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IEEE Transactions on Sustainable computing 2025年

作者： Li, Dun Li, Hongzhi Crespi, Noel Minerva, Roberto Li, Ming Liang, Wei Li, Kuan-Ching Tsinghua University Department of Industrial Engineering China Institut Polytechnique de Paris Samovar Telecom SudParis Palaiseau91120 France Chizhou University College of Big Data and Artificial Intelligence Chizhou247100 China The Hong Kong Polytechnic University Research Institute for Advanced Manufacturing Hung Hom Hong Kong Hunan University of Science and Technology School of Computer Science and Engineering Hunan University of Science and Technology Sanya Research Institute Hunan Key Laboratory for Service Computing and Novel Software Technology China Providence University Dept. of Computer Science and Information Engineering Taiwan

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data in Industrial Internet of Things (IIoT) devices have highlighted the need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. Besides, we defined a storage model and data format for private data by designing and deploying smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in real-world simulations, Hyper-IIoT demonstrates excellent contract execution time control, stable system throughput, and rapid consensus processes that are capable of handling high throughput and efficient consensus in distributed systems, even in large-scale request scenarios. © 2016 IEEE.

关键词： Smart contract

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Real-time Stream Processing in IoT Environments

Real-time Stream Processing in IoT Environments

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International Conference on science Technology Engineering and Management (ICONSTEM)

作者： P. Rajasekar Rupali Shekar Bhosale Indhumathi C Sandeep K V Prasanthi Kumari N Rajendiran M Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Electronics and Telecommunication Engineering Bharati Vidyapeeth Institute of Technology Navi Mumbai Maharashtra India Department of Computer Science and Business Systems RMK College of Engineering and Technology Puduvoyal Tamil Nadu India Department of Electronics & Telecommunication Engineering Dayananda Sagar College of Engineering Bengaluru Karnataka India School of Computer Sciences University of Petroleum and Energy Studies Dehradun Uttarakhand 248007 India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

New IoT devices generate massive real-time data. Fast, real-time stream processing is needed for IoT data insight extraction. There are pros and downsides to real-time stream processing for the Internet of Things (IoT). This article analyzes present methods and technologies. The inquiry begins with large-scale, real-time IoT data analysis. Real-time analytics and bulk processing limits are important for IoT decision-making. We then investigate IoT stream processing frameworks, methodologies, and systems' complexity. We explore Internet of Things technology selection and tradeoffs between scalability, accuracy, latency, and real-time processing system scalability. It covers real-time stream processing system design and deployment and risk mitigation. Case studies and use cases highlight real-time IoT stream processing. Healthcare, smart city, and industrial IoT operational efficiency and proactive decision-making improve with real-time data. Finally, the report suggests the Internet of Things real-time stream processing research. New IoT data analytics protocols, methods, and technologies are highlighted. We teach real-time stream processing to IoT academics, practitioners, and decision-makers. They could then develop more efficient and scalable IoT data stream optimization solutions.

关键词： Privacy Data analysis Protocols Smart cities Scalability Decision making Real-time systems

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A Comprehensive Analysis in Investigating the Impact of Big Data Analytics in Wireless Networks in Failure Prediction, Prevention and Recovery 2

A Comprehensive Analysis in Investigating the Impact of Big ...

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2nd International Conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Surya, S. Rajasekaran, P. Yuriy, Shvets Johar, Siti Sarawati Tripathi, Amrendra Magno Ventayen, Randy Joy Computer Science and Engineering School of Computing Kalasalingam University India Information Technology Dr. N.G.P Institute of Technology India Financial University under the Goverment of Russian Federation Institute of Control of Science Ras Moscow Russia Centre for General Studies and Co-curricular Universiti Tun Hussein Onn Malaysia Department of Social Science Parit Raja Johor Darul Takzim Batu Pahat86400 Malaysia School of Computer Science Upes Dehradun India Pangasinan State University Lingayen Campus Philippines

ISBN: (纸本)9781665437899

The current economic and competitive environment has enabled many businesses and industries to implement digital for enhancing the business operations and support in addressing the overall objectives. Dur to this there is a mounting avalanche information being shared between the individuals, companies and governments, the increase in the usage of mobile devices, internet, social media and other ecommerce services has supported in increasing the volume of data. This trend is expected to grow exponentially and hence there is a requirement to provide wireless network in an effective manner, the increased usage of data and information has resulted in facing various failures, like call drops for mobile users, low bandwidth for internet users etc. Hence, it is highly necessary to implement tools to understand the failure prediction, prevention and recovery on the network. Hence, this study is focused in investigating the impact of big data analysis in protecting the wireless networks from such failures and support in processing large volume of information in an effective manner, the big data analytics can also extract useful data will enable in addressing the concerned parties so as to make critical decisions. In this study, the researcher intends to conduct the survey using closed ended questionnaire from the individuals so as to measure the effectiveness of applying big data for failure prevention, protection and recovery in the wireless network. © 2022 IEEE.

