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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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Large-Scale Traffic Flow Forecast with Lightweight LLM in Edge Intelligence

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IEEE Internet of Things Magazine 2025年第1期8卷 12-18页

作者： Rong, Yi Mao, Yingchi He, Xiaoming Chen, Mingkai College of Computer and Software Hohai University China College of Internet of Things Nanjing University of Posts and Telecommunications China Nanjing University of Posts and Telecommunications Laboratory of Broadband Wireless Communication and Sensor Network Technology China

Large-scale traffic flow forecasting affiliated with the time is valuable for the management in Intelligent Transportation Systems (ITS). Recently, Large Language Models (LLMs) have shown the prominence on this issue. Unfortunately, the existing LLMs cannot forecast the entire road network, including two problems, i.e., (1) training the large-scale data generated by a road network on the central cloud can impose computational pressure. (2) LLMs fail to capture the spatiotemporal correlations in the road network. To overcome the challenges, we propose a novel architecture named Light-weight Spatio-temporal Generative Large Language Model on Edges (LSGLLM-E). Specifically, we first decompose the entire large-scale road network into several subparts, and deploy the Rode-side Unit (RSU) as an edge on each subpart, thus avoiding the computational pressure on the central cloud. In addition, we design an LLM-based method named Light-weight Spatio-temporal Generative Large Language Model (LSGLLM) to extract the spatiotemporal correlations, which compensates for the lack of spatiotemporal features. Finally, the experimental results illustrate that the LSGLLM-E is superior to some advanced baselines in terms of the accuracy and efficiency of the prediction. © 2025 IEEE.

关键词： Roads Cloud computing Correlation Training Predictive models Time series analysis Forecasting Edge computing Large language models Feature extraction

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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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Dimensional-Based Methods for Topological Management in Underwater wireless sensor networks: A Comprehensive Survey

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IEEE ACCESS 2025年 13卷 67511-67530页

作者： Pundir, Meena Sandhu, Jasminder Kaur Juneja, Sapna Gupta, Deepali Altuwaijri, Ghadir Ali Nauman, Ali Chandigarh Univ Dept Comp Sci & Engn Gharuan 140413 India IILM Univ Greater Noida Sch Comp Sci & Engn Greater Noida 201308 India KIET Grp Inst Ghaziabad 201206 India Chitkara Univ Inst Engn & Technol Rajpura 140401 Punjab India Majmaah Univ Coll Comp & Informat Sci Dept Comp Engn Al Majmaah 11952 Saudi Arabia Yeungnam Univ Dept Comp Sci & Engn Gyongsan 38541 South Korea

Most topology control algorithms consider a 2-D plane in terrestrial wireless sensor networks. However, this is not the case in Underwater wireless sensor networks (UWSN). Due to the underwater current, the node's positions constantly flux. It is a significant challenge to satisfy the deployment of nodes precisely underwater for reliable communication. A three-dimensional (3-D) network is created by deploying the sensors in water at different levels of depth. This article presents a comprehensive review of node deployment strategies based on topological management. There are two ways to deploy the nodes in an underwater environment either a dense or sparse network. The last six years of extensive research are summarized in tabular form. There are distinct limitations when nodes are deployed as sparse and dense networks. The fundamental concepts used in sparse and dense network solutions are elaborated for user readability. The topology control solutions are addressed through mathematical analysis in both cases. At last, the authors offer their perspective on the open challenges with some suggestions for future directions.

关键词： Three-dimensional displays sensors Topology network topology Delays Reliability Routing Oceans wireless sensor networks Surveys Dense network sparse network three-dimensional topology topology control underwater wireless sensor networks

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Advancements in Passive wireless Sensing Systems in Monitoring Harsh Environment and Healthcare Applications

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Nano-Micro Letters 2025年第5期17卷 145-192页

作者： Wei Yue Yunjian Guo Jong‐Chul Lee Enkhzaya Ganbold Jia-Kang Wu Yang Li Cong Wang Hyun Soo Kim Young-Kee Shin Jun-Ge Liang Eun-Seong Kim Nam-Young Kim RFIC Bio Centre Kwangwoon UniversitySeoul 01897South Korea Department of Electronic Convergence Engineering Kwangwoon UniversitySeoul 01897South Korea Department of Electronic Engineering Jiangnan UniversityWuxi 214122People’s Republic of China School of Microelectronics Shandong UniversityJinan 250101People’s Republic of China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001People’s Republic of China Department of Electronics Engineering Kwangwoon UniversitySeoul 01897South Korea Laboratory of Molecular Pathology and Cancer Genomics Department of Molecular Medicine and Biopharmaceutical SciencesGraduate School of Convergence Science and TechnologySeoul National UniversitySeoul 08826South Korea

