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2025 International conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Li, Daoke Navigation College Fujian Chuanzheng Communication College Fuzhou350007 China

ISBN: (纸本)9798331509828

The sea surface is often accompanied by a complex and fluctuating information environment. How to achieve the efficient and accurate water traffic management and perception is an important research branch of computer-aided maritime informatization. Therefore, this study analyzes the application and performance optimization of intelligent perception systems in water traffic management. First, this study expands the application of sensors in the ship hull by optimizing the perception of objects inside the ship. Then, a perception system with wide coverage and strong data processing capabilities is designed for the deployment of external sensor networks and the perception range of wireless sensor networks. At the same time, in response to the challenges of collecting, transmitting and analyzing complex dynamic data of ships, this paper proposes efficient data cleaning, fast data transmission technology and active routing protocol. In the experimental part, this study also verified the effectiveness of the designed system in ship dynamic trajectory prediction (GPS sensor information analysis) and conflict detection through MATLAB simulation. © 2025 IEEE.

关键词： intelligent perception Maritime traffic sensing Performance optimization Sensing and management

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Jointly Optimizing Age of information and Energy Consumption in Double-IRS-Assisted Wireless networks

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IEEE INTERNET OF THINGS JOURNAL 2025年第8期12卷 10722-10735页

作者： Li, Min Fu, Xuan Dong, Miao Wang, Heng Chongqing Univ Posts & Telecommun Key Lab Ind Internet Things & Networked Control Chongqing 400065 Peoples R China

Age of information (AoI) is a crucial metric for data freshness in 5G ultrareliable low-latency communication systems. It is affected by various factors, such as the channel quality, the energy of the nodes, and so on. To overcome the severe path losses and environmental obstacles, intelligent reconfigurable surface (IRS) is adopted to reconfigure signal propagation environments, thereby mitigating the significant losses and creating additional signal reflection links. Different from the existing research on AoI in IRS-assisted wireless networks, which overlooks the impact of energy consumption on AoI and assumes ideal phase shift models, we investigate the entire process of data generation to arrival and propose a double-IRS cooperative scheme for data transmission with practical phase shift models in blocked wireless networks. The joint optimization problem of the long-term average AoI and node energy consumption is formulated. Given the nonconvexity of the joint optimization problem, we decompose it into an outer data transmission scheduling problem and an inner double-IRS phase shift optimization using a two-layer optimization framework. Then, we use the deep Q-network algorithm to solve the outer problem. For the inner problem, we propose a penalty-based alternating optimization (PB-AO) algorithm to solve the coupling between double-IRS phase shifts and the nonconvex constraints of practical phase shift models. The extensive simulation results verify the effectiveness of the proposed PB-AO algorithm in optimizing double-IRS phase shifts under the practical phase shift model and demonstrate the superiority of our double-IRS cooperative data transmission scheme compared with other benchmark ones.

关键词： Optimization Energy consumption Wireless networks sensors Data communication Heuristic algorithms Array signal processing Data models Job shop scheduling Wireless sensor networks Age of information (AoI) deep Q-network (DQN) intelligent reconfigurable surface (IRS) phase shift model wireless networks

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Feature engineering based intelligent intrusion detection system for IoT network security

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年 1-25页

作者： Sadhwani, Sapna Sriwastawa, Asmi Muthalagu, Raja Pawar, Pranav M. Birla Inst Technol & Sci Pilani Dept Comp Sci Dubai Campus Dubai 345055 U Arab Emirates

Internet-of-Things (IoT) helps create smart systems by allowing physical environments to be controlled or monitored by users through extensive sensor networks. Such networks are heavily resource-constrained due to the low processing power and decentralised placement of sensors. Having been implemented in several backbone sectors such as industry, healthcare, etc., they often deal with sensitive information. Thus, there is an imminent need to secure IoT networks using lightweight intrusion detection systems. A novel feature engineering-based intelligent network intrusion detection framework, dubbed FEIIDS (Feature Engineering based intelligent Intrusion Detection System) entirely based on the Machine Learning concept is proposed. Its working is explained in depth and its time complexity is analyzed. The model is trained and tested over the UNSW-NB15 dataset and found to be competitive with existing works, achieving an accuracy of up to 98.8% and 97.03% for binary and multiclass classification. The proposed work also attains 99% for precision, recall, and F1 score. On the other hand, state-of-the-art work that uses a bidirectional long short-term memory (BiLSTM) model for network intrusion detection is explored over different run-time hardware environments (CPU, GPU, and TPU). Early stopping is implemented to improve model performance and enable better analysis of the results. Discussions on the results reveal that FEIIDS is more stable, transparent, efficient, and lightweight compared to existing state-of-the-art models.

