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A Review on the need of Clustering Techniques Used for Wireless sensor networks 2

A Review on the need of Clustering Techniques Used for Wirel...

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2nd International conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Bharany, Salil Rehman, Ateeq Ur Tariq Sadiq, M. Farrukh, Muhammad Alharbi, Meshal Hussain, Amjad Issa, Ghassan F. CET and Technology Department Amritsar India Government College University Department of Electrical Engineering Lahore Pakistan University of Brighton School of Architecture & Engineering United Kingdom Minhaj University Department of Computer Science Lahore Pakistan Prince Sattam Bin Abdulaziz University College of Computer Engineering and Sciences Department of Computer Science Alkharj11942 Saudi Arabia School of Computer Science NCBA&E Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan Skyline University College School of Information Technology Sharjah United Arab Emirates

ISBN: (纸本)9798350335644

Wireless sensor networks have been implemented in various software to help gather and analyze data from the physical world. Wireless sensor networks are networks of multiple tiny sensors powered by low-energy batteries (WSNs). WSN lifetime is a critical factor. This is because, after WSNs have been deployed in a given application, the inaccessibility of the sensing nodes makes it impossible to recharge or replace the power source that is limited in energy. Until recently, WSNs were thought to be the same. All the network nodes have the same power, processing speed, and operational capacity. However, scientists have developed heterogeneous WSNs, in which the properties of individual nodes may vary, to extend the lifespan of networks. In order to extend a network's helpful life, energy-efficient protocols must be designed. Even though WSN has its own problems, it has brought a fresh perspective to studying real-time intelligent systems. WSN (Wireless sensor Network) has gained significant popularity in the recent years, owing to its vast applications in real-time scenarios, such as disaster management, pollution monitoring, temperature monitoring, traffic monitoring, transport monitoring, healthcare monitoring, battlefield monitoring, and border security surveillance, to name a few. Numerous sensor nodes are employed in these applications. They are typically placed in the sensing field, where they automatically transfer data to the base station (BS) through energy transmission (i.e., battery in the sensor). In WSN, clustering is one method for optimizing power consumption at the sensor node. The sensor nodes are grouped and given a leader when they undergo clustering. This study overviews various WSN clustering algorithms, each categorized according to various clustering properties. © 2023 IEEE.

关键词： sensor nodes

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TODOS: Thermal sensor Data-driven Occupancy Estimation System for Smart Buildings 23

TODOS: Thermal sensOr Data-driven Occupancy Estimation Syste...

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10th ACM International conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys)

作者： Rajabi, Hamid Ding, Xianzhong Du, Wan Cerpa, Alberto Univ Calif Merced Merced CA 95343 USA

ISBN: (纸本)9798400702303

Occupancy sensing and estimation in large commercial buildings has become a significant problem to be solved, with applications ranging from occupancy-based HVAC control to space planning, and security, etc. Thermal sensing is a promising technology to solve this problem, being easy to deploy in practice and allowing an actual occupancy count in a particular room without violating the data and privacy concerns. While initial strides have been made to solve this problem with thermal arrays, there are many problems that remain unsolved, including accuracy performance, overlapping of sensing areas that lead to under/over-counting, and data training requirements for different zones. In this paper, we introduce TODOS1, a novel system for estimating occupancy in intelligent buildings. TODOS uses a low-cost, low-power thermal sensor array along with a passive infrared sensor. We introduce a novel data processing pipeline that allows us to automatically extract features from the thermal images using an artificial neural network. Through an extensive experimental evaluation(2), we show that TODOS provides occupancy detection accuracy of 98% to 100% under different scenarios. In addition, it solves the issue of occupancy over/under-counting by overlapping sensing areas when using multiple thermal sensors in large rooms. This is done by treating the entire area as a single input thermal image instead of partitioning the area into multiple thermal images individually processed. Furthermore, TODOS introduces a data augmentation technique that allows the generation of training data for rooms of different sizes and shapes, without requiring specific training data from each room. Using these data, TODOS can train specifically designed neural networks optimized for any room size and shape, and achieve almost the same level of occupancy detection accuracy in rooms where experimental labeled training data is available, making it a viable solution that generalizes to the different room

关键词： smart buildings occupancy estimation HVAC systems thermal occupancy sensor neural networks data augmentation

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Estimation of Soft Robotic Bladder Compression for Smart Helmets using IR Range Finding and Hall Effect Magnetic Sensing

Estimation of Soft Robotic Bladder Compression for Smart Hel...

