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IEEE International Conference on Communications (ICC)

作者： Xi Zhang Qixuan Zhu H. Vincent Poor Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The upcoming 6G mobile wireless networks are expected to support massive ultra-reliable and low-latency communications (mURLLC), which is an emerging service that demands more stringent requirements than the fifth generation (5G) wireless networks on delay and error-rate bounded quality-of-services (QoS) with massive connectivity. Finite blocklength coding (FBC) techniques based short packets communication techniques have been shown to be able to support both the statistical delay and error-rate bounded QoS provisioning. Rate splitting (RS) multiple access schemes have been proposed to address the sum degree-of-freedom loss problem over massive multiple-input and multiple-output channels when massive mobile users request to access the network. However, how to support the statistical delay and error-rate bounded QoS provisioning using FBC among massive mobile users to enable the massive access has not been sufficiently studied. In this paper, we propose to integrate FBC with RS techniques to achieve mURLLC transmissions. First, we define the ∊-effective capacity to measure the performance of statistical delay and error-rate bounded provisioning, and obtain a closed-form ∊-effective capacity under the RS scheme. Then, we maximize the aggregate ∊-effective capacity over all mobile users by deriving an optimal transmit power allocation strategy for the RS scheme. Finally, using numerical analyses, we validate and evaluate our proposed RS schemes to support statistical delay and error-rate bounded QoS provisioning over 6G communication networks in the non-asymptotic regime.

关键词： 6G mobile communication Numerical analysis Error probability Wireless networks Aggregates Measurement uncertainty Quality of service

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Survey on Machine Learning Techniques Used for Central Serous Retinopathy Detection

Survey on Machine Learning Techniques Used for Central Serou...

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Suba, M. Rajini, S. Nirmala Sugirtha Anand, M. Dr. Mgr Educational and Research Institute Department of Computer Science Chennai Tamil Nadu Maduravoyal India Dr. Mgr Educational and Research Institute Department of Computer Applications Chennai Tamil Nadu Maduravoyal India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Chennai Kattankulathur India

ISBN: (纸本)9798350344387

Central Serous Retinopathy (CSR) is an important health concern affecting millions of people worldwide, leading to vision loss and blindness. This condition is identified by abnormalities in retinal layers and fluid leakage, frequently occurring in the vicinity of the macula. The disease generates pressure inside the retinal layers, resulting in the detachment of retinal walls and subsequently causing impaired vision. As the macula is home to most of the visual sensory organs, including rods and cones, the eyesight undergoes substantial effects. Thus, early detection of CSR permits preventive actions to be taken to avoid any eye damage. While numerous physical approaches for detecting CSR have been devised previously, they have demonstrated inaccuracies and a lack of reliability. This paper examines the different algorithms, imaging modalities, and the outcomes they yield that have been devised over time for the timely detection of CSR. The research reveals that the SVM classifier has achieved a 97% accuracy rate in recognizing central serous retinopathy ailment based on OCT images. © 2023 IEEE.

关键词： Choroid Neovascularization Fundus Autofluorescence Optical Coherence Tomography image Pigment Epithelium Detachment Retinal Pigment Epithelium Support Vector Machine

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Enhanced Expectation-Maximization Algorithm for Smart Traffic IoT Systems using Deep Generative Adversarial Networks to Reduce waiting time 4

Enhanced Expectation-Maximization Algorithm for Smart Traffi...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Yamini, B. Jayaprakash, M. Logesswari, S. Ulagamuthalvi, V. Porselvi, R. Uthayakumar, G.S. Srm Institute of Science and Technology Kattangulathur Department of Networking and Communication Tamil Nadu Chennai India Rmk Engineering College Department of Information Technology Tamil Nadu Chennai India Rmd Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computing Satyabama Institute of Science and Technology Department of Computer Science Engineering Tamil Nadu Chennai India Tagore Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India St.Joseph's Institute of Technology Omr Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350300093

The main focus of the work is to improve the smart traffic systems for the Internet of Things (IoT) such as traditional signals and safe driving via tracking the congestion and monitoring traffic slowly. Depending on the circumstances, intersections between roads can sometimes lead to accidents, so this problem is considered part of the research. These limitations can be overcome by using modern sensors that can reduce the traffic signal waiting time and rash driving on the National Highway (NH) road. Urban countries make use of AI-based decisions performing smart control over managing vehicles. In the proposed model the novel combination of collecting road maps coordinating points that can connect the access movements. Also IoT devices such as mobile phones, unusual network failure, and peak time traffic control by preprocessing the collected traffic dataset. Based on the dataset the detection of coverage area can be extracted using Enhanced Expectation Maximization Algorithm for ranking the variables. Initially, the classification algorithms were used and grouped as accident months, non-accident months, hill stations, and highway NH Road delay time are some of categories. Using Deep Generative Adversarial Network (DGAN) data can be balanced and used to reduce the waiting time along with predicting the smart cities' transportation for reliable customers. © 2023 IEEE.

