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作者： Lee, Joash NANYANG TECHNOLOGICAL UNIVERSITY

学位级别：博士

Autonomous vehicles (AVs) produce high data rates of sensory information from sensing systems. In a cooperative driving setting, efficient sharing and processing of this sensory information has the potential to achieve collective sensor fusion, among other advanced functionalities that can enhance safe and efficient mobility. For such sharing to occur, high data-rate communication becomes essential. Unfortunately, the forthcoming proliferation of faster mmWave communication networks has raised concerns on interference with automotive radar sensors, which has led to a body of research on Joint Radar-Communication (JRC). Current strategies for JRC often rely on specialised hardware, prior knowledge of the system model, and entail diminished capability in either radar or communication functions. This thesis focuses on how wireless resources for JRC can be intelligently allocated through coordinated signals transmissions, with the ultimate aim of supporting vehicle safety while respecting limits on the availability of wireless resources. We formulate frameworks on how AVs might intelligently schedule JRC, then identify contemporary techniques in deep reinforcement learning to allow intelligent vehicles to learn, in a distributed manner, how best to conduct JRC. In formulating frameworks for intelligent scheduling of JRC, we consider the ques- tion of how radar sensing and communication can best support road safety. Based on a review of contemporary literature on automotive sensing and a modern un- derstanding of road safety models, we propose a metric for radar performance that incentivises more frequent sensing under risky environmental conditions for which radar excels. We also propose options for communications performance metrics that go beyond the traditional quantities of throughput signal-to-interference-plus- noise ratio (SINR). The first option considers the Age of information (AoI), defined as the duration between data generation and its receipt, in conjunctio

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intelligent Car Parking System Using WSN

Intelligent Car Parking System Using WSN

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Wireless and Optical Technologies (GCWOT), 2020 Global conference on

作者： Steve Azarsh Ratti Nasrullah Pirzada Syed Mohammad Ali Shah Anjum Naveed Department of Telecommunication Engineering Mehran University of Engineering & Technology Jamshro Hyderabad Pakistan Dept. Department of Telecommunication Engineering Mehran University of Engineering & Technology Hyderabad Pakistan Mehran University of Engineering & Technology Hyderabad Pakistan Dept. Of Computer Science & Technology Hazara University Mansehra Mansehra Pakistan

Numerous technologies are constantly being created due to the rapid rise in the standardization and automation of operational and management systems. It simplifies procedures and allows microelectronics technology to advance more often. It was managed by an Android app and controlled by sensor networks and parking systems. The wireless sensor network manages parking spot management, parking lot reservations, vehicle navigation, and other complex circumstances. This study suggests a wireless sensor network-based and android app-based intelligent parking system. sensors installed beneath enable the cutting-edge parking system to function. The research suggests an intelligent parking system that uses wireless sensor networks and IR, magnetic, and light sensors. The ground-level sensors used by the advanced parking system function. The base station receives the node/slot information, and the base station delivers data to FTP. At the user’s request, FTP transmits details about nodes and spaces to mobile devices. The idea came from using an IoT application to control the automobile parking system, which is crucial to our daily lives. For this purpose, a mobile application for Android called Smart Parker was developed. It provides information about a parking lot, such as available or reserved spaces. sensors at the base station will send information about parking spaces to the FTP server. The user of the innovative payment system is required to pay to park the car. Cash is first collected using cash counters, but maintaining them is challenging. Later, different methods are employed to collect the payment. The payment is made by an Automated Vehicle Identification (AVI) tag using RFID technologies. Smart, debit, and credit cards are contact methods, whereas RFID technology and mobile devices are contactless alternatives. The smart parking system on FPGA (Finite State Machine Modeling) works on two main modules, i.e., the identification module and slot checking module. The

关键词： Wireless communication Space vehicles Wireless sensor networks Base stations Aerospace electronics Mobile applications Automobiles

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Implementation of Soldier Tracking and Health Monitoring System 10

Implementation of Soldier Tracking and Health Monitoring Sys...