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IEEE/RSJ International conference on intelligent Robots and Systems (IROS)

作者： Pollard, Colin Aston, Jon Minor, Mark A. Univ Utah Salt Lake City UT 84112 USA

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

This research focuses on soft robotic bladders that are used to monitor and control the interaction between a user's head and the shell of a Smart Helmet. Compression of these bladders determines impact dissipation;hence the focus of this paper is sensing and estimation of bladder compression. An IR rangefinder-based solution is evaluated using regression techniques as well as a Neural Network to estimate bladder compression. A Hall-Effect (HE) magnetic sensing system is also examined where HE sensors embedded in the base of the bladder sense the position of a magnet in the top of the bladder. The paper presents the HE sensor array, signal processing of HE voltage data, and then a Neural Network (NN) for predicting bladder compression. Efficacy of different training data sets on NN performance is studied. Different NN configurations are examined to determine a configuration that provides accurate estimates with as few nodes as possible. Different bladder compression profiles are evaluated to characterize IR range finding and HE based techniques in application scenarios.

关键词： Head Magnetic sensors Estimation Training data Artificial neural networks Bladder Soft robotics

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Research on Real-Time Monitoring and Early Warning System of Lightning Protection and Grounding System Based on Computer intelligent sensor

Research on Real-Time Monitoring and Early Warning System of...

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

作者： Jian Liu College of Science Shenyang Jianzhu University Shenyang China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

In the study of real-time monitoring and early warning of the application of intelligent sensors to lightning protection and grounding system, an innovative work is to combine Graph Neural networks (GNN) and Multi-Objective Evolutionary Algorithm based on Decomposition, MOEA/D) combined to build a new hybrid model called Graph Evolutionary networks (GEN) to address the challenges in monitoring complex systems. With its efficient processing of network structure data, GNN can effectively capture and analyze the topological relationship and real-time data flow between lightning protection and grounding system sensors, and achieve accurate evaluation and prediction of system status. In view of the fact that the real-time monitoring and early warning of lightning protection and grounding system involves a number of mutually dependent objectives, such as response speed, monitoring accuracy and energy consumption, the researchers introduced the MOEA/D algorithm to seek the balance point between the objectives by decomposing the multi-objective problem into multiple single-objective sub-problems for synchronous optimization, so as to ensure the overall optimal operation of the system. The GEN model successfully combines the advantages of GNN and MOEA/D: GNN is used to analyze the complexity of sensor networks and obtain critical information in real time; MOEA/D is used to coordinate and optimize multiple performance indicators in system monitoring and early warning. Therefore, the GEN model can not only realize real-time and accurate system condition monitoring, but also make reasonable tradeoff decisions between different performance objectives, which significantly improves the safety and reliability of lightning protection and grounding systems. This interdisciplinary innovation opens up a new technical path for the application of intelligent sensors in the field of complex system monitoring and early warning, which has wide practical value and far-reaching industry devel

关键词： Accuracy Grounding Lightning protection Graph neural networks Real-time systems sensor systems Complex systems Monitoring intelligent sensors Optimization

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Fault detection of highway flow monitoring sensors based on grey correlation degree and neural network

Fault detection of highway flow monitoring sensors based on ...

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intelligent Control, Measurement and Signal processing and intelligent Oil Field (ICMSP), International conference on

作者： Jianfeng Zheng Yi Zhang Shawen Chen Zhejiang Expressway Information Engineering and Technology Co. Ltd. Hangzhou China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Highway traffic monitoring sensors require real-time collection of a large amount of data, including traffic flow, vehicle speed, lane occupancy rate, etc. These data are not only large in volume, but also highly complex and dynamic, and are affected by various factors such as weather, road conditions, and vehicle types, which increase the difficulty of fault detection. This paper proposes a research method for fault detection of highway flow monitoring sensors based on grey relational analysis and neural networks. By using grey relational analysis to evaluate the correlation between highway traffic data and its influencing factors, the close relationship between key influencing factors and traffic has been determined. Furthermore, the BP neural network model is introduced to model and predict the output data of the flow monitoring sensor, and identify whether the sensor has malfunctioned. The experimental results show that the detection method combining grey correlation degree and neural network can effectively improve the accuracy and timeliness of fault detection, providing strong guarantee for the stable operation of highway flow monitoring system.

