检索结果-内蒙古大学图书馆

International Conference on Communications and Signal Processing

作者： A. Arokiaraj Jovith Chitra Sabapathy Ranganathan S. Priya R. Vijayakumar R. Kohila S Prakash Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamil Nadu India Mphasis Corporation Chandler Arizona USA Department of Electrical and Electronics Engineering Academy of Maritime Education and Training (AMET) Deemed to be University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Mahendra Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Computer Science and Engineering (Cyber Security) Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Electronics and Communication Engineering Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

The Industrial Internet of Things (IIoT) has brought a new era of improved equipment monitoring and operational data management in manufacturing and other industrial settings. This research presents the use cases and benefits of IIoT sensor networks for gathering actionable insights and operational data from industrial machinery. Reliable IIoT sensor networks are built, including the design, deployment, data collecting, and cloud computing techniques. The achieved using constant, up-to-the-minute monitoring; reduced data collecting; and enhanced productivity. Data security, network stability, and scalability of the problems that develop during the deployment of IIoT sensor networks are covered in this paper. These networks might benefit from cloud computing to better manage and analyze the massive amounts of data the produce. The broader impacts of setting up IIoT sensor networks include savings in money and time and the ability to make more informed decisions based on data. It highlights the evolution of conventional industrial landscapes into linked ecosystems that may provide insightful decision-making data. The data for users to use sensor networks to monitor equipment and improve productivity. Reducing equipment downtime by 30% and increasing operational efficiency by 20% are both made potential by combining Industrial IoT sensor networks with cloud analytics. With an 80% success rate, maintenance techniques save a ton of money and make things more efficient.

关键词： Industries Productivity Cloud computing Technological innovation Decision making Transforms Real-time systems

来源：评论

学校读者我要写书评

暂无评论

A Privacy Preserving and Byzantine Robust Collaborative Federated Learning Method Design

A Privacy Preserving and Byzantine Robust Collaborative Fede...

引用

IEEE International Conference on Communications (ICC)

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Changchuan Yin Christopher G. Brinton Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Collaborative federated learning (CFL) enables device cooperation in training shared machine learning models without reliance on a parameter server. However, the absence of a parameter server also impacts vulnerabilities associated with adversarial attacks, including privacy inference and Byzantine attacks. In this context, this paper introduces a novel CFL framework that enables each device to individually determine the subset of devices to transmit FL parameters to over the wireless network, based on its neighboring devices' location, current loss, and connection information, to achieve privacy protection and robust aggregation. This is formulated as an optimization problem whose goal is to minimize CFL training loss while satisfying the privacy preservation, robust aggregation, and transmission delay requirements. To solve this problem, a proximal policy optimization (PPO)-based reinforcement learning (RL) algorithm integrated with a graph neural network (GNN) is proposed. Compared to traditional algorithms that use global information with high computational complexity, the proposed GNN-RL method can be deployed on devices based on neighboring information with lower computational overhead. Simulation results show that the proposed algorithm can protect data privacy and increase identification accuracy by 15% compared to an algorithm in which devices are partially clustered for model aggregation.

关键词： Training Privacy Federated learning Simulation Collaboration Clustering algorithms Propagation losses

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Approaches for Intrusion Detection with Emerging Cybersecurity Challenges

Deep Learning Approaches for Intrusion Detection with Emergi...

引用

Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： S. Hemalatha M. Mahalakshmi V. Vignesh M. Geethalakshmi D. Balasubramanian Anand A. Jose Department of Electrical and Electronics Engineering St. Joseph's Institute of Technology Chennai India Department of Networking and Communications SRM Institute of Science and Technology India Department of Computer Science and Engineering Karpagam College of Engineering Coimbatore India Department of Mathematics KCG College of Technology Chennai India Department of MCA Jaya Engineering College Chennai India Department of ECE KCG College of Technology Chennai Tamil Nadu India

Intrusion Detection Systems (IDS) play a pivotal role in preventing computer networks from an ever-evolving landscape of cybersecurity threats. As cyberattacks become increasingly sophisticated and diversified, the need for robust and adaptable intrusion detection models has never been more critical. This study explores the application of Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) in the context of intrusion detection, particularly in addressing emerging cybersecurity challenges. The performance of deep learning (DL)-based IDSs is highly dependent on the dataset used and no benchmark models for intrusion detection have been found. In this work, a recent dataset CIC-DDoS-2019 is used for the detection of DDOS (distributed deny of services). This work underscores the importance of adopting DL techniques, including DNNs, CNNs, and RNNs, for intrusion detection in the face of emerging cybersecurity challenges. It highlights the potential of these neural network architectures to enhance network security, adapt to evolving threats, and mitigate the risks posed by modern cyber adversaries. Moreover, it encourages further research and development in this field to stay ahead of the ever-changing cybersecurity landscape.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Survey on Machine Learning Techniques Used for Central Serous Retinopathy Detection

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

引用

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

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

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Seamless Replacement of UAV-BSs Providing Connectivity to the IoT

Seamless Replacement of UAV-BSs Providing Connectivity to th...

