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作者： Alonso, Carmen Amo Li, Jing Shuang Anderson, James Matni, Nikolai California Institute of Technology Department of Computing and Mathematical Sciences PasadenaCA91106 United States Columbia University Department of Electrical Engineering and the Data Science Institute New YorkNY10027 United States University of Pennsylvania Department of Electrical and Systems Engineering PhiladelphiaPA19104 United States

The increasing presence of large-scale distributed systems highlights the need for scalable control strategies where only local communication is required. Moreover, in safety-critical systems, it is imperative that such control strategies handle constraints in the presence of disturbances. In response to this need, in this article, we present the distributed and localized model-predictive control (DLMPC) algorithm for large-scale linear systems. DLMPC is a distributed closed-loop model-predictive control (MPC) scheme, wherein only local state and model information needs to be exchanged between subsystems for the computation and implementation of control actions. We use the system-level synthesis framework to reformulate the centralized MPC problem and show that this allows us to naturally impose localized communication constraints between subcontrollers. The structure of the resulting problem can be exploited to develop an alternating-direction-method-of-multipliers-based algorithm that allows for a distributed and localized computation of closed-loop control policies. We demonstrate that the computational complexity of the subproblems solved by each subsystem in DLMPC is independent of the size of the global system. DLMPC is the first MPC algorithm that allows for the scalable computation and implementation of distributed closed-loop control policies and deals with additive disturbances. In our companion paper, we show that this approach enjoys recursive feasibility and asymptotic stability. © 2014 IEEE.

关键词： Model predictive control

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Research challenges of Blockchain in 6G Network

Research challenges of Blockchain in 6G Network

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2022 IEEE Delhi Section Conference, DELCON 2022

作者： Sharma, Tripti Prasad, Sanjeev Kumar Sharma, Vedangi Inderprastha Engineering College Department of Computer Science-Data Science Ghaziabad India Galgotias University School of Computer Science and Engineering Greater Noida India Galgotias University School of Computing Science and Engineering U.P. Greater Noida India

ISBN: (纸本)9781665458832

Nowadays, video calling is very much on demand, and it is working well in 4G and 5G networks. It is a time for 3D calling, virtual reality live streaming and holographic communication;Accessing this type of data requires ultrahigh data rates and traffic volumes, so there is a need to speed up the network and increase bandwidth. On the other hand, a system has to maintain reliability, security, availability and flexibility in a wireless network. The network should be extended to the needs of human life such as entertainment, healthcare, smart cities, and transportation expected to enhance service quality with a high-end user experience. To establish this network, the telecommunications infrastructure must upgrade its unmatched service level requirements. The two major disruptive emerging components in this modern Internet-enabled era of technology are both the blockchain and the Internet of Things (IoT). In research and industry, IoT has experienced exponential growth, although it suffers from several limitations, such as poor interoperability, decentralization, privacy, and security vulnerabilities. 6G applications can be based on telecommunications networks and IoT, therefore, the network must ensure strong connectivity between nodes and also ensure the privacy and security of user data. Blockchain is the appropriate solution for these issues. Blockchain is a distributed ledger technology and it enables many industries to exchange data over the Internet between trusted parties without using a central server. The blockchain facilitates verifiable, transparent and secure digital asset transactions with proof of right and ownership. There is a need to identify the role of blockchain in 6G networks. This paper explores the challenges and opportunities related to blockchain for 6G networks, future application possibilities and upcoming research directions. © 2022 IEEE.

关键词： Internet of things

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RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection

arXiv

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arXiv 2025年

作者： Shi, Jianrui Zhao, Yong Cui, Zeyang Shen, Xiaoming Zeng, Minhang Liu, Xiaojie Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computing and Data Science The University of Hong Kong Hong Kong Pengcheng Laboratory Shenzhen China

Object detection plays a crucial role in smart video analysis, with applications ranging from autonomous driving and security to smart cities. However, achieving real-time object detection on edge devices presents significant challenges due to their limited computational resources and the high demands of deep neural network (DNN)-based detection models, particularly when processing high-resolution video. Conventional strategies, such as input down-sampling and network up-scaling, often compromise detection accuracy for faster performance or lead to higher inference latency. To address these issues, this paper introduces RE-POSE, a Reinforcement Learning (RL)Driven Partitioning and Edge Offloading framework designed to optimize the accuracy-latency trade-off in resource-constrained edge environments. Our approach features an RL-Based Dynamic Clustering Algorithm (RL-DCA) that partitions video frames into non-uniform blocks based on object distribution and the computational characteristics of DNNs. Furthermore, a parallel edge offloading scheme is implemented to distribute these blocks across multiple edge servers for concurrent processing. Experimental evaluations show that RE-POSE significantly enhances detection accuracy and reduces inference latency, surpassing existing methods. © 2025, CC BY.