关键词： Generative adversarial networks

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A model checking-based security analysis framework for IoT systems

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High-Confidence Computing 2021年第1期1卷 24-34页

作者： Zheng Fang Hao Fua Tianbo Gu Zhiyun Qian Trent Jaeger Pengfei Hu Prasant Mohapatra Department of Computer Science University of CaliforniaDavisUnited States Azure Networking team MicrosoftUnited States Computer Science and Engineering department University of CaliforniaRiversideUnited States School of Computer Science and Technology Shandong UniversityChina Computer Science and Engineering department Pennsylvania State UniversityUnited States

IoT systems are revolutionizing our life by providing ubiquitous computing,inter-connectivity,and automated ***,the increasing system complexity poses huge challenges for security as IoT devices are dis-tributed,highly heterogeneous,and can directly interact with the physical *** IoT systems,bugs in device firmware,defects in network protocols,and design flaws in automation rules can lead to system breach or *** challenge gets even more escalated as the possible attacks may be chained together in a long sequence across multiple layers,rendering the existing vulnerability analysis frameworks *** this paper,we present ForeSee,a model checking-based framework to comprehensively evaluate IoT system *** builds a multi-layer IoT hypothesis graph by simultaneously modeling all of the essential components in IoT systems,including the physical environment,devices,communication protocols,and *** model checker can then analyze the generated hypothesis graph to validate system security properties or generate attack paths if there are any *** optimization algorithm is further introduced to reduce the computational complexity of our *** framework verifies hypothesis graphs with millions of nodes in less than 100 *** illustrative case studies show that our framework can detect more potential threats than the existing approaches.

关键词： IoT Security and privacy Model checking Hypothesis graph

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Latency and Cost-Aware Deployment of Dynamic Service Function Chains in 5G Networks

Latency and Cost-Aware Deployment of Dynamic Service Functio...

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Amrin Karim Jannatul Ema Tasnia Yasmin Palash Roy Md. Abdur Razzaque Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Green Networking Research Group University of Dhaka Bangladesh

Efficient deployment of a service function chain (SFC) on virtual network functions (VNFs) and their mapping to virtual machines (VMs) in 5G networks is highly important to reduce user application service latency as well as to minimize the cost of VM migration. Existing works in the literature have either considered service latency or migration cost while deploying an SFC on VMs. In this paper, the problem of dynamically mapping VNFs running a SFC to different virtual machines in a 5G network has been formulated as a multi-objective linear Programming (MOLP) problem. The developed optimization model, namely Trade-Lcm, minimizes user application service latency while reducing the cost of migrating virtual network functions associated with an SFC. The numerical performance analysis results demonstrate a significant improvement in minimizing service latency of user applications and cost of VM migration as high as 30% and 10%, respectively, compared to the state-of-the-art work.

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Energy Consumption Aware Delay Minimization for UAV Enabled Internet of Vehicle

Energy Consumption Aware Delay Minimization for UAV Enabled ...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Shariar Hossain Emon MD Lotifur Rahman Sumiya Siddika Omi Palash Roy Md. Abdur Razzaque K. M. Shadman Wadith Jargis Ahmed Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Green Networking Research Group University of Dhaka Bangladesh

Because of its adaptable nature and excellent maneuvering capabilities, the Unmanned Aerial Vehicle (UAV) can develop a potential solution for executing the Internet of Vehicles (IoV) applications. However, the limited battery capacity of the UAV devices imposes significant barriers to realtime task execution. Existing works are limited to either considering the executing tasks at the UAVs or Road Side Units (RSUs) without considering the collaborative relationship between them for minimizing service delay. In this work, we have presented an optimization framework for task execution of the Internet of Vehicles by exploiting Multi-Uav,Task Executing of Internet of Vehicle by exploiting Multi-UAV namely TEIMU, which minimizes the total average latency by considering energy consumption. The developed TEIMU framework is a combinatorial optimization problem that offloads the tasks by collaboration of RSUs and UAVs. According to the experimental findings, the developed system, TEIMU, surpasses state-of-the-art works in terms of overall average latency and energy usage by up to 10% and 25 %, respectively.

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Predicting Power Consumption Anomaly Using Statistical and Supervised Machine Learning Techniques 6

Predicting Power Consumption Anomaly Using Statistical and S...

