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A Novel Machine Learning Strategy for Anomaly Identification Scheme in Wireless sensor Networks Using Supervised Training Principles 5

A Novel Machine Learning Strategy for Anomaly Identification...

引用

5th International conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Shifani, S. Agnes Mary, A. Antony Metilda, M.I. Mary Rajkumar, G. Vinoth Sutha, M.S. Maheshwari, S. Saveetha University Chennai

ISBN: (纸本)9798350379945

Anomaly detection in Wireless sensor Networks (WSN s) is critical for maintaining the integrity and reliability of various monitoring systems, especially in agricultural settings. The core of this study is the development of a hybrid anomaly detection model, SVM-RFNet, which integrates Support Vector Machines (SVM), Random Forests (RF), and Neural Networks (NN). By applying noise reduction techniques, such as smoothing algorithms, the data quality is significantly enhanced, resulting in cleaner and more stable datasets. Feature engineering, including temporal trend analysis and Principal Component Analysis (PCA), further refines the dataset by extracting and selecting the most informative features. This model combines the strengths of each method, leveraging SVM's robustness in high-dimensional data, RF's ensemble learning capabilities, and NN's ability to learn complex patterns. The SVM-RFNet model achieved an impressive accuracy of 96%, demonstrating its efficacy in real-time anomaly detection. The paper concludes with a discussion on future research directions, including the integration of advanced deep learning techniques and the development of self-healing networks, which promise to further enhance the capabilities and applications of WSN s in various domains. © 2024 IEEE.

关键词： Noise abatement

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A Systematic Analysis of Research Trends on Network Control System in Wireless sensor Network 8

A Systematic Analysis of Research Trends on Network Control ...

引用

8th International conference on advanced Computing and Communication systems, ICACCS 2022

作者： Paul, Subhra Prosun Aggarwal, Shruti Chandigarh University Department of Computer Science and Engineering Punjab Gharuan India

ISBN: (纸本)9781665408165

In the present world, wireless sensor network-enabled network control system is performing a significant role in science, technology, and our day-to-day life. The Network control system in wireless sensor network has a great contribution in modern technology-oriented lifestyle and is bringing control to engineers in communicating network system through various applications. Though network control system is facing different challenges like latency, security, and multiple accesses, it is widely accepted for the successful communication and effective data transmission process. In this research paper, we are trying to explain generally the research aspect, trends, and variation of research directions through comparative analysis of existing research work and scientometric analysis on network control systems in the wireless sensor networks. With the help of the Scopus database and Web of Science database separately, some assessments were performed to analyze research data. From different experiments, it is noticed that network control systems can be applied in different fields like environment, agriculture, home applications, traffic control, health, aircraft, and motor control system using a wireless sensor networks. Observing in the last twenty years of research works in this field, we have found that network control system in wireless sensor networks is increasingly become a talented topic in the communication and networking field. © 2022 IEEE.

关键词： Database systems

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A Comprehensive Survey on Traffic Light Detection sensors for Vehicle Safety and Automatic Emergency Braking systems Using Deep Learning Techniques 3rd

A Comprehensive Survey on Traffic Light Detection Sensors fo...

引用

3rd International conference on Intelligent systems and Sustainable Computing, ICISSC 2023

作者： Gowri, B. Shyamala Priya, G. Vishnu Anjana, S. Arjun, V. Kodal, Devendraa Department of Computer Science and Engineering Easwari Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9789819783540