关键词： Computation offloading

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A Novel Deep Learning based Stress Analysis and Detection Scheme using Characteristic data 8

A Novel Deep Learning based Stress Analysis and Detection Sc...

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8th IEEE International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2023

作者： Geetha, R. Krishnamoorthy, N.V. Shakthi Murugan, K.H. Gnanaprakasam, C. Swarna, M. Srm Institute of Science and Technology Department of Computing Technologies Chennai India Sri Krishna College of Engineering and Technology Department of Mechanical Engineering Tamilnadu Kuniyamuthur Coimbatore641008 India Narayana Engineering College Department of Electronics and Communication Engineering AndhraPradesh Gudur524101 India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Poonamallee Chennai600123 India

ISBN: (纸本)9798350347791

One of the most time-consuming and complex study subjects in the present day is trying to anticipate the amount of stress experienced by working professions. Although short-term stress might help us perform under pressure, chronic or severe stress compromises our well-being and causes disruptions in our daily life. Many stress-related health issues are avoidable if identified and treated early. A person's stress level may be determined by monitoring changes in a variety of biosignals, including temperature, electromagnetic, resistive, acoustical, visual and so on. There are a plethora of studies that may help you figure out what causes your stress and what kinds of things help you deal with it. Previous studies had a number of drawbacks, including more design complexity, more mistakes, worse efficiency, and a higher rate of misclassification. This research aims to apply an enhanced deep learning technique called Learning based Novel Stress Analyzer (LNSA) to forecast the level of stress of industry professionals in an effort to address these problems. Included in the methods proposed by this study are information preparation, extraction of features, classification and LNSA-based identification. Professionals' logical characteristic data is gathered via sensors and used as a dataset for processing the complete system. The sensors known as 3-Axis Accelerometer, Electrocardiogram, Pulse Sensor, Body Temperature and Respiration Sensor collect the defining data. Specifically, it eliminates redundant characteristics and completes incomplete data in the relevant dataset through a process called pre-processing. This paper provides the final classification findings, in which it show high accuracy in results and the technique of LNSA suggested in this paper accurately estimate the level of stress in a given situation. © 2023 IEEE.

关键词： Classification (of information)

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Enabling Protection for Critical Infrastructure through Security and Privacy in the Industrial Internet of Things 9

Enabling Protection for Critical Infrastructure through Secu...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Yasotha, B. Vijayanandh, T. Thirumoorthy, P. Priya, M.Sangeetha Sasidaran, S. Prakalya, S.B. Srmist Department of Data Science and Business Systems Kattankulathur India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Avadi Tamil Nadu Chennai India Dhanalakshmi College of Engineering Department of Electrical and Electronics Engineering Chennai India Chennai India Panimalar Engineering College Department of Information Technology Chennai600123 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350377972

Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causing huge disruptions and financial losses. IoT network security and privacy must be prioritized to protect key infrastructure. IIoT security issues and solutions are examined in this research. First, we take a look at the state of security and the unique risks that industrial IoT devices are up against, including hacking, illegal access, and cyber-physical assaults. Then, to lessen the impact of these dangers, we investigate cutting-edge security solutions, such as authentication, encryption, and access control systems. Building a robust security architecture specifically for IIoT applications is one of the main goals of the project. To improve safety and anonymity, this system uses cutting-edge innovation like blockchain, AI, and edge computing. We also talk about how privacy-preserving methods like differential privacy and anonymization are important for keeping sensitive information safe while allowing data sharing for analysis and operations. We assess the efficacy of the suggested security protocols and procedures using a battery of case studies and simulations. Reducing the attack surface and improving the resilience of IIoT systems can be achieved by a multi-layered security approach that includes effective privacy protections. We conclude by outlining our research's larger implications, which include the importance of standardization, regulatory compliance, and stakeholder collaboration in ensuring the safe and privacy-protecting deployment of IIoT technologies in vital infrastructure. Protecting vital infrastructure from cyber threats is our ultimate goal, and our work helps build IIoT systems that are more secure and dependable. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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Using Arduino Iot Modules As A Low Cost Environmental Research Monitoring System 1

Using Arduino Iot Modules As A Low Cost Environmental Resear...