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6th IEEE Information Technology and Mechatronics engineering Conference, ITOEC 2022

作者： Tan, Yi-Fei Tan, Wooi-Nee El-Hadad, Rawan Buhari, Abudhahir Faculty of Engineering Multimedia University Persiaran Multimedia Selangor Cyberjaya63100 Malaysia Faculty of Engineering Science and Technology Infrastructure University Department of Computer Science and Networking Kuala Lumpur Malaysia

ISBN: (纸本)9781665431859

Power has become an essential element of daily life in the modern world. At the same time, over usage of electricity may lead to excessive power consumption, causing the device to short circuit or be on fire. Therefore, it is crucial to monitor and forecast the anomalies in power consumption to avoid any tragedy. In this paper, the authors proposed a method of predicting anomalies in power consumption. The proposed method uses a statistical approach in labeling;the labeled power consumption data are then used to form the data instances. Later, supervised machine learning classification techniques, namely Support Vector Machine, Decision Tree, and Random Forest, are implemented on the data instances to predict the power consumption anomalies. The experimental results demonstrate that the precision, recall, and F1 score are achieving better results when the training dataset is larger. Besides that, the results show that the techniques used to handle the imbalanced data will affect the performance of models. © 2022 IEEE.

关键词： Electric power utilization

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User Quality of Experience and Profit Aware Task Allocation in Mobile Device Cloud

User Quality of Experience and Profit Aware Task Allocation ...

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Zeneya Sharmin Palash Roy Md. Abdur Razzaque Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Green Networking Research Group University of Dhaka Bangladesh

Mobile Device Cloud (MDC) is a promising and lucrative cloud environment that uses the idle resources of nearby mobile devices to improve the performance of compute-intensive applications. By computing code on nearby devices instead of a distant master cloud, the MDC system can improve the performance of real-time applications. It is challenging for the MDC system to efficiently use the resources because of cost and QoE. While allocating high-quality resources to mobile applications might be effective in decreasing the latency, it will increase the cost, and the allocation of low-quality resources will increase execution latency, diminishing the quality of experience (QoE) of the users. The works in the literature didn't come up with methods of trading off these two conflicting objectives in a fair way. In this paper, we present an optimization framework, namely QCMDC, for allocating tasks in an MDC environment, which brings a trade-off between maximizing users' QoE and minimizing the execution cost of the worker devices. Empirical evaluations have been carried out in Python and the results demonstrate significant performance improvement in terms of QoE and execution cost compared to other state-of-the-art works.

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Evaluation of CyPACH: A Cyber Privacy Advisor Chatbot

Evaluation of CyPACH: A Cyber Privacy Advisor Chatbot

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computer System, Information Technology, and Electrical engineering (COSITE), International Conference on

作者： Irfan Syamsuddin Rini Nur Irmawati Kasim Kasim Muhammad Fajri Raharjo Kahlil Muchtar Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia

Digital identity and assets have been increasingly more important in current digital society. Unfortunately, those who in charge in securing their own digital privacy in the form of digital identity and assets are often be the weakest link in digital security chain. Carelessness in maintaining the confidentiality of personal data repeatedly results in data leakage in the organization where the person works or even data leakage at the country level. Education and training are required to deal with the issue to enhance user awareness about cyber privacy. This paper proposes a Cyber Privacy Advisor in the form of chatbot to be learning trainer for those who want to achieve appropriate level of cyber privacy awareness. The chatbot is powered by Artificial Intelligence Scripting Language implemented by PandoraBot and AndroidJS to mimic human communications. At the end, two evaluation tools were applied to measure its technical correctness and content validity aspects which eventually indicate acceptable results.

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Smart Contract Vulnerabilities and Detection Methods: A Survey

Smart Contract Vulnerabilities and Detection Methods: A Surv...

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International Conference on Computing and networking Technology (ICCNT)

作者： Ayush Gurjar B. R. Chandavarkar Department of Computer Science and Engineering Wireless Information Networking Group (WiNG) National Institute of Technology Karnataka Surathkal Mangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Smart contracts are fundamental to blockchain technology, enabling automated contract execution without intermediaries. Their widespread adoption, particularly on platforms like Ethereum, has led to significant digital asset holdings. However, the immutability of blockchain magnifies the impact of even minor errors during smart contract development, potentially resulting in substantial financial losses. Given the frequency of vulnerabilities in existing contracts, secure smart contract development is essential. This paper conducts a comprehensive review of known vulnerabilities and detection methods to guide a more secure approach to smart contract development and promote safer blockchain ecosystems.

关键词： Surveys Reviews Smart contracts Ecosystems

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