The integration of Traffic Light Detection (TLD) systems with advanced Emergency Braking systems (AEBS) marks a critical milestone in enhancing road safety and paving the way for advanced autonomous driving. This survey paper provides a panoramic and extensive overview of the state-of-the-art TLD solutions leveraging sensors and deep learning techniques. With an increasing emphasis on accident prevention and traffic management, the intersection of TLD and AEBS has become a focal point of research and development. This survey begins by elucidating the fundamental challenges associated with TLD, including varying environmental conditions, occlusions, and complex traffic scenarios. We explore the pivotal role of sensors such as cameras, LiDAR, and radar in providing the requisite data for TLD, and delve into the intricacies of sensor fusion techniques for enhanced perception. Deep Learning has emerged as a cornerstone technology in TLD, enabling robust object detection, classification, and real-time decision-making. We meticulously analyze a spectrum of deep learning architectures including Single-Shot Detectors (SSD), Faster R-CNN, YOLO, and custom-designed networks tailored for TLD applications. Furthermore, the survey examines critical components of the TLD pipeline, encompassing data collection, preprocessing, model training, real-time inference, and integration with AEBS. Emphasis is placed on real-time constraints, multi-modal sensor fusion, and adaptability to diverse traffic light configurations. The paper also delves into the significance of accurate traffic light state prediction, going beyond mere detection to anticipate traffic light changes and optimize vehicle control actions. Human-centric interaction and privacy concerns are addressed, encompassing driver warnings, user interfaces, and data anonymization strategies. Moreover, the survey discusses the importance of safety, validation, and collaboration within the TLD and AEBS ecosystem, emphasizing compl

关键词： Road and street markings

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A review on Machine Learning based IDS approaches in Wireless sensor networks 9

A review on Machine Learning based IDS approaches in Wireles...

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9th International conference on advanced Computing and Communication systems, ICACCS 2023

作者： Madhu Midhan, T. Devika, Kalepalli Rajakumar, R. Manikandan, R. Dinesh, K. Sreedevi, M. Madanapalle Institute of Technology & Science Department of Computer Science and Technology Madanapalle India

ISBN: (纸本)9798350397376

In wireless sensor networks (WSNs), sensors are scattered in a particular region to record and evaluate the physical data from the environment. They are widely utilised in various applications, including healthcare, event detection, agriculture, and disaster management. However, the challenges in WSNs include high bandwidth demand, energy consumption, route design, quality of service (QoS) provisioning, unusual activities, etc. However, numerous ways are being considered to solve the aforementioned difficulties. Detecting abnormal network movements is one of the most difficult challenges. An intrusion detection system (IDS) is a productive way to detect infiltration effectively. However, the growth of contemporary technologies necessitates using an effective IDS strategy. As a result, many academicians focus on reducing and eliminating anomalous network security vulnerabilities. According to the findings, machine learning techniques are more critical in addressing these issues. The accuracy of several machine learning algorithms for detecting intrusion in WSNs is investigated in this work. Furthermore, this paper examines the various datasets, performance measurements, and future directions. © 2023 IEEE.

关键词： Wireless sensor networks

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Trade-off Analysis of Gateway Strategies for Embedding of CAN Frames into Ethernet Packets 15

Trade-off Analysis of Gateway Strategies for Embedding of CA...

引用

15th International conference on Information and Communication Technology Convergence, ICTC 2024

作者： Bae, Sungwon Park, Hanyoung Ko, Kanghyun Choi, Ji-Woong Dgist Department of Electrical Engineering and Computer Science Daegu Korea Republic of Cheonan Korea Republic of

ISBN: (纸本)9798350364637

The advancement of advanced driver assistance systems (ADAS) has increased the demand for more sensor data within vehicles, necessitating faster data transmission. To meet this requirement, in-vehicle networks (IVNs) with higher link speeds are essential, as the data rates of traditional IVN protocols are insufficient for future applications. To address these demands, an IVN architecture that integrates both controller area network (CAN) and automotive Ethernet is being considered. The coexistence of CAN and automotive Ethernet necessitates the use of gateways, which introduces latency when transferring data from CAN to automotive Ethernet. However, many studies have not thoroughly explored the strategies and characteristics of such gateways and their impact on performance. Therefore, in this paper, we analyze the processing latency and network efficiency associated with different gateway strategies for embedding CAN frames into Ethernet packets. The findings of this study show the trade-off between latency and network efficiency and highlight the potential for optimizing CAN-Ethernet gateways. © 2024 IEEE.