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1st International Conference on Computer science and Emerging Technologies, CSET 2023

作者： Magableh, Murad Marie, Ziyad Mohamed, Rajina R. Bin Ibrahim, Mohd Hafizuddin Jusoh, Julaily Aida Kumar, Rohit Irbid National University Faculty of Business and Financial Science MIS BI and Data Analysis Department Irbid Jordan Al-Istiqlal University Faculty of Administrative Sciences and Informatics Public Administration Department Palestine College of Computing Dan Informatics Universiti Tenaga Nasional Malaysia Politeknik Kuala Terenngganu Department of Electrical Engineering Kuala Terengganu20200 Malaysia UniSZA Faculty of Informatics and Computing Malaysia School of Computer Science and Engineering Jain Deemed to Be University Bangalore India

ISBN: (纸本)9798350341737

Environmental monitoring plays a crucial role in understanding the impact of human activities on our ecosystems. However, the cost and complexity of traditional monitoring systems often hinder widespread deployment, especially in resource-constrained regions. This research paper explores the feasibility and effectiveness of employing Arduino Internet of Things (IoT) modules as a low-cost alternative for environmental research monitoring. The study focuses on designing and implementing an Arduino-based environmental monitoring system capable of gathering real-time data on key environmental parameters, such as temperature, humidity, air quality, and soil moisture. The hardware setup comprises Arduino microcontrollers interfaced with a range of environmental sensors and connectivity modules for data transmission. To assess the system's reliability and accuracy, a series of field experiments were conducted in diverse environmental settings, including urban areas, agricultural zones, and natural habitats. The collected data was compared against measurements from conventional monitoring equipment to validate the Arduino-based system's performance. The research demonstrates that the Arduino IoT modules can effectively monitor and transmit environmental data with a reasonable level of accuracy and stability, making them suitable for various environmental research applications. The low-cost nature of Arduino components and the open-source ecosystem facilitate customization and scalability, enabling researchers and organizations to develop tailored monitoring solutions for specific environmental contexts. Additionally, the paper discusses the challenges and limitations encountered during the deployment of Arduino-based monitoring systems, including power constraints, sensor calibration, and data processing techniques. Furthermore, it explores potential avenues for improvement and optimization to enhance the system's capabilities and data accuracy. In conclusion, this research

关键词： Internet of things

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Intelligent Predictive Model for Hepatitis C

Intelligent Predictive Model for Hepatitis C

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Artificial Intelligence (ICAI), International Conference on

作者： Mehreen Shahzadi Faisal Bukhari Numan Shafi Department of Data Science Faculty of Computing & Information Technology University of the Punjab Lahore Pakistan Department of Computer Science University of Engineering and Technology Lahore Pakistan

Hepatitis C is the liver's festering that can lead to severe liver damage, usually caused by the hepatitis C virus. Hepatitis C has different stages. It is tough to cure in it's last stages; at the same time, it is expensive and painful process. The current research, however, is an alternative precaution to this issue. Hepatitis C can be predicted early by using multiple factors. The dataset related to hepatitis C was not publicly available. To overcome this challenge, the healthy and HCV effected samples were collected from different hospitals in Punjab. A questionnaire based survey was taken including different HCV related factor i.e. gender, weight loss, hives/ rashes, swelling, jaundice, drug addiction history, hepatic encephalopa-thy (drowsiness, slurred speech), Ascites (fluid buildup in belly/ abdomen), spider angiomas (Spiderlike blood vessels), shared syringe usage, medical history, and severeness. Different cleaning, scaling, and feature selection techniques were applied to collect the best feature data. After selection, various machine learning algorithms were applied. Random forest, KNN, Decision Tree, SVC, and MLP were used, but MLP yielded optimal results in all classification algorithms. We have gained 95.9 % accuracy when tested on unknown data based on the MLP model. As the predictions' results were satisfactory, it would be helpful for the people and act as a critical awareness.

