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International conference on Communication Technology (ICCT)

作者： Ruihang Yang Dezhi Wang Chen Zhu Boyu Ning Zhengyu Zhu Chongwen Huang Zhaohui Yang College of Information Science and Electronic Engineering Zhejiang University Hangzhou China State Key Laboratory of Integrated Services Networks Xidian University Xi’an China Polytechnic Institute Zhejiang University Hangzhou China National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu China College of Electrical and Information Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

The deployment of various emerging wireless services has necessitated that sixth-generation (6G) wireless networks integrate sensing, computing and communication within the same radio spectrum. However, it is challenging to improve the performance of the integrated sensing, communication, and computation (ISCC) systems in complex propagation environment. In this paper, we investigate the beamforming design problem in reconfigurable intelligent surfaces (RIS)-aided ISCC systems, where an RIS can provide additional spatial degree of freedom. To this end, we first formulate a joint beamforming problem for RIS-aided ISCC, aiming to concurrently improve the performance for multiple target sensing, multi-dimensional computation, and multi-stream communication. Then we present joint optimization of various performances (JOVP) algorithm that minimizes the sum mean square error (MSE) of various demands under power constraints. Next, we analyze the impact of key system parameters on the overall performance of ISCC, providing valuable guidelines for system design. Finally, simulation shows that the sensing error of JOVP algorithm is reduced by at least 46.7% compared with the existing popular algorithms, while the computation rate and communication rate are increased by more than 49.1% and 22.2%, respectively.

关键词： Wireless sensor networks Array signal processing Wireless networks System performance Mean square error methods Reconfigurable intelligent surfaces Communications technology sensors Optimization Guidelines

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Wireless Power Transfer for Future networks: Signal processing, Machine Learning, Computing, and Sensing

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL processing 2021年第5期15卷 1060-1094页

作者： Clerckx, Bruno Huang, Kaibin Varshney, Lav R. Ulukus, Sennur Alouini, Mohamed Imperial Coll London Elect & Elect Engn Depat London SW7 2AZ England Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA King Abdullah Univ Sci & Technol KAUST Comp Elect & Math Sci & Engn CEMSE Div Thuwal 239556900 Saudi Arabia

Wireless power transfer (WPT) is an emerging paradigm that will enable using wireless to its full potential in future networks, not only to convey information but also to deliver energy. Such networks will enable trillions of future low-power devices to sense, compute, connect, and energize anywhere, anytime, and on the move. The design of such future networks brings new challenges and opportunities for signal processing, machine learning, sensing, and computing. The objective is to make the best use of the RF radiations, spectrum, and network infrastructure to provide cost-effective and real-time power supplies to wireless devices and enable wireless-powered applications. In this paper, we first review recent signal processing techniques to make WPT and wireless information and power transfer (WIPT) as efficient as possible. Topics include high-power amplifier and energy harvester nonlinearities, active and passive beamforming, intelligent reflecting surfaces, receive combining with multi-antenna harvester, modulation, coding, waveform, large-scale (massive) multiple-input multiple-output (MIMO), channel acquisition, transmit diversity, multi-user power region characterization, coordinated multipoint, and distributed antenna systems. Then, we overview two different design methodologies: the model and optimize approach relying on analytical system models, modern convex optimization, and communication/information theory, and the learning approach based on data-driven end-to-end learning and physics-based learning. We discuss the pros and cons of each approach, especially when accounting for various nonlinearities in wireless-powered networks, and identify interesting emerging opportunities for the approaches to complement each other. Finally, we identify new emerging wireless technologies where WPT may play a key role-wireless-powered mobile edge computing, wireless-powered sensing, and wireless-powered federated learning-arguing WPT, communication, computation, sens

关键词： Wireless sensor networks Wireless communication sensors Radio frequency System analysis and design Computational modeling Erbium Wireless power transfer wireless powered networks wireless information and power transfer wireless powered communications wireless energy harvesting communications signal processing beamforming intelligent reflecting surface waveform modulation multi-antenna optimization information theory machine learning data-driven end-to-end learning physics-based learning sensing edge computing federated learning

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An Efficient and intelligent IoT-Based Security Model for Enhanced Protection Using Motion Detection and Cloud Storage Optimization

An Efficient and Intelligent IoT-Based Security Model for En...