关键词： advanced driver assistance systems

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STFO - Power-Saving Deep-Sleep States are Overrated 12

STFO - Power-Saving Deep-Sleep States are Overrated

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12th IEEE International conference on Intelligent Data Acquisition and advanced Computing systems: Technology and applications, IDAACS 2023

作者： Ziesmann, Maurice-Christian Fuhner, Claus Busching, Felix Ostfalia - University of Applied Sciences Wolfenbüttel38302 Germany

ISBN: (纸本)9798350358056

In many Internet-of-Things (IoT) and Wireless sensor Network (WSN) application areas, where the wireless nodes sleep most of the time, ultra-low power micro-controllers (μ Cs) are used, which usually feature several sleep modes. At particularly low duty cycles, the corresponding μ C are put into a very deep sleep mode, from which they wake up again - either independently by a timer or triggered from outside - to then perform their actual tasks. In the application area of addressable Wake-up Radios (WURs), mostly μ Cs are used as address decoder, which have a very low quiescent current consumption. In this paper, we show that for the purpose of address decoding in WURs it is not necessary to rely on μ Cs with particularly low sleep current consumption, because it is much more useful to simply switch off the μ Cs by an upstream circuit and thus save even more energy. Our evaluation results suggest that the lessons learned can also be applied to other application areas. © 2023 IEEE.

关键词： addressing energy efficiency internet of things PIC sleep modes sleep states wake-up calls wake-up radios wireless sensor networks

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A STUDY ON CHIP DELAY MEASUREMENT sensor BASED ON SCAN CHAIN 10

A STUDY ON CHIP DELAY MEASUREMENT SENSOR BASED ON SCAN CHAIN

引用

10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Liu, Peixin Hu, Xiao Yi, Xinjie National University of Defense Technology Changsha China Key Laboratory of Advanced Microprocessor Chips and Systems Changsha China

ISBN: (纸本)9781837242672

The reliability and performance of integrated circuits degrade over time, with one critical factor being the aging of numerous internal combinational logic circuits. Delay variations in these circuits not only reflect the chip's external environmental conditions but also indicate the degree of aging during its operational life. Traditional aging delay measurement sensors typically employ dedicated sampling circuits, which pose challenges for comprehensive detection of delays across all combinational logic paths within the chip. This study introduces a novel chip aging sensor, termed the FATE-sensor, which leverages the internal scan chains of the chip. By performing a full-chip frequency scan of the combinational logic circuits, the FATE-sensor can detect delay variations at various combinational logic locations using the extensive coverage provided by the scan chains. This paper presents a detailed investigation of the FATE-sensor's characteristics, including its consistency, repeatability, temperature and voltage dependencies, and resolution enhancement. Experimental calibration of the FATE-sensor demonstrates its effectiveness in monitoring and reflecting delay variations within the chip's internal circuits. This capability allows for accurate assessment of the chip path delay status, thus enhancing the reliability of chips used in high-precision and advanced applications. This technology not only aids in identifying potential faulty chips and supports chip timing verification testing but also provides new insights for understanding and extending chip lifespan. © The Institution of Engineering & Technology 2024.

关键词： Delay circuits

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Mathematical Model for Determining the Geometric Location of the Environmental Pollutant Based on sensor Data 12

Mathematical Model for Determining the Geometric Location of...

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12th IEEE International conference on Intelligent Data Acquisition and advanced Computing systems: Technology and applications, IDAACS 2023

作者： Khaidurov, Vladyslav Yailymov, Bohdan Shelestov, Andrii National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Kyiv03056 Ukraine Space Research Institute NASU-SSAU Kyiv03187 Ukraine

ISBN: (纸本)9798350358056

The practical result of this work is the development of a generalized mathematical model for determining the position of an environmental pollutant. Computer simulation was carried out in order to verify the developed mathematical model. A number of computational experiments were conducted to verify the mathematical model. In the future, the developed algorithm is proposed to be used for real ground air quality measurement stations with additional use of air quality information from satellite data and specify the location of the pollution source using the Urban Atlas. © 2023 IEEE.