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Evaluation of Wireless Sensor Networks Module using IoT Approach 3

Evaluation of Wireless Sensor Networks Module using IoT Appr...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Anand, L. Padmalal, S. Seetha, J. Juliana, R. Naveen Kumar, P.S. Parasa, Gayatri Srm Institute of Science and Technology School of Computing Department of Networking and Communications Kattanguluthur India Mangalam College of Engg Department of Computer Science and Engineering Kerala India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India Loyola-ICAM College of Engineering and Technology Department of Information Technology Chennai India St. Ann's College of Engineering and Technology Department of CSE-DATA Science AP Chirala India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9781665462167

Microcomputers and medical devices with signal transceivers that operate on a specific radio display constitute the backbone of wireless sensor networks (WS Ns) that monitor environmental conditions (temperature, pressure, light, vibration levels, location). It is widely used in WAN sensor networks because of its flexible design and low setup fees. The u200b touch network allows for the connection of up to 65,000 devices, while the Intelligent sensors on other wireless networks are used to transfer data ports and assign wireless networks. Since the price of wireless solutions has been decreasing, and their functional capabilities have been growing, they are gradually replacing wired ones in telemetry data gathering systems and long- distance detecting communication. A deep learning model was used in this investigation to prevent the sensor nodes from manipulating data. Sensor nodes include a lot of parameters and estimations. If these projected data values are altered, network performance will suffer, and the node's lifetime will be reduced. data security became a priority when the sensor nodes were distributed. This new method is 98.82% more efficient than the previous one. © 2023 IEEE.

关键词： Sensor nodes

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Implicit Supervision-Assisted Graph Collaborative Filtering for Third-Party Library Recommendation

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IEEE Transactions on Services computing 2025年第3期18卷 1459-1471页

作者： Lianrong Chen Mingdong Tang Naidan Mei Fenfang Xie Guo Zhong Qiang He School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China Unicom (Guangdong) Industry Internet Company Guangzhou China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computing Technologies Swinburne University of Technology Melbourne VIC Australia

Third-party libraries (TPLs) play a crucial role in software development. Utilizing TPL recommender systems can aid software developers in promptly finding useful TPLs. A number of TPL recommendation approaches have been proposed and among them graph neural network (GNN)-based recommendation is attracting the most attention. However, GNN-based approaches generate node representations through multiple convolutional aggregations, which is prone to introducing noise, resulting in the over-smoothing issue. In addition, due to the high sparsity of labelled data, node representations may be biased in real-world scenarios. To address these issues, this paper presents a TPL recommendation method named Implicit Supervision-assisted Graph Collaborative Filtering (ISGCF). Specifically, it takes the App-TPL interaction relationships as input and employs a popularity-debiased method to generate denoised App and TPL graphs. This reduces the noise introduced during graph convolution and alleviates the over-smoothing issue. It also employs a novel implicitly-supervised loss function to exploit the labelled data to learn enhanced node representations. Extensive experiments on a large-scale real-world dataset demonstrate that ISGCF achieves a significant performance advantage over other state-of-the-art TPL recommendation methods in Recall, NDCG and MAP. The experiments also validate the superiority of ISGCF in mitigating the over-smoothing problem.

关键词： Vectors Noise Training Collaborative filtering Recommender systems Libraries Predictive models Graph neural networks Electronic mail Convolution

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Color-Based Ripeness Classification of Oil Palm Fresh Fruit Bunches Using Convolution Neural Networks and data Augmentation 7

Color-Based Ripeness Classification of Oil Palm Fresh Fruit ...

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7th IEEE International Conference on Electronics and Communication Engineering, ICECE 2024

作者： Bakar, Juhaida Abu M Kehail, Mahmoud A Mat Kassim, Muhamad Syafiq Saravanan, Ranjithkumar Cheng, Zhao Wei Dahham Al-Marsoomi, Ali Ghazi School of Computing Universiti Utara Malaysia Data Science Research Lab Kedah Malaysia UCSI University Department of Mechanical Engineering Cheras Malaysia

ISBN: (纸本)9798331517755

Proper ripeness categorization of oil palm fresh fruit bunches is essential to ensure they are harvested at the optimal stage to maximize oil output. The current manual method of ripeness assessment, which relies on harvesters visually inspecting the fruit's color, is both labor-intensive and prone to human error. To address these limitations, there is a growing need for an automated classification system that leverages artificial intelligence. The manual classification process is cumbersome and susceptible to inaccuracies due to its reliance on subjective visual inspection. This labor-intensive approach increases the likelihood of human error, underscoring the necessity for a more reliable and efficient automated solution. Thus, this study aims to evaluate the feasibility of using Convolutional Neural Networks for automating the grading based on color. A dataset was compiled from various sources and augmented using a data generator. The model was constructed incorporating a transfer learning approach with an Xception model. The model achieved an overall accuracy of 80.4%. Visualization of the model using activation maps confirmed that the model can effectively classify the color, indicating its potential viability for the task. © 2024 IEEE.

关键词： Palmprint recognition

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