关键词： Feature engineering Feature subsetting Intrusion detection system IoT FEIIDS BiLSTM Linear regression

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Helicopter Turboshaft Engines' Neural Network System for Monitoring sensor Failures

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sensors 2025年第4期25卷 990-990页

作者： Vladov, Serhii Scislo, Lukasz Szczepanik-Scislo, Nina Sachenko, Anatoliy Perzynski, Tomasz Vasylenko, Viktor Vysotska, Victoria Kharkiv Natl Univ Internal Affairs 27 L Landau Ave UA-61080 Kharkiv Ukraine Cracow Univ Technol Fac Elect & Comp Engn 24 Warszawska St PL-31155 Krakow Poland Cracow Univ Technol Fac Environm Engn & Energy 24 Warszawska PL-31155 Krakow Poland European Org Nucl Res CERN 1 Esplanade Particules CH-1211 Geneva 23 Switzerland West Ukrainian Natl Univ Res Inst intelligent Comp Syst 11 Lvivska St UA-46009 Ternopol Ukraine Casimir Pulaski Radom Univ Fac Transport Elect Engn & Comp Sci PL-26600 Radom Poland Lviv Polytech Natl Univ Informat Syst & Networks Dept 12 Bandera St UA-79013 Lvov Ukraine Osnabruck Univ Inst Comp Sci 1 Friedrich Janssen St D-49076 Osnabruck Germany

An effective neural network system for monitoring sensors in helicopter turboshaft engines has been developed based on a hybrid architecture combining LSTM and GRU. This system enables sequential data processing while ensuring high accuracy in anomaly detection. Using recurrent layers (LSTM/GRU) is critical for dependencies among data time series analysis and identification, facilitating key information retention from previous states. Modules such as sensorFailClean and sensorFailNorm implement adaptive discretization and quantisation techniques, enhancing the data input quality and contributing to more accurate predictions. The developed system demonstrated anomaly detection accuracy at 99.327% after 200 training epochs, with a reduction in loss from 2.5 to 0.5%, indicating stability in anomaly processing. A training algorithm incorporating temporal regularization and a combined optimization method (SGD with RMSProp) accelerated neural network convergence, reducing the training time to 4 min and 13 s while achieving an accuracy of 0.993. Comparisons with alternative methods indicate superior performance for the proposed approach across key metrics, including accuracy at 0.993 compared to 0.981 and 0.982. Computational experiments confirmed the presence of the highly correlated sensor and demonstrated the method's effectiveness in fault detection, highlighting the system's capability to minimize omissions.

关键词： sensor failures neural network system helicopter turboshaft engines sensors recurrent layers approximation anomaly detection

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A Novel Optimal Distributed Nonlinear Filter for Simultaneous State and Unknown Input Estimation in Multi-sensor networks

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IEEE Signal processing Letters 2025年 32卷 1745-1749页

作者： Ding, Bo Wei, Yuanchu Zhang, Enze Fang, Huajing Yangzhou University College of Information Engineering Yangzhou225127 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China

This letter investigates the problem of simultaneous state and unknown input estimation in a nonlinear system within multi-sensor networks. To avoid the linearization errors caused by existing methods, such as statistical linear regression techniques and first-order Taylor approximation, a novel optimal distributed nonlinear filter is proposed based on the unscented transformation and unbiased minimum variance criterion. First, the unscented transformation is applied to generate predicted estimates and the covariance matrix. Second, input and state estimation are carried out in an unbiased minimum variance manner using measurements from the nonlinear system. Third, a distributed strategy leveraging average consensus is employed to incorporate local estimates from neighboring sensors. Finally, simulation results confirm that the proposed method achieves substantial improvements in estimation accuracy compared to existing methods. © 1994-2012 IEEE.