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Network, Multimedia and information Technology (NMITCON) International conference on

作者： H B Gangadharaswamy Narendra Kumar N Sharmila M Shilpa S Mallikarjunaswamy R Shivaji N M Basavaraju P Balachandra M Komala Dept of Computer Science and Engineering JSS Academy of Technical Eduction Bengaluru India Artificial Intelligence and Machine Learning RNS Institute of Technology Bengaluru India Dept of Electronics and Electrical Engineering JSS Science and Technology University Mysuru India Dept of Information Science and Engineering Bengaluru Institute of Technology Bengaluru India Dept of Electronics and Communication Engineering JSS Academy of Technical Eduction Bengaluru India Dept Electronics and Telecommunication Engineering Jagadambha college of Engineering & Technology India Dept of Mathematics SJB Institute of Technology Bengaluru India Dept of Electronics and Communication Engineering SJB Institute of Technology Bengaluru India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

The proposed IoT-based Smart Security Solution (IoT-SSS) for CCTV and network nodes addresses the challenges of high data volume management, power consumption, and cost inefficiency in traditional security systems. Leveraging Python’s image processing for motion detection via passive infrared (PIR) sensors, IoT-SSS intelligently stamps video recordings and sensor data, activating only when necessary. This method ensures data is initially saved on local storage, significantly reducing the need for continuous recording and, thereby, minimizing power usage and storage demands. When data hits a pre-defined threshold, influenced by location sensitivity, it’s automatically uploaded to the cloud, further optimizing storage needs and eliminating manual intervention. This smart approach not only conserves energy by powering down inactive devices but also slashes maintenance costs. Time, date, and sensor information are embedded directly onto each video frame, facilitating easy review without excessive data overload. Simulation results demonstrate that IoT-SSS outperforms traditional security systems in terms of data storage efficiency and power consumption, offering a cost-effective and energy-efficient alternative for modern security needs.

关键词： Cloud computing Power demand Costs Simulation Memory Infrared image sensors Motion detection Hybrid power systems Security intelligent sensors

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Concurrent Architecture of Novel Scheduler for IoT based sensor networks

Concurrent Architecture of Novel Scheduler for IoT based Sen...

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Sustainable Communication networks and Application (ICSCNA), International conference on

作者： Prachi C. Khanzode Dinesh G. Harkut Sipna COET Amravati India PRMCEAM Badnera Amravati India

A typical sensor network is the area under observations in which is different sensors are deployed and the different parameters are recorded and shared to the sensor node. sensor node is the head among the sensors which are is responsible for proper working of other sensor nodes and collection and process the data samples which is collected by the node. At the head sensor node different data packets may arrive at a time for processing purpose. Since, all of the data packets are carrying significant information, hence it is possible that the head sensor node may lose significant information if the packet remains in queue for longer time or if the packet is lost. To address this issue, this study proposes a novel concurrent architecture of the scheduling algorithm for IOT based sensor networks. The proposed architecture is implemented using latest Virtex-7 series FPGAs and statistically analyzed with respect the different verticals like time, power, resources and frequency parameters.

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intelligent Solid Waste Sorting System using IoT and Image processing

Intelligent Solid Waste Sorting System using IoT and Image p...

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Innovation in Technology (ASIANCON), Asian conference on

作者： Remya Jayachandran Rupesh S Shaeen Kalathil Electronics and Communication Engineering The National Institute of Engineering Mysuru Karnataka India Biofuel Research Information and Demonstration Center (BRIDC) P.E.S College of Engineering Mandya Karnataka India Dept. of Electrical Engineering Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Solid waste management (SWM) is one of the global challenges due to the lack of an efficient automated real-time waste segregation and reuse system. This work deals with the design, development and testing of an intelligent solid waste sorting system by integrating image processing and Internet of things (IoT). The proposed system utilizes image processing algorithm to detect the e-waste as well as e-waste with plastic content from other wastes. Additionally, the developed system is capable of sorting organic wastes, plastic wastes and metal wastes using respective sensors embedded in the system. The speed of the conveyor belt is synergistically tuned with the processing time of the algorithm for waste detection, signal communication, and response time of the flap, thereby attaining effective sorting. The detection accuracy above 95% is obtained for the present system which is better compared to the existing ones.

