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A real-time Model for Pedestrian Flow Estimation in Urban Areas based on IoT Sensors 25

A Real-Time Model for Pedestrian Flow Estimation in Urban Ar...

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ieee 25th international conference on Intelligent Transportation systems (ITSC)

作者： Khoshkhah, Kaveh Pourmoradnasseri, Mozhgan Hadachi, Amnir Univ Tartu Inst Comp Sci ITS Lab Tartu Estonia

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Understanding foot traffic in real-time at a city scale is significant from different perspectives such as safety, redesigning public spaces, traffic management, hazard management, and integration of autonomous vehicles. Nevertheless, human factors are the most challenging to grasp when addressing urban dynamics, mobility, and transport problems. Moreover, accomplishing this requires extensive data on pedestrians' movement and walkable urban networks. This paper proposes an optimization-based cost-effective methodology for estimating hourly, city-wide pedestrian flow between districts, using limited pedestrian count data. Additionally, employing a network-based approach, the simulation of generated trips obtained from the flow estimation is highly accurate. We applied our method to the city of Tartu, Estonia. The results obtained in the laboratory and real field testing confirmed that our proposed model with a low error rate could be potentially applied in large urban areas with scarce data sources. Moreover, the system can benefit from real-time monitoring applications, as demonstrated in Tartu city's real-time modal split application2.

关键词： Space vehicles Soft sensors Urban areas Estimation Human factors real-time systems Sensor systems

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Multi-Agent systems for Distributed Control in IoT-Enabled Smart Manufacturing 2

Multi-Agent Systems for Distributed Control in IoT-Enabled S...

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2nd ieee international conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Alzubaidi, Laith H. Mekala, Lahari Senthil Kumar, S. Kumar, Rakesh Sainath, C. Gouri Prakalya, S.B. College of Technical Engineering Department of Computers Techniques Engineering The Islamic University Najaf Iraq Bachupally Department of AIML GRIET Telangana Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of EEE Tamil Nadu Chennai600073 India GLA University Department of Computer Engineering & Applications Mathura India CMR College of Engineering & Technology Department of IT India Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (纸本)9798350368109

This research focus on applying Multi-Agent systems (MAS) with Internet of Things (IoT) in order to improve the distributed control for the smart manufacturing. The emphasis of the study is laid on the capability of MAS to enhance production performance and diminish time losses, as well as to develop efficient strategies regarding faults and their reporting. Besides, based on real-time data of manufacturing, an information of schedules, faults and communications of production, the study measures the result of the MAS against the conventional centralized system's efficiency. The outcomes reveal the positive changes in production rate, time loss, and communication performance indicating the suitability of MAS to deal with the challenging and evolving nature of the manufacturing systems. This paper constitutes information to smart manufacturing research and forms a basis for research on the subject in the future. © 2024 ieee.

关键词： Smart manufacturing

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Deep Learning-Based Turkish License Plate Recognition System on Low-Power Microcontroller systems 8

Deep Learning-Based Turkish License Plate Recognition System...

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8th international Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Gorgulu, Emre Ozcan, Ahmet Remzi Bursa Teknik Üniversitesi Mekatronik Mühendislǐgi Bölümü Bursa Turkey

ISBN: (纸本)9798331531492

Automatic License Plate Recognition (ALPR) systems play a crucial role in intelligent transportation systems, traffic management, and security. However, ensuring that these systems operate efficiently on embedded devices with low power consumption presents a significant challenge. In this study, an energy-efficient ALPR system has been designed using the RISC-V-based Kendryte K210 microcontroller. The proposed system adopts a two-stage deep learning-based architecture consisting of YOLOv2 and LPRNet models for license plate detection and recognition. The YOLOv2 model achieves high accuracy in the license plate detection process, while the LPRNet model performs character recognition on the detected license plates. This deep learning-based approach offers an ideal solution for portable and embedded systems with low power consumption. The training of the models leveraged various open-access datasets, aiming to enhance the model's ability to adapt to different conditions. The findings of the study demonstrate that the developed system achieves high accuracy rates in both license plate detection and recognition processes. The performance observed in the license plate detection stage indicates that the system can reliably operate under different conditions. However, the high inference time observed in the license plate recognition process suggests that the model needs to be optimized for real-time applications. In future work, optimization and quantization techniques in deep learning models are planned to enhance the speed and efficiency of the license plate recognition process. Additionally, the applicability of different text recognition models for the license plate recognition problem in embedded systems will be explored. © 2024 ieee.