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10th International conference on Emerging Trends in Engineering and Technology - Signal and information processing, ICETET-SIP-22 2022

作者： Thakre, Laxman Patil, Nayan Kapse, Prashant Potbhare, Piyush G H Raisoni College of Engineering Electronics Department Maharashtra Nagpur India

ISBN: (纸本)9781665467414

In modern times enemy warfare is a major factor in the sovernity of any country. National security lies in the hand of army (on the ground), the ships (at sea), the air force (in the air). An crucial and indispensable role is played by the military. There are many concerns about the safety of the soldiers.. This program will be useful for soldiers, participants in special trips or special missions. This system enables tracking of these soldiers via GPS (Global positioning systems). It could be MHealth. M-health can be explain as mobile computing, medical sensors and healthcare communications technology. In the enemy's territory soldiers are not only exposed to physical threats, but also they feel exhausted and weary caused by prolonged work or poor sleep. Therefore for security reasons we require a tool for distant soldiers to monitor his wellbeing. Therefore in this project the tool is used using biosensors for the purpose of monitoring health. And a Global positioning system is used to track a soldier's location. Adding to it the GSM modem which make the system compatible wireless. In our module we have come about with the design of tracking a soldier and giving him a military position during the war. © 2022 IEEE.

关键词： Wireless sensor networks Military aircraft Modems Safety sensors Security National security

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IoT Based Water Management and Monitoring System

IoT Based Water Management and Monitoring System

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intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Muthulakshimi S Malathy Sathyamoorthy Akash S Charumathi P Sri Suvathi N M Department of Information Technology KPR Institute Of Engineering And Technology Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

IoT (Internet of Things) has revolutionized various industries, and water management is no exception. This abstract introduces an IoT-based water management system designed to address the challenges of water scarcity, pollution, and inefficient distribution. Leveraging IoT devices like sensors, actuators, and communication modules, the system creates a networked infrastructure for real-time monitoring and management of water resources. sensors deployed throughout the water distribution network collect data on flow, pressure, quality, and consumption patterns, which is then transmitted to a central server or cloud platform. The system instantly detects pipe leakage and promptly halts the water flow, even in the absence of human presence. This automated response helps prevent water scarcity. The system supports remote monitoring and control via web-based dashboards or mobile applications, facilitating proactive maintenance and troubleshooting. Additionally, it provides data analytics and reporting functionalities, empowering stakeholders to make informed decisions and promote water conservation initiatives. Overall, this IoT-based water management system offers a comprehensive solution to enhance sustainability, efficiency, and resilience in water distribution networks.

关键词： Water conservation Water pollution sensor systems Real-time systems Maintenance Internet of Things Stakeholders Sustainable development Water resources intelligent sensors

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Portable Field Device for Real-Time Soil Fertility Prediction Using CatBoost

Portable Field Device for Real-Time Soil Fertility Predictio...

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Innovative Computing, intelligent Communication and Smart Electrical Systems (ICSES), International conference on

作者： Karthiyaiyini G Keertha Dharshini S A. Gobinath Poornima K Department of Information Technology Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Accurate prediction of soil fertility is the main factor to improve agriculture production with adapted sustainable farming. Therefore, this paper would present a proposition for developing a Soil Fertility Prediction System using the NPK-pH sensors integrated with Arduino to acquire the data in real time. The system will incorporate the use of the CatBoost model, a gradient boosting algorithm, in predicting soil fertility using sensor data. The Arduino-based sensor setup allows for constant monitoring of soil conditions, providing a very accurate and highly precise measure of NPK levels and pH values. These are very key indicators of soil health that may influence crop yield and quality. Data collected is fed into the CatBoost model that is trained on historical soil and crop yield datasets to understand the relationship between soil properties and fertility. Experimental results reveal quite high performance of the CatBoost model in predicting soil fertility, therefore showing good application of the model. This system will be beneficial in making appropriate decisions for fertilization and good management of the soil by farmers, leading to better yields of crops and usage of resources efficiently. This was a huge step forward in precision agriculture, where sensor technology was integrated with high-machine-learning models, showing the potential of IoT-based sensor networks along with machine learning for real-time assessment of soil fertility and sustainable agricultural practices.