关键词： Wireless networks

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Variational quantum simulation of the critical Ising model with symmetry averaging

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Physical Review A 2023年第4期107卷 042620-042620页

作者： Troy J. Sewell Ning Bao Stephen P. Jordan Joint Center for Quantum Information and Computer Science College Park Maryland 20742 USA Department of Physics University of Maryland College Park Maryland 20742 USA University of Maryland Institute for Advanced Computer Studies College Park Maryland 20742 USA Computational Science Initiative Brookhaven National Laboratory Upton New York 11973 USA Microsoft Redmond Washington 98052 USA

Here we investigate the use of deep multiscale entanglement renormalization ansatz (DMERA) circuits as a variational ansatz. We use the exactly solvable one-dimensional critical transverse-field Ising model as a test bed. Numerically exact simulation of the quantum circuit ansatz can in this case be carried out to hundreds of qubits by exploiting efficient classical algorithms for simulating matchgate circuits. We find that, for this system, the DMERA strongly outperforms a standard quantum approximate optimization algorithm (QAOA)–style ansatz, and that a major source of systematic error in correlation functions approximated using the DMERA is the breaking of the translational and Kramers-Wannier symmetries of the transverse-field Ising model. We are able to reduce this error by up to four orders of magnitude by symmetry averaging, without incurring additional cost in qubits or circuit depth. We propose that this technique for mitigating systematic error could be applied to noisy intermediate-scale quantum (NISQ) simulations of physical systems with other symmetries.

关键词： Quantum algorithms Quantum simulation 1-dimensional spin chains Quantum many-body systems Approximation methods for many-body systems Ising model Tensor network methods Variational approach

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Parameter Estimation of Multiple Poles by Subspace-Based Method

Parameter Estimation of Multiple Poles by Subspace-Based Met...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Prabhu Vijayan Philippe Dreesen Ivan Markovsky Mariya Ishteva Department of Computer Science ADVISE-NUMA KU Leuven Geel Belgium Department ELEC Vrije Universiteit Brussel Brussel Belgium Department of Advanced Computing Sciences Maastricht University Maastricht The Netherlands Gran Capità International Centre for Numerical Methods in Engineering (CIMNE) Barcelona Spain Catalan Institution for Research and Advanced Studies (ICREA) Barcelona Spain

This work deals with the problem of estimating the parameters (i.e., pole multiplicity structure, pole locations, and complex amplitudes) of exponential polynomials for a given model order. We start from an exponential polynomial signal model and algebraically demonstrate the reasoning behind and the manner in which this model generalizes to the broader class of signals containing multiple poles. The poles and complex amplitudes are estimated by a subspace-based method. With the given model order as prior, a heuristic algorithm is developed to estimate the multiplicity structure of poles. Numerical simulations are conducted to study the performance of the subspace-based method and the sensitivity of poles and multiplicity estimation. Our simulations show that our pole estimation method is accurate for signals with poles of multiplicity greater than one, particularly for signals with lower orders. This allows for successful signal regeneration, producing estimates that closely match the original data. The estimation is sensitive, particularly in the presence of noise, and when the poles are complex and have a higher order of multiplicity.

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Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments 6

Deep Reinforcement Learning-based Obstacle Avoidance for Rob...