关键词： Deep learning

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Methodology for Analyzing the Performance of 5G-NR Networks During Initial Planning Without Deployment Using the NS-3 Network Simulator

Methodology for Analyzing the Performance of 5G-NR Networks ...

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2024 international conference on Industrial Engineering, applications and Manufacturing, ICIEAM 2024

作者： Larionov, A.Y. Karanova, E.E. Itmo University Itmo Saint-Petersburg Russia Llc 'STC' Saint-Petersburg Russia

ISBN: (纸本)9798350395013

The primary goal of this research is to improve the performance of fifth-generation (5G) wireless networks. This study will examine existing algorithms for creating and implementing 5G infrastructure, as well as conducting simulation experiments using current network configurations. Through these trials, we will gain insights into the parameters of 5G systems and assess the effectiveness of these networks at the initial stage of planning, before actual deployment. This process will significantly enhance the efficiency of communication network planning, especially in industrial Internet of Things (IoT) applications. Furthermore, by optimizing 5G networks for industrial use cases, this research aims to unlock transformative capabilities, such as real-time monitoring, predictive maintenance, and seamless integration of machinery and devices, thereby fostering Industry 4.0 initiatives and enhancing productivity and competitiveness in industrial settings. © 2024 ieee.

关键词： 5G mobile communication systems

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Lightweight real-time Object Detection System Based on embedded AI Development Kit 2

Lightweight Real-time Object Detection System Based on Embed...

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2nd international conference on Machine Learning and Intelligent systems Engineering, MLISE 2022

作者： Li, Junjie Zhou, Xinsen Wang, Qianqiu Luo, Xianlu School Of Computer Science And Engineering Neusoft Institute Guangdong Foshan China Guangdong Common Technology Co. Ltd Foshan China

ISBN: (数字)9781665492461

ISBN: (纸本)9781665492461

Lightweight object detection focuses on the lightweight and real-time performance of the model, aiming to reduce the size of the model as much as possible without significantly reducing the detection accuracy, so that it can be deployed in practical application scenarios. This system improves the MobileNet-SSD object detection algorithm and uses standard datasets for training and testing. Through channel pruning, the parameters of the model are greatly reduced while the accuracy remains unchanged, and the model compression ratio is 1.17:1, which reduces the model's occupation of the device memory capacity, and obtains about 44% improvement in detection speed. Finally, the trained model is deployed on the embedded artificial intelligence development kit EAIDK-610 to process and detect the collected video content in real time. This system can be extended to practical detection tasks with limited computing resources in various specific occasions. © 2022 ieee.

关键词： Object detection

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Securing Payment Transactions: A Comprehensive Review of Smart Cards and Contactless Payments with Cryptographic Methods 18

Securing Payment Transactions: A Comprehensive Review of Sma...

引用

18th INDIAcom;11th international conference on computing for Sustainable Global Development, INDIACom 2024

作者： Patel, Yatri Chovatia, Niti Kaur, Hargeet Pdeu Computer Science Engineering Gandhinagar India

ISBN: (纸本)9789380544519

In today's fast, advanced and tech-oriented world, human life has been simplified and upgraded at a much higher level which with time will increase with a great spectrum. According to the report, the smart card market is projected to experience a growth rate (CAGR) of 7.33% by 2033. Payment systems have evolved a lot with time and from which we are about to discuss security payment transactions using smart cards. A smart card is a card with a chip comprising CPU, RAM and ROM for storage purposes. This concept of using a plastic card with an embedded chip was first patented in 1968 by two German inventors, Jurgen Dethloff and Helmut Grottrup. Motorola and Bull developed the first smart card microchips in 1977. Due to its ability to provide more secured and authenticated transaction smart cards are fit for use in contactless payments as compared to barcodes or machine-readable stripes. Smart cards find utility in scenarios demanding heightened authentication and security in contrast to alternative machine-readable data stores such as barcodes and magnetic stripes. The first is passwords, confidential and public cryptographic keys for encryption and decryption etc. It performs personal identification by collecting such personal information. This involves biometric information like fingerprint scans, iris scans and images. The second is digital currency. Electronic cash is debited at the time of purchase and the cardholder must pay 'real' money to complete it. Personal information (excluding personal information), such as purchase information at certain stores, medical history and travel history. One of the most crucial factors in the success of smart cards is that they are secure, transferable and can maintain data integrity and then data is stored on the card. In this article we will examine the security of contactless payment systems, considering personal threats and different attacks which the system must protect against. Our review focused on the importance of trus