关键词： Road transportation Fault diagnosis Correlation Accuracy Fault detection Neural networks Predictive models Data models Monitoring intelligent sensors

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Deep Reinforcement Learning Based Secure Communication and Computing Resource Allocation for Grid Cyber-Physical System 1

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2nd International conference on Internet of Things, Communication and intelligent Technology, IoTCIT 2023

作者： Sun, Qiangqiang Lian, Gengxiong Cao, Zhiwei Zeng, Xiangsheng Lv, Zhiyao Liu, Lei Ju, Ying Zheng, Tong-Xing Shenzhen Power Supply Company China Southern Power Grid Shenzhen518028 China School of Information and Communications Engineering Xi’an Jiaotong University Xi’an710049 China School of Telecommunications Engineering Xidian University Xi’an710077 China

ISBN: (数字)9789819727575

ISBN: (纸本)9789819727568

Grid Cyber-Physical System (CPS) improves the intelligence of the grid by combining computing, communication and control technologies, but this new grid CPS system may also have some new security risks, such as new types of attacks on the connection between the physical and information networks. In this paper, we propose a deep reinforcement learning-based joint optimization scheme to improve the security and resource efficiency of multiple grid sensors by exploiting physical layer security (PLS) techniques in a scenario where a malicious eavesdropper can wiretap confidential grid information. We use Wyner’s wiretap coding scheme to prevent confidential information from being decoded and eavesdropped by malicious eavesdroppers. We minimize the system processing latency while securing the wireless communication process by jointly optimizing the transmit power of the grid sensor and the allocation of computing resource blocks. The optimization problem in this paper is formulated as a multi-agent cooperative optimal decision problem and is solved using a double deep Q-network algorithm. Simulation results demonstrate the robustness and effectiveness of the scheme in ensuring information security and reducing delay. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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A Cost-Effective Method to Detect Structural Damage by Employing IOT

A Cost-Effective Method to Detect Structural Damage by Emplo...

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2023 International conference on Recent Advances in Science and Engineering Technology, ICRASET 2023

作者： Nayak, Deekshitha S Pandey, Anubhav Kumar Mangalore Institute of Technology & Engineering Department of Electronics & Communication Engineering Karnataka Moodabidri India Manipal Institute of Technology Manipal Academy of Higher Education Department of Electrical & Electronics Engineering Karnataka Manipal India

ISBN: (纸本)9798350306927

Each structure is unique in its composition, form, and behavior, all of which could change over time due to wear and tear, usage, or environmental factors. Examples of infrastructure that need ongoing monitoring include dams, electrical grids, nuclear power plants, and bridges. The procedure of inspecting any structure to detect the damage and get access to it is called as structural damage detection. Structural health monitoring (SHM) has become increasingly important in the current environment. In order to monitor the structures and conduct critical analysis so that steps can be taken to determine and improve the structures remaining life, remote data collection has become significantly more affordable due to advances in sensors and connection. The Internet of Things (IoT) is a network of intelligent sensors that combines the detection of different structural responses with their transmission to distant computers for ongoing processing and monitoring. It refers to linking any gadget to the Internet and making it interconnected through other connected devices. A vast network of linked individuals and objects known as the Internet of Things (IoT) collects and disseminates information about its users and their surroundings. The use of IoT-based structural damage detection utilizing simulation and hardware testing is the primary focus of this paper. © 2023 IEEE.

关键词： Wireless sensor networks

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Design and Implementation of intelligent Dressing Mirror Based on Raspberry Pi 4B

Design and Implementation of Intelligent Dressing Mirror Bas...

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Digital Analysis and processing, intelligent Computation (DAPIC), International conference on

作者： Chunliu Wang Keling Wu Yan Zhang School of Information Engineering Nanjing Normal University Zhongbei College Danyang Jiangsu Province China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

With the rapid advancement of smart home technologies, personalized home devices have garnered significant attention from users. This paper presents the design and implementation of an intelligent dressing mirror based on Raspberry Pi 4B, aimed at addressing the growing demand for personalized home environments. The system utilizes Raspberry Pi 4B as the central controller and integrates with Alibaba Cloud's MQTT service to store customized clothing items and images, categorized by gender. Real-time environmental data, including weather, time, temperature, humidity, and ultraviolet levels, are acquired via the wireless module integrated into the Raspberry Pi. Additionally, photoelectric sensors and cameras capture relevant user data. By analyzing these datasets, the system recommends suitable clothing combinations and displays personalized clothing suggestions, weather updates, date, and schedule information on the mirror interface. Experimental results demonstrate that the system not only exhibits low power consumption but also effectively integrates personal care with home automation technologies, providing users with a convenient and tailored experience.