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6th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2024

作者： Li, Keqin Chen, Jiajing Yu, Dezhi Dajun, Tao Qiu, Xinyu Lian, Jieting Ji, Ryan Zhang, Shengyuan Wan, Zhenyu Sun, Baiwei Hong, Bo Ni, Fanghao Han, Jiaqi Ama University Department of Computer Science Quezon Philippines Courant Institute New York University 251 Mercer St New YorkNY10012 United States University of California Berkeley University Avenue and Oxford St School of Information BerkeleyCA94720 United States Carnegie Mellon University Master of Science in Mechanical Engineering School of Engineering 5000 Forbes Avenue PittsburghPA United States Northeastern University College of Engineering Seattle United States School of Professional Studies New York University Master of Science in Database Technology 50 West 4th Street New YorkNY United States Cornell University College of Engineering 616 Thurston Ave IthacaNY14853 United States Cornell University Master of Professional Science in Applied Statistics-Data Science College of Computing and Information Science 616 Thurston Ave IthacaNY United States Independent Researcher Beijing China University of California Irvine Master of Computer Science Donald Bren School of Information and Computer Sciences 510 Aldrich Hall IrvineCA United States Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ United States Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9798350389418

This paper proposes a deep reinforcement learning based on the warehouse environment, the mobile robot obstacle avoidance Algorithm. Firstly, the value function network is improved based on the pedestrian interaction. We can learn to obtain the relative importance of the current state and the historical trajectory state as well as the joint impact on the robot's obstacle avoidance strategy. Secondly, the reward function of reinforcement learning is designed based on the spatial behaviour of pedestrians, and the robot is punished for the state where the angle changes too much, so as to achieve the requirement of comfortable obstacle avoidance. Finally, the feasibility and effectiveness of the deep reinforcement learning-based mobile robot obstacle avoidance algorithm in the warehouse environment in the complex environment of the warehouse are verified through simulation experiments. © 2024 IEEE.

关键词： Deep reinforcement learning

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Proactive Network Optimization Using Deep Learning in Predicting IoT Traffic Dynamics

Proactive Network Optimization Using Deep Learning in Predic...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： S. Anu Priya Niyati Bhat B. Rajesh Kanna S. Rajalakshmi R. Beaulah Jeyavathana Srimathi S Department of Computer Applications Vels Institute of Science Technology & Advanced Studies ((VISTAS) Chennai B.E Computer Engineering University of Mumbai Mumbai Department of Electronics and Communication Engineering Alagappa Chettiyar College of Engineering and Technology Karaikudi Department of Electronics and Communication Engineering RMK College of Engineering and Technology Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai Department of Bio Technology Saveetha School of Engineering SIMATS Chennai Tamilnadu India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

Problems have arisen in allocating and managing network resources effectively because of the proliferation of IoT gadgets. This study aims to provide a deep learning-based strategy for forecasting and analyzing IoT traffic patterns to solve this problem. The model uses recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) cells to capture the complex temporal dependencies in IoT data streams. Following careful data gathering from various IoT sources, a thorough preprocessing phase is performed to account for missing values and outliers. Effective modeling is enabled by applying feature engineering approaches to distill meaningful information and by temporally aggregating the dataset. The heart of the study is the creation of a sequence-to-sequence architecture that allows for reliable forecasting of future IoT traffic trends. Mean Squared Error (MSE) and other performance metrics measure the quality of the proposed model's predictions. In addition to its predictive abilities, the model's analytical section reveals interesting facts about the fundamental habits underpinning IoT traffic. This dual purpose helps network managers better comprehend the ever-changing IoT ecosystem and allocate scarce resources. The results of this study have important implications for network optimization, providing a preventative strategy for dealing with the difficulties posed by the exponential growth of IoT data traffic. Regarding accuracy and interpretability, the suggested deep learning model significantly contributes to the development of intelligent network resource management in the IoT era.

关键词： Deep learning Analytical models Recurrent neural networks Ecosystems Predictive models Traffic control Internet of Things

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Dissipation-enhanced unidirectional transport in topological systems

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Physical Review B 2024年第16期109卷 165402-165402页

作者： Ming Lu Xue-Zhu Liu Hailong Li Zhi-Qiang Zhang Jie Liu X. C. Xie Beijing Academy of Quantum Information Sciences Beijing 100193 China Center for Advanced Quantum Studies Department of Physics Beijing Normal University Beijing 100875 China International Center for Quantum Materials School of Physics Peking University Beijing 100871 China School of Physical Science and Technology Soochow University Suzhou 215006 China Institute for Advanced Study Soochow University Suzhou 215006 China Department of Applied Physics School of Science Xian Jiaotong University Xian 710049 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 China Hefei National Laboratory Hefei 230088 China

