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Energy-Efficient-Enabled Edge-AI-IoT Integrated Traffic Incident Analysis and Avoidance of Secondary Incidents

IEEE Internet of Things Journal 2025年

作者： Chavhan, Suresh Deepika, Illa Sai Gupta, Deepak Rodrigues, Joel J P C Indian Institute of Science Education and Research Thiruvananthapuram School of Data Sciences India Indian Institute of Management Mumbai India Maharaja Agrasen Institute of Technology Dept. of Computer Science and Engineering Delhi India Chitkara University Chitkara University Institute of Engineering and Technology Punjab India Amazonas State University AM Manaus Brazil

Intelligent transportation systems (ITS) use information communication and technologies to provide road safety, traffic control, traffic congestion, accident avoidance, etc. Traffic accidents cause huge disruption of vehicle movements, road blockages, traffic jams, etc., known as secondary traffic incidents. These incidents in turn lead to huge CO2 emissions and fuel consumption, which directly impact the environment, reduce vehicle mileage, and unnecessary fuel waste. To reduce or avoid secondary traffic incidents, in this paper, we propose an Edge-AI-IoT integrated energy efficient system to detect, analyze, and predict the primary and secondary incidents. The proposed system uses the existing sensor technology like accelerometer, tilt, etc., to detect the accident and severity levels using Edge-AI-IoT, locally it analyzes and predicts the secondary incidents. The proposed system has been exhaustively simulated using real-time scenarios in OMENT++, Veins, and SUMO, and it was tested using the NVIDIA Jetson AGX Xavier edge device integrated with the ThingSpeak cloud platform. The proposed system is tested with performance parameters such as clearance time, accident detection, density of vehicles, speed of vehicles, CO2 emissions, and fuel consumption at different times of day. The simulation and real-time tested results show its real-time deployment. © 2025 IEEE.

关键词： Traffic congestion

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Application of a systematic approach to the analysis of requirements and testing of NPP automated process control systems in the field of information security using specialized software

Application of a systematic approach to the analysis of requ...

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2021 International Conference on Electrotechnical Complexes and Systems, ICOECS 2021

作者： Kemkin, Vladislav Doroshenko, Boris Sakharov, Konstantin Salov, Igor Salova, Julia Chernyaev, Alexey National Research University 'Moscow Power Engineering Institute' Dept. of Automated Control Systems for Thermal Processes Moscow Russia Joint-stock Company 'Rasu' Dept. of Design of I&C Computer Security Systems Moscow Russia National Research University 'Moscow Power Engineering Institute' Joint-stock Company 'Rasu' Dept. of Automated Control Systems for Thermal Processes Moscow Russia

ISBN: (纸本)9781665407274

This article reports examines the prerequisites for development and the expected results of the application of a new systematic approach to testing Industrial control System of Nuclear Power Plants (NPP ICS) in the field of information security using specialized software. The regulatory and technical documentation containing the requirements for testing the NPP ICS in the field of information security is considered, the requirements are analyzed and criteria for systematization of these requirements are formed. The result of the research is the creation of a systematic list of requirements for testing of NPP ICS subsystems in terms of information security, as a basis for the software of formation of programs, methods of checking and testing of NPP ICS subsystems in terms of information security. © 2021 IEEE.

关键词： Application programs

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Securing IoT Networks in Healthcare for Enhanced Privacy in Wearable Patient Monitoring Devices

Securing IoT Networks in Healthcare for Enhanced Privacy in ...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Tiwari, Vibha Jharbade, Nitin Chourasiya, Prashant Supraja, B. Wani, Priyanka Sujit Maurya, Rashmi Madhav Institute of Technology and Science Department of Centre for Artificial intelligence M. P. Gwalior India GGITS Dept of Computer Science & Business System M.P. Jabalpur India IES Institute of Technology and Management IES University Department of Computer Science & Engineering M.P. Bhopal462044 India Guru Nanak Institutions Technical Campus Department of CSE - AIML Hyderabad India Dr. D. Y. Patil Institute of Technology Dept. of E&TC Maharastra Pune India