关键词： Temperature sensors Human computer interaction Wireless communication Wireless sensor networks Temperature distribution Clothing Cameras Universal Serial Bus Mirrors intelligent sensors

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Design of Campus Security information Identification System Based on ZigBee and CC2530 Module

Design of Campus Security Information Identification System ...

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Control, Electronics and Computer Technology (ICCECT), IEEE International conference on

作者： Zixiao Mo School of Accounting Chongqing Finance and Economic College Chongqing China

ISBN: (数字)9798350380958

ISBN: (纸本)9798350380965

With the development of information technology, more and more big data and Internet of Things related technologies have begun to integrate into the construction of campus information, and the construction of campus security information identification system is developing in the direction of automatic, intelligent, sharing and linkage. In this study, the intelligent campus security information needs are investigated, and the campus security system based on the Internet of Things is designed, and the overall design scheme is proposed. CC2530 chip is selected to be responsible for data processing and communication coordination. The main idea is to sense the environment through various terminal modules, that is, temperature sensors, smoke detection sensors, etc. sensor nodes and coordination nodes independently establish a wireless local area network based on ZigBee protocol, and the sensor module in the sensor nodes distributed on the campus begins to power on and collect environment and security information continuously. The sensing node sends the detected information to the coordination node through multicast mode, and then the coordination node sends the detected information to the back-end control gateway through the serial port. STM32 displays security detection information through the TFT LCD module, which is processed by the system administrator; When the monitoring indicators exceed the normal range, the remote alarm function will be activated, and the back-end control gateway will send alarm information to the Android client.

关键词： Temperature sensors Wireless sensor networks Wireless LAN Zigbee Logic gates sensors Security

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How can Emotions be Classified with ECG sensors, AI techniques and IoT Setup? 2

How can Emotions be Classified with ECG Sensors, AI techniqu...

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2nd International conference on Signal and information processing, IConSIP 2022

作者： Patil, Varsha K. Pawar, Vijaya R AISSMS Institute of Information Technology Department of E&Tc Engineering Pune India Bharati Vidyapeeth's CoE for Women Department of E&Tc Engineering Pune India

ISBN: (纸本)9781728168852

Emotion recognition is a demanding field in the current world. The demands of a dynamically changing world are modifying appliances to incorporate emotion-dependent services. Previously, Intruders at public places, lifts, and entrances could be scanned and selected with the face detection method. However, to incorporate advanced safety, Automatic Emotion Recognition methods are in need for predicting the intentions of intruders. In this connection, we are proposing a system that can automatically identify human emotions with the help of sensors to be used at entrance gates. The system has Real-time hardware sensors which are dealing with various parameters of the 'CASE' Dataset and real-time body parameters like temperature, ECG signals, and heart rate. To make the proposed emotion classification system intelligent, we have used a microcontroller-based system along with Machine learning algorithms like clustering (K means), distance calculation, Linear regression etc. Further proposed system artificial neural networks and Fuzzy classification utilizes to obtain correct results. The proposed system utilizes an ECG sensor, pulse sensor, and temperature sensor. With this article, we are proposing the Internet of Artificial Intelligence Things and a Fuzzy logic-based framework for Emotion Recognition. Our proposed framework is equipped with ECG sensors and temperature sensors. The proposed system has a software requirement of NetBeans IDE. The attractive Graphical interface is created to show the emotion classification of the person who is using sensors of the proposed setup. The novelty of the proposed method is that fuzzy logic, and ANN methods are used with machine learning algorithms, which results in fewer errors in prediction. The measures of errors are given by MSE and RMSE. After emotion classification, analysis and comparison of MSE for No of Actual Emotion Recognized V/s No of Expected Emotion Recognized is performed. A similar emotion recognition setup can be

关键词： Electrocardiograms

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