Dissipation is a common occurrence in real-world systems and is generally considered detrimental to transport. In this paper, we examine the transport properties of a narrow quantum anomalous Hall system with dissipation applied on one edge. When the Fermi level resides within the hybridization gap, we find that, while transport is suppressed on one edge, it is significantly enhanced on the other. We reveal that the enhancement of the conductance on the other edge arises from dissipation-induced gap closure, which is deeply rooted in the point-gap topology of the system, resulting in a reduction of the decaying coefficient. When the dissipation is very large, we find that the low-energy physics is nearly indistinguishable from a narrower system, whose dissipation amplitude is inversely proportional to that of the original one. To get more physical intuition, we demonstrate that the low-energy physics can be well captured by a pair of coupled counterpropagating chiral edge states, one of which has a modified group velocity and an effective dissipation. We also briefly discuss the possible experimental realizations of this enhanced unidirectional transport.

关键词： Dissipative dynamics Edge states Electronic structure Mesoscopics Skin effect Topological phases of matter Transport phenomena Non-Hermitian systems Topological materials

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Enhancing Cybersecurity with AI: Anomaly Detection in Network Traffic

Enhancing Cybersecurity with AI: Anomaly Detection in Networ...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： V. Kavitha S. Malathi Vishnu Priya P M Saranya Ekambaram B. Charith M. Deivakani Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Katankullathur Tamil Nadu India Department of Information Technology Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer-Science Kristu Jayanti College Autonomous Bengaluru Karnataka India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Management & Commerce International Institute of Business Studies Bengaluru Karnataka India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

Traditional symbol processing styles have the limitation to do so in the various and dynamic nature of ultramodern data transfers. The reason is that communication systems become more and more serious. This offers a potentially useful answer in that much of the functionality of machine literacy is afforded by data driven adaption and can offer the eventuality to ameliorate signal processing performance. The aim of this composition is to carry out a thorough investigation of several classes of machine literacy practices and their function in central fields of study like modulation recognition, noise reduction, channel estimation, and signal discovery. The study shows how machine literacy ways can provide much increase in the effectiveness, delicateness and robustness of signal processing tasks in communication systems. By doing detailed analysis and comparison you witness through this is fulfilled. Machine literacy will allow the unborn communication systems to adapt to advanced situations of robotization and smartness, which makes the progress possible in the similar fields, for instance, the Internet of Things (IoT), wireless deliverances, and other.

关键词： Wireless communication Symbols Telecommunication traffic Robustness Real-time systems Internet of Things Artificial intelligence computer security Anomaly detection Robots

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Intelligent Kidney Abnormality Detection in CT Images Using GLCM and Neural Networks

Intelligent Kidney Abnormality Detection in CT Images Using ...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Chhavi Bajpai Anitha S Manish Gaur M. Raghini S. Kaliappan Imran Khan Department of Computer Science and Engineering Centre for Advanced Studies AKTU Lucknow India Department of CSE-Cyber Security ACS College of Engineering Bangalore India Department of Computer Science & Engineering Institute of Engineering and Technology AKTU Lucknow India Department of Artificial Intell igence and Data Science Velammal College of Engineering and Technology Madurai India Department of Research and Development Lovely Professional University Jalandhar India Department of Medical Surgical Nursing Sharda School of Nursing Science and Research Sharda University Greater Noida India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Kidney stone illness, one of the most common medical problems worldwide, relieves pain and drives people to the ER. Diagnoses often use imaging, but educated doctors must interpret them. computer-aided diagnostics (CAD) systems help doctors discover kidney stones more accurately. The suggested technique includes preprocessing, feature extraction, and model training. Ultrasound (US) image preprocessing includes restoration, smoothness, sharpness, and contrast enhancement. The Grey Level Co-occurrence Matrices (GLCM) analyze image texture to extract characteristics. Model training uses KELM. The KELM-based model outperforms Extreme Learning Machine (ELM) and K-Nearest Neighbours (KNN) with an average accuracy of 92.54%. This accuracy demonstrates the method can detect kidney stones in ultrasound images. This proposed shows that computer-aided kidney stone detection can be fast and accurate. The combination of GLCM's texture analysis and KELM's classification skills can help medical professionals diagnose kidney stones using imaging.

关键词： Training Solid modeling Accuracy Ultrasonic imaging Computational modeling Medical services Nearest neighbor methods Feature extraction Kidney Medical diagnostic imaging

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