ISBN: (纸本)9798331539948

The project aims to make smart patient monitoring devices more private. This is crucial to healthcare IoT network security. Strong security is needed to protect patient data in a growing digital healthcare system. Our research develops a whole security system with sophisticated encryption, user-centric identity, blockchain integration, and deep learning to detect unusual activity. As mentioned before, sophisticated patient monitoring gadgets in contemporary healthcare have increased security vulnerabilities. A powerful and adaptable security system is needed to secure patient data;hence, a multidimensional approach is studied. This study's approaches include experimental design, dataset preparation, and measure choice. The security framework uses cutting-edge cryptography, including ECC, homomorphic encryption, and blockchain. Also crucial are recognizing users based on biological features and spotting abnormalities using deep learning and LSTM networks. Mixed methods mitigate all security issues. The findings reveal that the recommended strategy boosts encryption to 256 bits. Since authentication accuracy has reached 98%, user-centered authentication approaches work. Cryptographic processes employ resources effectively, and anomaly detection rates reflect how successfully the approach detects unusual device behavior. Elliptic Curve Cryptography (ECC), which may give 256-bit encryption, is stronger than symmetric encryption (128 bits). User-centric authentication was 98% accurate, outperforming public key infrastructure (95%). With a 92% success rate, LSTM networks can predict abnormal activity in connected devices. The proposed approach performs cryptographic operations fast, with 120 operations per unit time. In conclusion, this research introduces a healthcare IoT security solution. The combined encryption, identity, blockchain, and deep learning technology is secure. The recommended solution protects patient data on personal tracking devices with greater encrypt

关键词： Differential privacy

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Exploring the Capabilities and Limitations of Generative AI Applications, Challenges, and Future Directions

Exploring the Capabilities and Limitations of Generative AI ...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Gulbir Singh Meenakshi Poonam Sharma Anjul Bhardwaj Department of Computer Science and Engineering Graphic Era Hill University Haldwani Campus Nanital Uttarakhand India MMICT&BM Maharishi Markandeshwar (Deemed to be University) Mullana Ambala Haryana India Dept. of Computer Application Divine Institute of Management & Studies Ziradei Siwan Bihar India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Unprecedented capabilities for content generation, predictive analytics, and automation are made available by the introduction of Generative Artificial Intelligence (AI) technologies, which uses in a new age of industrial innovation. The purpose of this study is to investigate the transformational potential of generative AI from the perspective of exploring its capabilities and limitations and highlighting its role in the development of future technological developments. With the help of this study, we want to highlight important unsolved issues in contemporary generative AI paradigms. These challenges need to be addressed in order to further improve the capabilities, variety, and dependability of these concepts. Our goal is to give researchers with significant insights that will allow them to explore successful research areas, which will ultimately lead to the creation of generative AI systems that are more robust and accessible.

关键词： Industries Economics Technological innovation Generative AI Production Regulation Predictive analytics Monitoring Business Testing

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Technological Innovations in IoT-Based Smart Agriculture Systems for Sustainable Crop management

Technological Innovations in IoT-Based Smart Agriculture Sys...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： S. Padmapriya Ashok Todmal Vivek Veeraiah Bhawna Janghel Rajput G. Muthugurunathan Rakshitha Dept. of CSE Dhanalakshmi Srinivasan University Tamil Nadu India MIT College of Management MIT Art Design and Technology University Pune Maharashtra India Dept. of Computer Science Sri Siddhartha Academy of Higher Education Karnataka India Dept. of CSE Rungta College of Engineering and Technology Bhilai Chhattisgarh India Dept. of CSE Madanapalle Institute of Technology & Science Madanapalle AP India Dept. of AIML Nitte (Deemed to be University) NMAMIT Nitte India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Modern farming is now achievable because to the IoT, which has revolutionised agricultural management. investigates the most current IoT based smart agriculture system technologies in view of sustainable crop management. Combining IoT sensors with data analytics found in studies that precision farming, automated irrigation systems, real-time soil monitoring, coupled with resource optimisation may be accomplished. IoT technology might help farmers to better regulate their water, pest management, along with soil health, thus improving their yield with less impact on the environment. Exposing upcoming trends along with research opportunities investigates cost difficulties, system integration, data security. This paper provides understanding of how IoT technology is changing sustainable agriculture by way of a thorough review of present developments and case studies.