关键词： Accuracy Soil Predictive models sensor systems Real-time systems Data models intelligent sensors Monitoring Crop yield Farming

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Comparison of Decoupling Algorithms for Six-Dimensional Force sensors Based on Least Squares Method and RBF Neural Network

Comparison of Decoupling Algorithms for Six-Dimensional Forc...

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Image processing and Computer Applications (ICIPCA), IEEE International conference on

作者： Xiliang Chen Zihan Ding Yiwen Sun School of Intelligent Manufacturing Taizhou University Taizhou China School of Mechanical Engineering Zhejiang Sci-Tech University Hangzhou China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

In order to solve the problem of dimensional coupling, this paper proposes a decoupling method based on the least squares method and RBF neural network. By comparing the force matrix before and after decoupling, the error matrix E is calculated to obtain the measurement errors of Type I and Type II after decoupling. The RBF neural network decoupling algorithm reduces the Type I error to within 1% and the Type II error to within 2%. Whether it is Type I or Type II error, the decoupling effect of the RBF neural network algorithm is better. By using the decoupling algorithm, the dimensional coupling can be minimized, thereby improving the linearity and measurement accuracy of the six-dimensional force sensor.

关键词： Couplings Measurement errors Force measurement Accuracy Neural networks Force Linearity Prediction algorithms Force sensors Least mean squares methods

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The system of automatic greenhouse care and its informational model 2

The system of automatic greenhouse care and its informationa...

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2nd International conference on Smart Automation and Robotics for Future Industry, SMARTINDUSTRY 2025

作者： Kryvenchuk, Yurii Doroschuk, Ruslan Sundeha, Roksolana Shymanskyi, Volodymyr Department of Systems of Artificial Intelligence Lviv Polytechnic National University Lviv79005 Ukraine Department of Applied Linguistics Lviv Polytechnic National University Lviv79013 Ukraine

The article deals with an automatic greenhouse care system that ensures monitoring and regulation of the microclimate to enhance plant growth efficiency. An informational model of the system is proposed, reflecting the structured representation of data about the greenhouse condition and its control elements. The informational model includes sensor modules for collecting data on temperature, humidity, light levels, CO content, and other parameters, as well as algorithms for information processing to automatically manage ventilation, irrigation, and lighting. The system architecture, its operating principles, and integration possibilities with intelligent platforms for analysis and forecasting are described. The informational model facilitates the visualization and modeling of processes within the greenhouse, enabling prompt responses to changes in conditions and improving resource use efficiency. The research confirms the effectiveness of automation in optimizing resources and increasing crop yields. © 2025 Copyright for this paper by its authors.

关键词： Automation greenhouse informational model microclimate monitoring neural networks sensors

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Research on Smart Home System Based on Internet of Things 1

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3rd International conference on Artificial Intelligence, 2022

作者： Wang, Hai Sun, Peng Sun, Guiling Wang, Zhihong Jiang, Xiaomei Bi, Chaoran Zhang, Ying Teaching Center for Experimental Electronic Information College of Electronic Information and Optical Engineering Nankai University Tianjin300350 China Tianjin Key Laboratory of Optical Thin Film Tianjin Jinhang Technical Physics Institute Tianjin300308 China College of Media Design Tianjin Modern Vocational Technology College Tianjin300350 China Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology Tianjin300071 China

ISBN: (数字)9789811694233

ISBN: (纸本)9789811694226

In the era of Internet of Things (IOT), a more convenient and efficient life concept has created by the smart home system. In this integrated home solution, in order to realize the intelligent control, information technique is frequently utilized to interconnect ordinary household appliances to form an internal family network. With the help of sand table, the real environment is simulated in this design. Different types of sensors are integrated on ZigBee module which controller is CC2531. The intelligent gateway is responsible for analyzing and processing the environmental information obtained by the sensors. The proprietor can control one or more household electrical appliance and set personalized and intelligent home control scenarios according to the actual needs, such as home mode, out mode, sleep mode, etc. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Zigbee

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Influence Maximization With Graph Neural Network in Multi-Feature Social Network 24

Influence Maximization With Graph Neural Network in Multi-Fe...

