static thresholds, has become inadequate for managing dynamically changing heat loads, often resulting in energy inefficiency and increased risk of local hot spots. In this study, we develop a data center cooling optimization system that [...] Read more.

The conventional reactive cooling strategy, which relies on static thresholds, has become inadequate for managing dynamically changing heat loads, often resulting in energy inefficiency and increased risk of local hot spots. In this study, we develop a data center cooling optimization system that integrates distributed sensor arrays for predictive analysis. By deploying high-density temperature and humidity sensors both inside and outside server racks, a real-time, high-fidelity three-dimensional digital twin of the data center’s thermal environment is constructed. Time-series analysis combined with Long Short-Term Memory algorithms is employed to forecast temperature and humidity based on the extensive environmental data collected, achieving high predictive accuracy with a root mean square error of 0.25 and an R² value of 0.985. Building on these predictions, a proactive cooling control strategy is formulated to dynamically adjust fan speeds and the opening degree of chilled-water valves in computer room air conditioning units, changing the cooling approach from passive to preemptive prevention of overheating. Compared with conventional proportional–integral–differential control, the developed system significantly reduces overall energy consumption and maintains all equipment within safe operating temperatures. Specifically, the framework has reduced the energy consumption of the cooling system by 37.5%, lowered the overall power usage effectiveness of the data center by 12% (1.48 to 1.30), and suppressed the cumulative hotspot duration (temperature 27 °C) by nearly 96% (from 48 to 2 h). Full article

(This article belongs to the Topic Application of IOT on Manufacturing, Communication and Engineering, 2nd Volume)

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21 pages, 2255 KB  

Open AccessArticle

An Intelligent Support Method for the Formation of Control Actions in Proactive Management of Complex Systems

by Vladimir Artyushin, Kirill Dereguzov, Maxim Shcherbakov, Konstantin Zadiran and Alla Kravets

Appl. Syst. Innov. 2026, 9(1), 5; https://doi.org/10.3390/asi9010005 - 25 Dec 2025

Cited by 1 | Viewed by 1119

Abstract

This paper addresses the problem of ensuring the continuous operation of cyber–physical systems (CPS) under conditions of component degradation and wear. To achieve this goal, a transition to the concept of Proactive Prognostics and Health Management (PPHM) is proposed, focused on proactive control [...] Read more.

This paper addresses the problem of ensuring the continuous operation of cyber–physical systems (CPS) under conditions of component degradation and wear. To achieve this goal, a transition to the concept of Proactive Prognostics and Health Management (PPHM) is proposed, focused on proactive control of the technical condition of equipment. A key stage of PPHM is the generation of control actions aimed at extending the remaining useful life by adapting the operational parameters of the system. This paper proposes an intelligent support method for generating control actions to optimize the operational conditions. The proposed method integrates an RUL prediction model with optimization procedures based on genetic algorithm. The method was experimentally validated using XJTU-SY Bearing test rig and a bearing-degradation dataset. The obtained results demonstrate its effectiveness and confirm its applicability for extending the service life of technical systems. The proposed method is general and can be adapted to any CPS where controllable parameters affect the degradation rate Full article

(This article belongs to the Topic Application of IOT on Manufacturing, Communication and Engineering, 2nd Volume)

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