关键词： Smart agriculture Technological innovation Costs Data security Crops Food security Soil Internet of Things Monitoring Farming

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Automated Banking Fraud Detection for Identification and Restriction of Unauthorised Access in Financial Sector 3

Automated Banking Fraud Detection for Identification and Res...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Biswas, Ankur Deol, Ramandeep Singh Jha, Barun Kumar Jakka, Geethamanikanta Suguna, M. Raja Thomson, Benjamin Isaac Adamas University Dept. of Comp. Sc. & Engg. Kolkata India Lkctc Dept. Management Jalandhar India University of Delhi Department of Commerce Atma Ram Sanatan Dharma College India University of the Cumberlands Information Technology United States Computer Science and Engineering R.M.K. College Engineering and Technology India BTech Biotechnology Sathyabama Institute of Science and Technology India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Modern techniques have allowed for using advanced technology and tools in order detect fraud within an organisation. It is essential for any Banking sector or financial sector to identify those fraudulent activities that might be associated within their network. Different AI-based technology and ML algorithms are used within banking sectors of India and other countries of this world to determine frauds and unauthorised access. This research article have identified that Data mining is effective for the banking sector to target customers, who might develop fraudulent activities through the credit process. SVM, Logistic regression, Decision tree, Neural networks are used as data mining tools within banking sector's fraud detection process, however all these tools required a data balancing method before structuring the model. Autoencoder is a model that is proposed for fraud detection without any data balancing. A secondary thematic data analysis process is developed within this research article to investigate of different variables relevant to this topic. It has been found that, proposed model for cyber-crime detection, AI-enabled automated fraud controlling system are effective to reduce risk within banking sector. Moreover, SAS is effective software used by banking sector to detect fraud. It generates alert signals for any fraudulent behaviour with system of banks. However, banking sectors need to engage good governance to mitigate challenge regarding lack of skill and expertise within their data mining process. © 2022 IEEE.

关键词： Decision trees

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Industrial IoT Smart Manufacturing Systems based on Cloud Computing Energy Efficiency Analysis using Machine Learning Algorithms

Industrial IoT Smart Manufacturing Systems based on Cloud Co...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： P. Chinnasamy Patan Feridoz Khan C Sasikala Mavalluru Swathi M Prema Kumar Ajay Kumar Yadav Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati Andhra Pradesh India Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Ananthapuramu Andhra Pradesh India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of ECE Shri Vishnu Engineering College for Women (A) Bhimavaram Andhra Pradesh India Department of Computer Application United Institute of Management Prayagraj India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time.

关键词： Cloud computing Machine learning algorithms Automation Accuracy Linear regression Energy efficiency Virtual machines Time factors Industrial Internet of Things Smart manufacturing

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Design of a DC/DC Converter with a PID controller and Backpropagation Neural Network for Electric Vehicles

Design of a DC/DC Converter with a PID Controller and Backpr...