引用

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Hamed Hellaoui Bin Yang Tarik Taleb Jukka Manner Communications and Networking Department Aalto University Finland Chuzhou University School of Computer and Information Engineering China University of Oulu Centre for Wireless Communications Finland

ISBN: (纸本)9781665435413

This paper considers the scenario of Unmanned Aerial Vehicles (UAVs) acting as flying base stations (UAV-BSs) to provide network connectivity to ground Internet of Things (IoT) devices. More precisely, we investigate the issue where a UAV-BS needs to be replaced by a new one in a seamless way. First, we formulate the issue as an optimization problem aiming to maximize the minimum transmission rate of the served IoT devices during the UAV-BS replacement process. This is translated into jointly optimizing the trajectory of the source UAV-BS (the one to be replaced) and the target UAV-BS (the replacing one), while pushing the IoT devices to seamlessly transfer their connections to the target UAV-BS. We therefore consider a target replacement zone where the UAV-BS replacement can happen, along with IoT connections transfer. Furthermore, we propose a solution based on Deep Reinforcement Learning (DRL). More precisely, we introduce a Multi-Heterogeneous Agent-based approach (MHA-DRL), where two types of agents are considered, namely the UAV-BS agents and the IoT agents. Each agent implements a DQN (Deep Q-Learning) algorithm, where UAV-BS agents learn optimal policies to perform replacement while IoT agents learn optimal policies to transfer their connections to the target UAV-BS. The conducted performance evaluations show that the proposed approach can achieve near optimal optimization.

关键词： Performance evaluation Deep learning Base stations Q-learning Handover Autonomous aerial vehicles Trajectory

来源：评论

学校读者我要写书评

暂无评论

Ahead-Me Coverage (AMC): On Maintaining Enhanced Mobile Network Coverage for UAVs

Ahead-Me Coverage (AMC): On Maintaining Enhanced Mobile Netw...

引用

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Hamed Hellaoui Bin Yang Tarik Taleb Jukka Manner Communications and Networking Department Aalto University Finland School of Computer and Information Engineering Chuzhou University China Centre for Wireless Communications University of Oulu Finland

ISBN: (纸本)9781665435413

This paper proposes the concept of Ahead-Me Cov-erage (AMC) aiming to get the coverage of a cellular network ahead of the mobile users for maintaining enhanced Quality- of-Service (QoS) in cellular-connected unmanned aerial vehicle (UAV) networks. In such networks, each base station (BS) with an intelligent logic can automatically tilt the direction of its radio antennas based on the trajectory of UAV s. For this purpose, we first formulate AMC as an integer optimization problem for maximizing the minimum transmission rate of UAVs by jointly optimizing the angles of the different radio antenna, the resource allocation and the selection of the appropriate serving BS for the UAVs throughout their path. For this complex optimization problem, we then propose a solution based on Deep Reinforcement Learning (DRL) to solve it. Under this solution, we adopt a multi-heterogeneous agent-based approach (MHA-DRL) including two types of agents, namely the UAV agents and the BS agents. Each agent implements an Advantage Actor Critic (A2C) to learn optimal policies. Specifically, the BS agents aim to tilt their antennas to get ahead of the UAV s throughout their mobility, and the UAV agents target selecting the appropriate serving BSs along with resource allocation. Performance evaluations are presented to validate the effectiveness of the proposed approach.

关键词： Performance evaluation Transmitting antennas Reinforcement learning Quality of service Interference Directive antennas Autonomous aerial vehicles

来源：评论

学校读者我要写书评

暂无评论

Accelerating Convolutional Neural Networks via Inter-operator Scheduling

Accelerating Convolutional Neural Networks via Inter-operato...