关键词： applications Contactless contactless payments cryptography Electrical potential EMV ISO multiple targets NFC proximity to the communications area read smart cards review RFID security Smartcards

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Enhancing Photovoltaic Maintenance with real-time Data Integration: A Sensorless and Scalable Approach Validated by Intelligent Correlation Analysis 3

Enhancing Photovoltaic Maintenance with Real-Time Data Integ...

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3rd international conference on embedded systems and Artificial Intelligence, ESAI 2024

作者： Mohamed, Karom Bekkaye, Chaieb El Mounim, Chikri Hajar, Chadli Sara, Chadli Khalid, Salmi Abdechafik, Derkaoui Abdelouahad, Tahani Mohammed First University Electronics and Systems Laboratory Faculty of Sciences Oujda Morocco Mohammed First University Laboratory of Physics Matter and Radiation Faculty of Sciences Oujda Morocco

ISBN: (纸本)9798331522056

This study introduces an innovative and cost-effective approach to gathering, storing and managing photovoltaic and meteorological data, avoiding the use of traditional sensors, wires and cables. Conventionally, systems for data collection often require costly tools as well as sophisticated cabling networks;they revolve around the same methodologies with difficult setups and complicated maintenance needs, which can limit their application, long-Term reliability and scalability in countries with budget constraints and challenging environmental conditions such as Morocco. The presented solution leverages built-in functionalities of photovoltaic inverters to collect crucial data such as real-time generated power, current, voltage and consumed energy. Regarding weather parameters, a set of online open-source techniques has been explored to compile all necessary data required by the researchers, especially those working merely with simulated metrics, to successfully carry out their projects. Machine learning models have been developed to forecast PV power and verify the accuracy of the data, showing a high degree of correlation with inverter-based information. Particularly, decision tree regression and support vector regression were employed. The results on the test sets were as follows: The decision tree regression model achieved a mean squared error of 0.2199 and anR}^{2 score of 0.7769, while the support vector regression model presented a mean squared error of 0.0936 and a higherR}^{2 score of 0.9051. In the end, an online accessible dashboard that showcases important graphs and includes a notification center to alert users as the need arises has been designed, representing a significant step forward in the quest for photovoltaic system maintenance excellence. © 2024 ieee.

关键词： Budget control

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Ciphertext-Policy Attribute-based Encryption for Securing IoT Devices in Fog computing

Ciphertext-Policy Attribute-based Encryption for Securing Io...

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10th international conference on Computer, Information and Telecommunication systems (CITS)

作者： Tu, Shanshan Huang, Fengming Zhang, Shengju Badshah, Akhtar Alasmary, Hisham Waqas, Muhammad Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China China Mobile SuZhou Software Technol Co Ltd Suzhou 215153 Peoples R China GIK Inst Engn Sci & Technol Fac Comp Sci & Engn Topi 23640 Pakistan King Khalid Univ Dept Comp Sci Coll Comp Sci Abha Saudi Arabia King Khalid Univ Informat Secur & Cybersecur Unit Abha Saudi Arabia Univ Bahrain Comp Engn Dept Coll Informat Technol Zallaq 32038 Bahrain Edith Cowan Univ Sch Engn Perth WA 6027 Australia