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Learning and Technology Conference (L&T)

作者： Aziza I. Hussein Basmah Shigdar Lina Almatrafi Batool Alaidroos Futoon Alsharif Rabab Hamed M. Aly Dept. of Electrical & Computer Engineering Effat University Jeddah Saudi Arabia Zahid Group OMAS Jeddah Saudi Arabia The Higher Institute for Management and Information Technology Minya Egypt

Currently, global warming has become a major problem. The pollution caused because of conventional internal combustion engines are increasing dramatically. Electric Vehicles are good alternatives to conventional IC engine vehicles in promoting a green environment. controllers, converters, and modulation schemes are needed to provide a safe and reliable power transmission from energy storage systems to the electric motor in electric cars. In this paper, a design of DC/DC boost converter based on a PID controller is proposed. Moreover, a Back Propagation Neural Network (BPNN) technique is applied to generate the optimal PID parameters before using the PID. The proposed DC/DC boost converter is simulated using MATLAB software. The simulation results proved that the proposed DC/DC converter with PID-BPNN achieved higher performance and a stable output voltage.

关键词： Backpropagation Simulation Neural networks Modulation Power transmission Voltage Electric vehicles

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LPV Modeling of the Atmospheric Flight Dynamics of a Generic Parafoil Return Vehicle

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IFAC-PapersOnLine 2022年第35期55卷 37-42页

作者： Matthis H. de Lange Chris Verhoek Valentin Preda Roland Tóth Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven 5600 MB The Netherlands ESTEC European Space Agency Noordwijk 2200 AG The Netherlands Systems and Control Lab Institute for Computer Science and Control Budapest 1111 Hungary

Obtaining models that can be used for flight control is of outmost importance to ensure reliable guidance and navigation of spacecrafts, like a Generic Parafoil Return Vehicle (GPRV). In this paper, we convert an existing, high-fidelity nonlinear model of the atmospheric flight dynamics of a GPRV to a Linear Parameter-Varying (LPV) form that enables high-performance navigation control design. Application of existing systematic conversion methods for such complicated nonlinear models often result in complex LPV representations, which are not suitable for controller synthesis. We apply and compare state-of-the-art conversion techniques on the GPRV model, including learning based approaches, to optimize the complexity and conservatism of the resulting LPV embedding. The results show that we can obtain an LPV embedding that approximates the complex nonlinear dynamics sufficiently well, where the balance between complexity, conservatism and model performance is efficiently chosen.

关键词： linear parameter-varying systems scheduling reduction spacecraft modeling aerodynamics principle component analysis deep neural networks

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Data-Centric Approach for Leather Quality control Using Advanced Vision Transformer Models

Data-Centric Approach for Leather Quality Control Using Adva...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Rezaul Haque Mahbub Khan Md Nuruzzaman Pranto Md Ariful Islam Md Babul Islam Umashankar Rawat Pronaya Bhattacharya Mahesh Jangid Applied Bahari Big Data Research House Dhaka Bangladesh Dept. of Management Information System Pacific State University United States Dept. of Business Administration International American University CA USA Dept. of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Department of CSE Manipal University Jaipur Rajasthan India Department of CSE Amity School of Engineering and Technology Amity University Kolkata West Bengal India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Leather defect classification is crucial for maintaining the quality and value of leather products. Industry statistics show that about 10-15% of leather produced is affected by defects, leading to significant financial losses. Early detection of these defects is essential for reducing waste and ensuring highquality standards in leather manufacturing. However, traditional methods of manual inspection are subjective, inconsistent, and labor-intensive. In this study, we propose a comprehensive approach to leather defect classification using advanced transfer learning models. We collected and annotated a diverse dataset of leather images from four different animals: buffalo, cow, goat, and sheep. We also employed extensive preprocessing techniques, including data augmentation, normalization, and Zero Component Analysis (ZCA) whitening. We implemented and evaluated several state-of-the-art transfer learning models. Our results show that the Convolutional Vision Transformer (CvT) model outperforms all other models. It achieved the highest accuracy of 99.13% and F1-score of 99.04%. Our contributions also include developing a user-friendly mobile application for real-time leather quality assessment, providing a reliable and efficient method for defect detection and quality control.

关键词： Industries computer vision Transfer learning Quality control Transformers Real-time systems Product design Quality assessment Mobile applications Standards

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