引用

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yi You Pangfeng Liu Ding-Yong Hong Jan-Jan Wu Wei-Chung Hsu Department of Computer Science and Information Engineering National Taiwan University Graduate Institute of Networking and Multimedia National Taiwan University Graduate School of Advanced Technology National Taiwan University Institute of Information Science Academia Sinica

Convolution neural networks (CNNs) are essential in many machine learning tasks. Current deep learning frameworks and compilers usually treat the neutral network as a DAG (directed acyclic graph) of tensor operations and execute them one at a time according to a topological order, which respects the dependency in the DAG. There are two issues with this general approach. First, new CNNs have branch structures, and they form complex DAGs. These DAGs make it hard to find a good topology sort order that schedules operators within a GPU. Second, modern hardware has high computational power, which makes running operators sequentially on modern hardware under-utilizes resources. These two issues open the possibility of exploiting inter-operator parallelism, i.e., parallelism among independent operators in the DAG, to utilize the hardware resources more efficiently. In this work, we formally define the DAG scheduling problem that addresses the resource contention and propose an early-start-time-first algorithm with two heuristic rules for exploiting parallelism between independent operators. Experimental results show that our method improves the performance by up to 3.76× on RTX 3090 compared to the sequential execution.

关键词： Schedules Tensors Processor scheduling Heuristic algorithms Graphics processing units Parallel processing Hardware

来源：评论

学校读者我要写书评

暂无评论

Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspective

arXiv

引用

arXiv 2023年

作者： Wang, Jiacheng Du, Hongyang Niyato, Dusit Kang, Jiawen Cui, Shuguang Shen, Xuemin Zhang, Ping The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology Guangzhou China Chinese University of Hong Kong Shenzhen China The Department of Electrical and Computer Engineering University of Waterloo Canada The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China

As generative artificial intelligence (GAI) models continue to evolve, their generative capabilities are increasingly enhanced and being used extensively in content generation. Beyond this, GAI also excels in data modeling and analysis, benefitting wireless communication systems. In this article, we investigate applications of GAI in the physical layer and analyze its support for integrated sensing and communications (ISAC) systems. Specifically, we first provide an overview of GAI and ISAC, touching on GAI's potential support across multiple layers of ISAC. We then concentrate on the physical layer, investigating GAI's applications from various perspectives thoroughly, such as channel estimation, and demonstrate the value of these GAI-enhanced physical layer technologies for ISAC systems. In the case study, the proposed diffusion model-based method effectively estimates the signal direction of arrival under the near-field condition based on the uniform linear array, when antenna spacing surpassing half the wavelength. With a mean square error of 1.03 degrees, it confirms GAI's support for the physical layer in near-field sensing and communications. © 2023, CC BY.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

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

Enhanced Expectation-Maximization Algorithm for Smart Traffi...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： B Yamini M Jayaprakash S Logesswari V Ulagamuthalvi. R Porselvi G S Uthayakumar Department of Networking and Communication SRM Institute of Science and Technology Kattangulathur Chennai Tamil Nadu India Department of Information Technology RMK Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering RMD Engineering College Chennai Tamil Nadu India Department of Computer Science Engineering School of Computing Satyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Tagore Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering St.Joseph’s Institute of Technology OMR Chennai Tamilnadu India

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Sum Rate Maximization for RIS-Aided NOMA With Direct Links

IEEE Networking Letters

引用

IEEE networking Letters 2022年第2期4卷 55-58页

作者： Li, Xingwang Xie, Zhen Huang, Gaojian Zhang, Jianhua Zeng, Ming Chu, Zheng Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454000 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Laval University Department of Electrical and Computer Engineering Quebec CityG1V 0A6 Canada Institute for Communication Systems University of Surrey 5GIC and 6GIC GuildfordGU2 7XH United Kingdom

Reconfigurable intelligent surface (RIS) is an electromagnetic surface, and has abundant low-power reflecting elements which can dynamically tune the wireless propagation environment by changing their phase shifts. Non-orthogonal multiple access (NOMA) technology would be capable of greatly improving the spectral efficiency of communication via differentiating users through power. Via integrating RIS into NOMA to improve wireless system performance, we take an uplink RIS-aided NOMA network with direct links into account, where multiple users communicate with the access point under the assistance of a RIS with multiple elements. We aim to obtain the maximum sum rate by optimizing the phase matrix of RIS subject to users' transmitted power. Two algorithms are put forward to conquer the formulated intricate non-convex puzzle. More exactly, the semidefinite programming is proposed to relax the function while the majorization-minimization is used to derive the closed-form phase shifts. Finally, presented simulation results demonstrate the high performance of proposed schemes compared with no RIS and show the benefits of the existence of direct links. © 2019 IEEE.

关键词： Interlocking signals

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：