关键词： Image sensors Accuracy Metals Electronic waste sensor systems Capacitive sensors sensors Time factors Internet of Things Sorting

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ADA-STNET: A DYNAMIC ADABOOST SPATIO-TEMPORAL NETWORK FOR TRAFFIC FLOW PREDICTION 47

ADA-STNET: A DYNAMIC ADABOOST SPATIO-TEMPORAL NETWORK FOR TR...

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47th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Sun, Jiawei Li, Jie Wu, Chentao Tang, Zili Wu, Celimuge Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Univ Electrocommun Grad Sch Informat & Engn Chofu Tokyo Japan

ISBN: (纸本)9781665405409

Traffic flow prediction is of particular interest since its massive applications in intelligent transportation systems (ITS). The problem is challenging due to the complex spatio-temporal correlations and nonlinearities of traffic flows. However, existing methods based on the graph neural networks cannot efficiently extract the dynamic and long-range spatial correlations, thus producing unsatisfactory prediction results. In this paper, we propose an AdaBoost Spatio-temporal Network (Ada-STNet). Similar to AdaBoost, Ada-STNet stacks several base neural networks as "layers" which capture spatial and temporal correlations simultaneously. Each layer learns an adaptive adjacency matrix from weights and embedding of nodes. The adjacency matrix is layer-wise adjusted to extract information from distant neighbors and adapt to dynamic correlations. Experiments are conducted on three real-world benchmark datasets, demonstrating that the Ada-STNet outperforms the state-of-the-art methods.

关键词： Traffic prediction road sensor network graph neural network signal processing over graphs

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Energy-Efficient Clustering Algorithm Analysis for intelligent Marine Ecosystem Monitoring

Energy-Efficient Clustering Algorithm Analysis for Intellige...

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Renewable Energies and Smart Technologies (REST), International conference on

作者： Qamil Kabashi Isak Shabani Almas Abbasi Aaqif Afzaal Abbasi Department of Electronics Faculty of Electrical and Computer Engineering University of Prishtina Prishtina Kosova Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Prishtina Prishtina Kosova Department of Computer Science Faculty of Computing and Information Technology International Islamic University Islamabad Pakistan Department of Earth and Marine Sciences National Biodiversity Future Center University of Palermo Palermo Italy

ISBN: (数字)9798350358902

ISBN: (纸本)9798350358919

Marine ecosystems are complex and dynamic environments. Monitoring these ecosystems is essential for understanding and identifying potential threats and conservation needs. Different sensory devices such as wireless sensor networks (WSNs) are used to monitor these environments. WSN have been in industrial use for the last two decades. However, energy efficiency in WSNs remains a topic that requires attention. With the rise of artificial intelligence and machine learning, numerous techniques have been proposed for developing energy-efficient clustering algorithms. These algorithms aim to improve the efficiency of battery- driven sensors to prolong the network's lifetime by balancing the energy consumption among all nodes in the network. In this paper, we present a comprehensive evaluation and literature review of state-of-the-art energy-efficient clustering algorithms that can be used for intelligent marine ecosystem monitoring. The review aims to dive into the theoretical aspects of clustering in sensor networks and its application to energy optimization, offering insights into the various clustering schemes proposed for wireless sensors, specifically focusing on energy efficiency.

关键词： Wireless communication Wireless sensor networks Renewable energy sources Machine learning algorithms Reviews Clustering algorithms Marine ecosystems

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Enhancing Traffic Flow Forecasting with Spatio-Temporal information: A Case Study on Highway networks in Belgium 8

Enhancing Traffic Flow Forecasting with Spatio-Temporal Info...