关键词： environmental pollutant genetic algorithm inverse problem mathematical modeling PM2.5 smart city Urban Atlas

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Modelling of advanced sensor based Distributed Control System for Real- Time Prediction and Minimization of Power Loss in Industrial Automation systems 4

Modelling of Advanced Sensor based Distributed Control Syste...

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4th International conference on Expert Clouds and applications, ICOECA 2024

作者： Sengeni, D. Mano Jemila, M.R. Priya, G. Soorya Krishnan, T. Santhana Rathinasabapathi, G. Kannan, Sathish CK College of Engineering and Technology Department of Electronics and Communication Engineering Chellangkuppam Tamil Nadu Cuddalore607003 India Dr. N. G.P. Institute of Technology Department of Biomedical Engineering Tamil Nadu Coimbatore641048 India Loyola Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Mevalurkuppam Chennai600123 India Department of Electrical and Electronics Engineering Tamil Nadu Thandalam Chennai602105 India Panimalar Engineering College Department of Mechanical Engineering Tamil Nadu Chennai600123 India Saveetha School of Engineering Saveetha Institute of Medical and Techinical Sciences Department of VLSI Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350385793

This research study analyses the integration of a Distributed Control System (DCS) to optimise the efficiency and reliability of industrial automation, with a special emphasis on reducing power loss. Leveraging real-time sensor data and machine learning models, including Decision Trees, Artificial Neural Networks, and Random Forests, the research provides a proactive strategy to anticipating and avoiding anomalous current and voltage patterns. The DCS, armed with predictive maintenance procedures, emerges as a solid framework for reducing energy usage, assuring system stability, and limiting possible quality losses. Evaluation using performance ratings and confusion matrices reveals the efficiency of the proposed system in properly recognising and correcting deviations from normal operating circumstances. The results contribute not only to the progress of distributed control systems in industrial automation but also provide a realistic approach to real-time monitoring and predictive maintenance, supporting sustainability and operational excellence in manufacturing settings. This study sets a platform for future initiatives seeking to construct intelligent, adaptable systems for increased industrial automation performance. © 2024 IEEE.

关键词： Predictive maintenance

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Enhancing Indoor Mobile Robot Localization through the Integration of Multi-sensor Fusion Algorithms 1

Enhancing Indoor Mobile Robot Localization through the Integ...

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1st International conference on Robotics, Engineering, Science, and Technology, RESTCON 2024

作者： Yatigul, Rut Pudchuen, Noppadol Blattler, Aran Jitviriya, Wisanu King Mongkut's University of Technology North Bangkok Department of Production and Robotics Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Department of Instrumentation and Electronics Engineering Bangkok Thailand

ISBN: (纸本)9798350348842

This paper presents an innovative approach that combines visual odometry, Inertial Measurement Unit (IMU), and wheel odometry, using the Extended Kalman Filter and Unscented Kalman Filter to enhance mobile robot localization and mapping. The primary goal is to improve localization accuracy and robustness while providing a cost-effective alternative to traditional SLAM methods that rely on expensive LIDAR and RGBD camera systems. By integrating visual odometry, IMU data, and wheel odometry our method not only enhances precision and robustness of the mobile robot localization system but also reduces the financial burden associated with high-end sensor hardware. This fusion creates a reliable solution, particularly suited for resource-constrained environments. Our research contributes to the democratization of SLAM technologies, making them more accessible to a wider range of applications. The results presented in this report showcase the potential of our approach. This paper highlights the pivotal role of multi-sensor fusion framework which significantly enhances localization accuracy and robustness. © 2024 IEEE.

关键词： Extended Kalman filters

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