关键词： Covariance matrix

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Multi-Agent Cooperation-Based Deep Reinforcement Learning for Multisensor Perception Communication System in HSR Tunnel Scenario

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IEEE TRANSACTIONS ON COMMUNICATIONS 2025年第3期73卷 1816-1832页

作者： Liu, Tanda Hu, Fengye Ling, Zhuang Li, Cheng Liang, Ying-Chang Jilin Univ Coll Commun Engn Changchun 130012 Peoples R China Mem Univ Dept Elect & Comp Engn St John NF A1B 3X5 Canada Mem Univ Dept Comp Sci St John NF A1B 3X5 Canada Univ Elect Sci & Technol China Ctr Intelligent Networking & Commun CINC Chengdu 611731 Peoples R China

The rapid development of High-Speed Railway (HSR) puts higher requirements on comprehensive perception and reliable transmission in tunnel scenarios. To realize efficient and reliable perception information transmission of HSR in the tunnel, we propose a multisensor perception communication system, which consists of an Access Point (AP) deployed on each carriage for perception information transmission and self-powered wireless sensors. The AP remote transmits the perception information through the leaky cable deployed in the tunnel. We construct an optimization problem for minimizing the transmission time of the whole system's perception information in the multi-network system and the adjacent area of the carriage. A Multi-Agent Cooperation-based Deep Reinforcement Learning (MA-CDRL) algorithm is proposed to get the optimal scheduling strategy for reducing the transmission time. We construct the CDRL neural network for the algorithm to introduce the states of other APs, resulting in the system making more efficient transmission strategies. In the simulations, the proposed algorithm gets a better performance than the comparison algorithms and is verified in various dynamic HSR scenarios, such as different travel speeds and sensor distributions.

关键词： sensors Wireless sensor networks Wireless communication Communication systems Optical fiber cables information processing Communication cables High-speed railway (HSR) tunnel scenario multisensor perception communication system multi-agent cooperation-based deep reinforcement learning (MA-CDRL)

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ESMDP: Encrypted ML Framework for Secure Medical Data processing 3rd

ESMDP: Encrypted ML Framework for Secure Medical Data Proces...

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3rd International conference on Network Security and Blockchain Technology

作者： Reddy, B. Pradeep Kumar Chatterjee, Ayantika Indian Instutite Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9789819780501;9789819780518

In this age of Internet of Things (IoT), remote health monitoring and out-of-hospital treatment is not a distant dream. Wearable medical sensors and smartphones in the current IoT-driven environment allow for real-time monitoring of important health markers, with data quickly sent to distant specialists for analysis. Moreover, due to the onset of technological advancements like Electronic Health Records (EHR), IoT-enabled devices, Public Open health Data (POD), Linked Opened Data (LOD), remote health monitoring, vast quantities of information created in this domain as big data in health care. intelligent storage and analysis of this big data may lead to generate critical insights for future predictions. The storage challenge of big data can be mitigated by the use of cloud computing. However, processing of Personal Health information (PHI) on remote medical servers or cloud raises serious concern about the security of critical PHI. One immediate solution is for the information proprietors to transmit and store their delicate information in encrypted form. However, traditional encryption schemes do not support processing on that encrypted form of data. To take full advantage of cloud computing and outof-hospital treatment, there should be a solution to process on that encrypted form of data directly. In this paper, we propose a framework which supports secure data transmission from medical sensors to distant cloud server or remote medical organizations, secure processing on that data to provide distant medical treatments and perform secure machine learning-based analytics on such sensor-generated datasets. For this secure processing, we use Fully Homomorphic Encryption(FHE) scheme, which supports direct processing on encrypted data. Our implementation shows the proposed secure framework is capable of disease detection remotely in an encrypted domain confirming PHI data confidentiality. The framework also supports performing encrypted machine learning algorithm on clo

关键词： IoT Security eHealth Fully homomorphic encryption (FHE)

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Edge Perception: intelligent Wireless Sensing at Network Edge

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IEEE COMMUNICATIONS MAGAZINE 2025年第3期63卷 166-173页

作者： Cui, Yuanhao Cao, Xiaowen Zhu, Guangxu Nie, Jiali Xu, Jie Beijing Univ Posts & Telecommun Beijing Peoples R China Shenzhen Univ Shenzhen Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China Chinese Univ Hong Kong Shenzhen Future Network Intelligence Inst Sch Sci & Engn Shenzhen Peoples R China Chinese Univ Hong Kong Guangdong Prov Key Lab Future Networks Intelligenc Shenzhen Peoples R China