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8th International conference on Instrumentation, Control, and Automation, ICA 2023

作者： Fathurrahman, Muhammad Farhan Catteeuw, Arne Gautama, Sidharta Aguirre, Angel L. Joelianto, Endra Sutarto, Herman Y. Ghent University Engineering and Product Design Department of Industrial Systems Ghent Belgium Department of Intelligent System Bandung Indonesia FlandersMake@UGent-Corelab ISyE Lommel Belgium Institut Teknologi Bandung Instrumentation and Control Research Group Faculty of Industrial Technology Bandung Indonesia

ISBN: (纸本)9798350301274

As the number of vehicles continues to increase every year, it is crucial to monitor traffic conditions effectively. Traffic forecasting serves as a valuable method in traffic management to support decision making. This study investigates the impact of utilizing spatio-Temporal data for traffic flow forecasting, as opposed to forecasting each location individually. We compare spatio-Temporal Long Short-Term Memory (LSTM), a machine-learning technique that utilizes spatially correlated sensor data in a network, and compare this to conventional LSTM and Seasonal ARIMA (SARIMA). Experiments have been conducted on counting loop data of a highway network in Belgium. We have investigated the performances of the forecasting model for different prediction horizons. The results show that spatial information improves the performance of the forecasting model and reduces the training time required. Further analysis show that the spatio-Temporal LSTM improves majorities of detectors while the minorities detectors are degraded which imply the importances of network selection. The computational load of the spatio-Temporal LSTM model is minimally impacted by the number of sensors in the network thus making it scalable. The results show that longer prediction horizons degrade the forecasting performance but the impact up to a 12 hour-horizon is not significant. © 2023 IEEE.

关键词： Long short-term memory

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Gas distribution mapping based on integrating system with mobile robot and sensor network 41

Gas distribution mapping based on integrating system with mo...

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第41届中国控制会议

作者： Zhirui Xu Lei Cheng Daiwei Tang Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The monitoring of industrial environmental gas quality becomes an increasingly important topic due to its significant impact on the health of the producers and the safety of the production ***,there are two main approaches to this task:first,using mobile robots equipped with sensors to patrol and monitor the environment,the prediction accuracy of this method is relatively poor;second,using a sensor network distributed in the environment for a long term monitoring,however this method lefts spatial relations and non-detection areas *** solve the above problems,this paper proposes a method combining the spatial map constructed by the mobile robot with fixed monitoring *** this method,the concentration of gas is collected by a fixed gas sensor and predicted by a deep neural *** the same time,a mobile robot equipped with lidar and a gas sensor is used to model the spatial construction of the environment as well as verify the *** embedding the predicted concentration map with the spatial map,a gas concentration map with high accuracy and spatial information is *** using anhydrous ethanol odor sources in a closed and windless room showed high prediction accuracy(98%) with this method.

关键词： Gas distribution mapping Deep neural networks spatial-temporal distribution simultaneous Localization and Mapping(SLAM)

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Real-Time Monitoring of Structural Health with Wireless sensor networks in IoT Infrastructure 3

Real-Time Monitoring of Structural Health with Wireless Sens...

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3rd IEEE International conference on Automation and Computation, AUTOCOM 2025

作者： Bhushan, Bharat Pushparajesh, V. Bethanney Janney, J. Sahoo, Girija Shankar Aggarwal, Prateek Das, Dibyasundar Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Biomedical Engineering Tamil Nadu Chennai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Centre for Cyber Security Odisha Bhubaneswar India

ISBN: (纸本)9798331542375

Wireless sensor networks for structural health monitoring of infrastructure are now becoming an IoT. It provides the ability to collect and analyze survivability data in real-time to enable continuous structural health monitoring for buildings, bridges, and other critical infrastructure. The purpose of this abstract is to introduce Real-time Monitoring with Wireless sensor networks in IoT Infrastructure. Wireless sensor networks are used in structural health monitoring. They are typically composed of many distributed, cheap sensors embedded in the structure that communicate wirelessly to a central monitoring station. Those sensors, including temperature, strain and vibration, are critical in detecting potential structural problems. This data gets transmitted to a cloud for processing, which provides indications of an unusual characteristic or predictive failure. Compared to the traditional methods, there are several advantages of applying WSN for real-time structural health monitoring. Lead leads provide real-time updates on the condition of a bridge that allows for continuous monitoring, with advanced sensors pinpointed through its structure. It will assist in keeping the infrastructure safe and affordable by guaranteeing rapid time-to-resolution. In addition, WSNs have low implementation costs and do not interfere too much with the structure of large structures. © 2025 IEEE.

关键词： Wireless sensor networks

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