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24th IEEE International conference on High Performance Computing and Communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wang, Yuhao Li, Peng Huang, Weiyi School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9798350319934

The classical influence maximization (IM) prob-lem aims to select a set of nodes as seed nodes for information propagation. Most related studies focus on one-dimensional information dissemination. However, the diffusion of information should be a multi-dimensional (or multi-feature) diffusion process. The user's judgment of information often depends on the combined judgment of multiple-dimensional information. Based on this, we consider a multi-feature diffusion model. In-formation will be decomposed into multi-dimensional features for diffusion. The user will receive these features and integrate them into a complete message. In addition, we consider seed node selection as a network node classification task. Consid-ering that social networks satisfy a graph structure, we use the node classification technique of graph neural networks. This technique classifies the nodes by the network topology to find the set of influential nodes. We propose a multi-feature influence (MFI) algorithm to improve the performance of the prediction. The set of candidate nodes obtained by classification is used as a benchmark to find the set of nodes with maximum influence. Our experiment results on real datasets show that the proposed algorithm has better algorithmic efficiency than the traditional greedy algorithm and better effectiveness than similar heuristic algorithms. © 2022 IEEE.

关键词： information dissemination

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Diffusion Bayesian Subband Adaptive Filters for Distributed Estimation Over sensor networks

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IEEE TRANSACTIONS ON COMMUNICATIONS 2021年第10期69卷 6909-6925页

作者： Huang, Fuyi Zhang, Jiashu Zhang, Sheng Chen, Hongyang Poor, H. Vincent Guangxi Univ Sch Comp Elect & Informat Nanning 530004 Peoples R China Guangxi Univ Guangxi Key Lab Multimedia Commun & Network Techn Nanning 530004 Peoples R China Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China Res Ctr Intelligent Network Zhejiang Lab Hangzhou 311121 Peoples R China Princeton Univ Dept Elect & Comp Engn Princeton NJ 08544 USA

sensor networks are an indispensable part of the Internet of Things (IoT), where sensors perform data acquisition and information processing tasks to obtain the parameters of interest so that IoT-based monitoring, diagnosis and other systems respond quickly to the changing conditions, instantaneous faults, etc. Distributed estimation algorithms are usually employed to estimate the parameters of interest in these IoT-based applications. However, when sensor networks have highly correlated input signals and nonstationary behavior in which the parameters of interest are time-varying, conventional distributed estimation algorithms suffer from severely degraded learning performance due to the large eigenvalue spread in the covariance matrix of the input signals and the random perturbation of the parameters of interest. To address these problems, this paper proposes two diffusion Bayesian subband adaptive filter (DBSAF) algorithms from a Bayesian learning perspective. As the highly-correlated input signal is whitened in a multiband structure and an estimate of the uncertainty in the parameters of interest is obtained by performing Bayesian inference, the proposed DBSAF algorithms are able to achieve better learning performance in comparison with the competing diffusion algorithms. The transient and steady-state mean square error performance of the proposed DBSAF algorithms are analyzed, and are verified by numerical simulations. A lower bound on the time-varying step-size is derived to maintain the optimal steady-state performance in nonstationary scenarios. A new method for the estimation of the noise variance is also proposed. Numerical simulations demonstrate the excellent learning performance of the proposed algorithms in comparison with benchmark algorithms.

关键词： Estimation Bayes methods Steady-state Signal processing algorithms Inference algorithms Internet of Things Covariance matrices Adaptive signal processing distributed estimation algorithm IoT multiband structure sensor networks

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