Recent advancements in passive wireless sensor technology have significantly extended the application scope of sensing,particularly in challenging environments for monitoring industry and healthcare *** systems are equipped with battery-free operation,wireless connectivity,and are designed to be both miniaturized and *** features enable the safe,real-time monitoring of industrial environments and support high-precision physiological measurements in confined internal body spaces and on wearable epidermal *** the exploration into diverse application environments,the development of a systematic and comprehensive research framework for system architecture remains elusive,which hampers further optimization of these *** review,therefore,begins with an examination of application scenarios,progresses to evaluate current system architectures,and discusses the function of each component—specifically,the passive sensor module,the wireless communication model,and the readout module—within the context of key implementations in target sensing ***,we present case studies that demonstrate the feasibility of proposed classified components for sensing scenarios,derived from this systematic *** outlining a research trajectory for the application of passive wireless systems in sensing technologies,this paper aims to establish a foundation for more advanced,user-friendly applications.

关键词： wireless sensing Passive detection Harsh environment Biomedical monitoring Flexible sensors

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Low-Power Intelligent wireless sensor network for Precision Agriculture Oriented Agricultural Greenhouse Management System

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IEEE ACCESS 2025年 13卷 34865-34876页

作者： Jin, Hongguo Jilin Int Studies Univ Int Business Sch Changchun 130117 Peoples R China

wireless sensor networks are able to monitor environmental parameters in agricultural greenhouses in real time, thus realizing precise control of the growing environment of crops. However, wireless sensor networks usually need to run for a long time, which requires high energy consumption. To reduce the energy consumption of wireless sensor networks in agricultural greenhouses, this study proposes a research on low-power intelligent wireless sensor networks for agricultural greenhouse management system for precision agriculture. The results showed that when the number of iterations reached 100, the clustering accuracy was as high as 94%, and the mean square error was the lowest 0.018, while when the number of iterations was 20, the mean square error was also maintained at 0.018. This showed that the method used performed well in optimizing quality of service routing in wireless sensor networks. When the number of sensor nodes was 100, the improved whale optimization algorithm accounted for only 15% of the routing energy consumption, while the simulated annealing algorithm accounted for 53% of the routing energy consumption. This further verified the effectiveness of the proposed method in improving the clustering accuracy and reducing the energy consumption of wireless sensor networks, especially in agricultural greenhouse management system. In conclusion, the research method not only effectively improves the clustering accuracy, but also significantly reduces the energy consumption, providing an efficient wireless sensor network optimization scheme for agricultural greenhouse management.

关键词： wireless sensor networks Green products Optimization Monitoring Genetic algorithms Energy consumption Accuracy Routing Mathematical models Encoding Agricultural greenhouse wireless sensor network genetic algorithm whale optimization algorithm network energy consumption

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Malicious node detection in wireless sensor network using modified sandpiper optimization algorithm

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wireless networkS 2025年第2期31卷 1095-1116页

作者： Nirmala, B. Vijaya Selvaraj, K. PSNA Coll Engn & Technol Dept Informat Technol Dindigul India

The most crucial thing about the wireless sensor network (WSN) application is the validation of dangerous as well as remote sensing fields, which are expensive to perform by human insights. Further, these features may lead to the self-managed networking model, in which it faces numerous confronts in the network lifetime, fault tolerance, and energy consumption depending upon the non-renewable energy resources. The major advantages of the WSNs are regarded as the monitoring process as well as the nodes used in this network model are positioned commonly in harsh environments. network management and its efficiency are considered as the most significant factor in network operation. Then, the faults in the WSN have been categorized in terms of persistence, behavior, and underlying causes according to the observation time. Due to its underlying causes in the WSN, the faults are categorized as incorrect computation fault, timing, omission, crash, and fail and stop. Consequently, due to the persistence, the faults are then categorized as a transient fault, intermittent, and permanent, and due to the behaviors, the fault is categorized as a soft and hard fault. As the recent conventional fault detection models failed to provide significant applications in WSN, this work suggests a new way of performing fault tolerance in WSN. In this research, a newly derived technique is implemented by using two functions like energy level checker and a routing manager for fault tolerance to detect malicious nodes in WSN. Here, the Energy level checker checks the residual energy for each communication. If the energy dissipation for a particular communication is less or higher than the threshold it does not send the packet, instead, it forwards the warning messages of the transmitted node that is further sent to the energy level checker. Next, the routing manager sends the path verification packets to the path, if acknowledgment is received, then, the packet is transmitted, and also Certific

关键词： wireless sensor network Malicious node detection Fault tolerant network management Cluster head selection Modified sandpiper optimization algorithm

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Reconfigurable Intelligent Surfaces for Cost-Effective Localization and Secure communication in wireless sensor networks

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INTERNATIONAL JOURNAL OF communication SYSTEMS 2025年第7期38卷

作者： Amer, Asmaa Abd El-Samie, Fathi E. Shokair, Mona Menoufia Univ Fac Elect Engn Dept Elect & Elect Commun Engn Menoufia Egypt Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh Saudi Arabia October 6 Univ Fac Engn Dept Elect Engn October 6 City Egypt