Future sixth-generation (6G) networks are envisioned to support intelligent applications across various vertical scenarios which have stringent requirements on high-precision sensing as well as ultra-low-latency data processing and decision making. Toward this end, a new paradigm of edge perception network emerges. This network integrates wireless sensing, communication, computation, and artificial intelligence (AI) capabilities at network edge for intelligent sensing and data processing. This article provides a timely overview on this emerging topic. We commence by discussing wireless edge perception, including physical layer transceiver design, network-wise cooperation, and application-specific data analytics, for which the prospects and challenges are emphasized. Next, we discuss the interplay between edge AI and wireless sensing in edge perception, and present various key techniques for two paradigms, namely edge AI empowered sensing and task-oriented sensing for edge AI, respectively. Finally, we emphasize interesting research directions on edge perception to motivate future works.

关键词： Wireless communication Wireless sensor networks Translation Text summarization Edge AI Predictive models Transformers Question answering (information retrieval) sensors Artificial intelligence

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Multi-Shadow Scenarios Tennis Ball Detection by an Improved RTMdet-Light Model

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IET IMAGE processing 2025年第1期19卷

作者： Zhu, Yukun Peng, Yanxia Yu, Cong Jianghan Univ Sch Phys Educ Wuhan Peoples R China Jianghan Univ Sch Intelligent Mfg Wuhan Peoples R China

The real-time and rapid recording of sport sensor data related to tennis ball trajectories facilitates the analysis of this information and the development of intelligent training regimes. However, there are three essential challenges in the task of tennis ball recognition using sport vision sensors: the small size of the ball, its high speed, and the complex match scenarios. As a result, this paper considers a lightweight object detection model named improved RTMDet-light to deal with these challenges. Specifically, it has compatible capacities in the backbone and neck, constructed by a basic building block that consists of large-kernel depth-wise convolutions. Furthermore, GhosNet and ShuffleNet are used to replace the CSPLayers which reduce the parameters of our model. The lightweight model proposed addresses the inherent challenges of detecting small objects and muti scenarios in the match. After training, the proposed model performed better on four scenarios with different shades of tennis ball match, with results visualized through heatmaps and performance metrics tabulated for detailed analysis. The recall, FLOPs and number of parameters of the improved RTMDet-light are 71.4%, 12.543G, and 4.874M, respectively. The results demonstrate robustness and effectiveness of our model in accurate tennis ball detecting across various scales. In conclusion, our model for real-time detection in tennis ball detection offers a lightweight and faster solution for sport sensors.

关键词： complex networks image matching image recognition learning (artificial intelligence) sport

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Adaptive Traffic Light Control Through Reinforcement Learning Based on sensor Integration 21st

Adaptive Traffic Light Control Through Reinforcement Learnin...

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21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Uribe-Chavert, Pedro Lopez-Cuerva, Luis Posadas-Yague, Juan-Luis Poza-Lujan, Jose-Luis Univ Politecn Valencia Doctoral Sch Camino VeraSn Valencia 46022 Spain Univ Politecn Valencia Informat Sch Camino VeraSn Valencia 46022 Spain Univ Politecn Valencia Res Inst Ind Comp & Automat Camino VeraSn Valencia 46022 Spain

ISBN: (纸本)9783031820724;9783031820731

This paper explores intelligent traffic light management advancements, focusing on controlling intersection traffic opening times. The decision-making process is influenced by factors such as traffic density. The information for these decisions is gathered from sensors placed on the streets, whose accuracy can vary. Data collected are processed to aid control agents in decision-making. The paper proposes an intersection control algorithm that operates under the assumption of lacking sensorisation. To balance raw sensor data, control nodes implement a reinforced learning algorithm to select the most suitable combination of sensors to improve traffic parameters. The paper also introduces a method for calculating traffic density by combining sensors with imprecise data. This research contributes to intelligent traffic management by providing a novel approach to intersection control and traffic density calculation.

关键词： intelligent Traffic Light Management Reinforced Learning Algorithm Traffic Density Calculation

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