ISBN: (数字)9781665486156

ISBN: (纸本)9781665486156

Securing Internet of Things (IoT) devices in fog computing systems can be challenging due to the inherent limitations of IoT devices. For instance, cryptographic primitives, such as attribute-based encryption (ABE) schemes, are computationally expensive for deployment on IoT devices. Thus, ABE is not realistic in facilitating real-time updates in various applications of IoT. Therefore, attribute-based and multi-authority encryption schemes are designed that help attribute revocation and computation outsourcing from IoT devices to fog computing servers. The attribute revocation scheme is based on the ciphertext-policy attribute-based encryption (CP-ABE) technique. The CP-ABE scheme allows secret keys between IoT devices and fog nodes to be dynamically generated by incorporating the attribute group keys. Then, the encryption and decryption functions for IoT devices are outsourced to fog nodes, which present the CP-ABE validation.

关键词： real-time systems Encryption Outsourcing Telecommunications Internet of Things Servers Computer security

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IoT applications in Economic Data Collection and Analysis

IoT Applications in Economic Data Collection and Analysis

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Kumar, Brajesh Krupanandhan, H. Gautarm, Sharad Das, Ajanta Nargiza, Shaymatova Rao, D.N. Murali Krishna Dr. Apj Abdul Kalam Women's Institute of Technology Department of Computer Science & Engineering Bihar Darbhanga India School of Commerce Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai600 062 India Graphic Era Hill University Haldwani Campus India Government Model Degree College Baghbar India Termez State University The Department of Accounting and Auditing Termez Uzbekistan Jntuh Hyderabad India

ISBN: (纸本)9798331543617

The process of collecting and interpreting lucrative data has transformed because of the adoption of technology related to the Internet of Things (IoT). This is because real-time data and perceptivity can now be experienced in ways that were before impractical thanks to modern technologies. This endeavor aims to examine the impact that Internet of Things operations have on lucrative data operations. More specifically, there is a lot of interest in how detector-driven data collecting improves the accuracy, timeliness, and granularity of profitable points of information. This inquiry uses case studies and empirical data to show how the Internet of Things (IoT) has revolutionizedseveral industries, including manufacturing, retail, and municipal planning. This research has been used to demonstrate these consequences. This article aims to investigate the role that the Internet of Things plays in enhancing data accuracy, enabling predictive analytics, and streamlining the process of making informed decisions. Furthermore, the research explores issues that have similarities to the previous discussions, including the need for a robust framework, integration obstacles, and data sequestration. The results highlight how the Internet of Things may be used to improve profitable soothsaying, optimize resource allocation, and foster innovation. © 2024 ieee.

关键词： Data accuracy

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ARIES: Accelerating Distributed Training in Chiplet-based systems via Flexible Interconnects 42

ARIES: Accelerating Distributed Training in Chiplet-based Sy...

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42nd ieee/ACM international conference on Computer-Aided Design (ICCAD)

作者： Yin, Lingxiang Ghazizadeh, Amir Louri, Ahmed Zheng, Hao Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA George Washington Univ Dept Elect & Comp Engn Washington DC USA

ISBN: (纸本)9798350322255

Large-scale deep learning models are widely deployed in many application domains with remarkable performance improvements. However, training these models with immense parameters calls for unprecedented computing and communication capabilities. Recently, chiplet-based architectures have shown much promise in scaling Deep Neural Network (DNN) inference, but their applications in the training phase remain unexplored and challenging. In this paper, we posit, beyond scaling computing capability, chiplet-based architectures could also be leveraged to enable new optimization opportunities for existing parallel training algorithms (e.g., Ring and Tree-based all-reduce). Specifically, we aim to explore a variety of topological characteristics, along with the interposer technology, to sustain the performance scaling of parallel training in chiplet-based systems. We propose ARIES, a versatile chiplet-based communication architecture supporting various parallel training algorithms using a flexible interconnect design. The proposed design can adapt to various collective operations such as reduce and gather across a wide diversity of training algorithms. Moreover, such flexibility is also leveraged to further enhance existing all-reduce algorithms depending on the latency and bandwidth requirements of the DNN model and dataset size. Simulation results show that the proposed ARIES can achieve up to 3.92x speedup in execution time and 38.8% reduction in Network-on-Chip (NoC) energy consumption when compared to prior work.

关键词： DNNs parallel training chiplets collective operations

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