Recently, reconfigurable intelligent surfaces (RIS) have gained significant traction owing to their remarkable flexibility and cost-effectiveness in manipulating the wireless environment. This research paper introduces a novel approach to localization in wireless sensor networks (WSNs) by leveraging RIS. RIS-assisted positioning offers a cost-effective and energy-efficient solution for wireless network positioning, resulting in reduced expenses and power consumption compared to global positioning system (GPS) and range-based methods. Specifically, our approach involves two key steps. First, we formulate a mathematical model for RIS-aided localization in WSNs, enabling us to accurately determine the locations of the sensor nodes. Second, we enhance cluster head (CH) selection in WSNs by incorporating location awareness. The main objective of our proposed routing protocol, efficient residual energy location-aware protocol (ERLAP), is to achieve a balance between intra-cluster and inter-cluster transmission. The simulation results clearly indicate that our suggested algorithm achieves zero localization estimation error in a noise-free environment. Furthermore, even in a noisy environment, the localization estimation error remains minimal. Additionally, the simulation results clearly illustrate the impact of the signal-to-noise ratio (SNR) on the accuracy of node position estimation. As the SNR increases, the algorithm exhibits an enhanced accuracy level in estimating the node positions, even amidst noise interference. Moreover, the proposed ERLAP outperforms other protocols, such as LEACH, RLEACH, and DARE-LEACH, in terms of network lifetime and energy efficiency.

关键词： localization localization estimation error network lifetime reconfigurable intelligent surfaces

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Optimizing Energy Efficient Routing Protocol Performance in Underwater wireless sensor networks With Machine Learning Algorithms

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TRANSACTIONS ON EMERGING TELEcommunicationS TECHNOLOGIES 2025年第3期36卷

作者： Shwetha, M. Sannathammegowda, Krishnaveni Univ Mysore PG Ctr Dept Studies Elect Hemagangotri Hassan India Univ Mysore Dept Studies Phys Mysore India

Underwater wireless sensor networks (UWSNs) and other communication technology improvements have become increasingly important for monitoring marine environments. These networks predict disasters by analyzing soil properties such as moisture and salinity. The restricted capacity of integrated batteries, along with the challenges associated with their replacement or recharging, has rendered energy efficiency a complex issue in the design of UWSNs. This research suggests a machine learning-based routing protocol that combines the energy-efficient Sea Lion Emperor Penguin Routing Protocol (EESLEPRP) with Gaussian Mixture Clustering (GMCML) to address these problems. The EESLEPRP is used to determine the optimal network path. In this case, the residual energy, delay, and distance of each node is evaluated to determine the optimal path. A comparison shows that the suggested approach yields notable gains, such as a minimal packet loss ratio (PLR) of 2.23%, a 97.76% packet delivery ratio (PDR), and a 90.56% throughput. With an end-to-end latency of 1.38 ms, the model optimizes energy consumption at 97.69%. According to the results, the suggested approach can improve UWSN performance and increase network lifetime.

关键词： cluster head clustering data package expectation maximization hybrid model propagation path transmission power

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Smart Gas sensors:Recent Developments and Future Prospective

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Nano-Micro Letters 2025年第3期17卷 55-86页

作者： Boyang Zong Shufang Wu Yuehong Yang Qiuju Li Tian Tao Shun Mao College of Environmental Science and Engineering Biomedical Multidisciplinary Innovation Research InstituteShanghai East HospitalState Key Laboratory of Pollution Control and Resource ReuseTongji University1239 Siping RoadShanghai 200092People’s Republic of China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092People’s Republic of China Microbiome Medicine Center Department of Laboratory MedicineZhujiang HospitalSouthern Medical UniversityGuangzhou 510280People’s Republic of China

Gas sensor is an indispensable part of modern society withwide applications in environmental monitoring,healthcare,food industry,public safety,*** the development of sensor technology,wireless communication,smart monitoring terminal,cloud storage/computing technology,and artificial intelligence,smart gas sensors represent the future of gassensing due to their merits of real-time multifunctional monitoring,earlywarning function,and intelligent and automated *** electronicand optoelectronic gas sensors have been developed for high-performancesmart gas *** the development of smart terminals and the maturityof integrated technology,flexible and wearable gas sensors play an increasingrole in gas *** review highlights recent advances of smart gassensors in diverse *** structural components and fundamentalprinciples of electronic and optoelectronic gas sensors are described,andflexible and wearable gas sensor devices are ***,sensorarray with artificial intelligence algorithms and smart gas sensors in“Internet of Things”paradigm are ***,the challengesand perspectives of smart gas sensors are discussed regarding the future need of gas sensors for smart city and healthy living.

关键词： Smart gas sensor Electronic sensor Optoelectronic sensor Flexible and wearable sensor Artificial intelligence

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