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An Efficient Blockchain-Based Healthcare System Using Artificial Intelligence

computers, Materials & Continua 2022年第5期71卷 2721-2738页

作者： Aitizaz Ali Muhammad Fermi Pasha Ong Huey Fang Jehad Ali Mohammed A.Al.Zain Mehedi Masud Department of Software Systems and Cyber-Security School of ITMonash University47500Subang JayaMalaysia Department of Computer Engineering and Department of AI Convergence Network Ajou UniversitySuwon16499Korea Department of Information Technology College of Computers and Information TechnologyTaif University11099Taif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif University11099Taif21944Saudi Arabia

Personal health records and electronic health records are considered as the most sensitive information in the healthcare *** solutions have been provided for implementing the digital health system using blockchain,but there are several challenges,such as secure access control and privacy is one of the prominent ***,we propose a novel framework and implemented an attribute-based access control system using ***,we have also integrated artificial intelligence(AI)based approach to identify the behavior and activity for security *** current methods only focus on the related clinical records received from a medical ***,existing methods are too inflexible to resourcefully sustenance metadata changes.A secure patient data access framework is proposed in this research,integrating blockchain,trust chain,and blockchain methods to overcome these problems in the literature for sharing and accessing digital healthcare *** have used a neural network and classifier to categorize the user access to our proposed *** proposed scheme provides an intelligent and secure blockchain-based access control system in the digital healthcare *** results surpass the existing solutions by collecting attributes such as the number of transactions,number of nodes,transaction delay,block creation,and signature verification time.

关键词： Personal health records blockchain technology artificial intelligence access control attributes health care system

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Multi-hop Link Quality Prediction for DRL based Task Offloading in RSU-assisted IoV

Multi-hop Link Quality Prediction for DRL based Task Offload...

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Smart World Congress (SWC), IEEE

作者： Wei Zhao Qixue Zuo Tangjie Weng Xinwei Xu Xun Shao Zhi Liu School of Computer Science and Technology Anhui University of Technology China School of Management Science and Engineering Anhui University of Technology China Department of Electrical and Electronic Information Engineering Toyohashi University of Technology Japan Department of Computer and Network Engineering The University of Electro-Communications Japan

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Roadside units (RSUs) with strong sensing abilities enhance the feasibility of the RSU-to-Everything (R2X) paradigm, providing crucial infrastructure support for mobile edge computing and reducing data processing latency. Due to the limited computing capacity of RSUs, it is beneficial to offload tasks to other RSUs, vehicles, and cloud servers for processing, allowing for the efficient utilization of computational resources. To leverage the abundant idle vehicle resources on the road, tasks can be offloaded to vehicles outside the coverage area of the RSUs through multi-hop transmission. Due to the high mobility of vehicles, there is a risk of communication link disconnection during task transmission, which poses a challenge to the reliable transmission of tasks. In this paper, we ensure the communication link quality among nodes based on the established vehicle mobility model, and design a mechanism for selecting forwarding vehicles. The goal is to find the path with the highest reliability between vehicles and the source RSU. To encourage nodes to participate in multi-hop transmission, a certain relay cost and computational cost are paid to the computing nodes. We establish a joint optimization model focusing on task delay and task cost. To achieve this, we develop a 0-1 mathematical model and then transform it into a Markov Decision Process (MDP). Building on this foundation, we propose a solution based on Deep Reinforcement Learning (DRL). Experimental results demonstrate that this method significantly reduces task delay and task cost.

关键词： Costs Multi-access edge computing Simulation Transforms Spread spectrum communication Deep reinforcement learning Delays Computational efficiency Reliability Servers

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Resource allocation in D2D based mobile edge computing system for applications in rural areas

Resource allocation in D2D based mobile edge computing syste...

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8th International Conference on Computing in engineering and Technology (ICCET 2023)

作者： A. Garg R. Arya M. P. Singh Wireless Sensor Network Lab Department of Electronics & Communication Engineering India Department of Computer Science and Engineering National Institute of Technology Patna India

With the development with 5G the mobile connectivity in rural areas has increased. The use of artificial intelligence (AI) and internet of things (IOT) has increased its horizon in some applications like Agriculture, Dam monitoring etc. This high computation application requires high computation capacity and low latency. A multi-hop mobile edge computation with D2D communication (MECD2D) has been proposed. The resource allocation has been performed using improved artificial bee colony optimization algorithm (IABCO). It aims at minimizing overall cost of computation by reducing energy consumption and optimizing number of idle users for computation offloading. The simulation results shows that the proposed algorithm performs better as compared to the Kuhn Munkres algorithm. The performance is improved by 2% in terms of energy consumption and 32-48% in terms of resource consumption.

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LAORA: Location-Aware Orientation Adjustment for MIMO Magnetic Wireless Charging System 21

LAORA: Location-Aware Orientation Adjustment for MIMO Magnet...

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21st Annual IEEE International Conference on Sensing, Communication, and networking, SECON 2024

作者： Kong, Lingchang Wang, Xinyu Zhou, Hao Zhou, Fengyu Jiang, Shenyao Zhu, Peide Song, Qi Liu, Zhi LINKE Lab School of Computer Science and Technology University of Science and Technology of China China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China China Department of Computer Science City University of Hong Kong Hong Kong School of Mathematics and Statistics Fujian Normal University Fuzhou China Department of Computer and Network Engineering The University of Electro-Communications Tokyo182-8585 Japan

ISBN: (纸本)9798331519186

Wireless power transfer (WPT) systems using magnetic resonant coupling (MRC) have made significant progress recently, leading to various optimization methods in scenarios involving multiple-input multiple-output (MIMO) to improve charging performance. Adjusting the coil orientation of the power transmitter (TX) is a simple but effective method due to the directional nature of magnetic field distribution, but existing approaches often require unnecessary coil rotations. In this study, we introduce a Location-Aware Orientation Adjustment algorithm, known as LAORA, to address these inefficiencies. LAORA focuses on solving the problems of charging devices (RX) localization and location-based optimization. We begin by introducing the concept of Equivalent Impedance Distribution Image (EIDI) and transform the RX localization problem into a combined matching process involving EIDI. In addition, we establish a dynamic simulation framework to predict charging performance using RX-related knowledge, enabling us to obtain optimal TX orientations through reinforcement learning without needing to rotate on mechanical devices physically. We then implement the prototype and conduct extensive experiments. The results show that, compared to other existing orientation adjustment methods, LAORA achieves an average improvement of 186% while reducing the mechanical rotations by 83.3%. © 2024 IEEE.

关键词： Inductive power transmission

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Benchmarking the Performance of Mobile Mid-haul networks with Multi-flow Optical Transponders

Benchmarking the Performance of Mobile Mid-haul Networks wit...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Ítalo B. Brasileiro Iulisloi Zacarias André C. Drummond Antonio Napoli Admela Jukan Institute of Computer and Network Engineering Technische Universität Braunschweig Germany Department of Computer Science University of Brasília Brazil Infinera Munich Germany

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The next-generation mobile networks follow the 3GPP disaggregated Radio Access network (RAN) model, composed of three separate functional units: Centralized Unit (CU), Distributed Unit (DU), and Radio Unit (RU), and its interconnection network infrastructures, front-haul, between RU and DU and mid-haul, between DU and CU. The current focus has been extensively on the front-haul, proposing a myriad of networking technologies and resource allocation solutions. Mid-haul transport infrastructure has received comparably less attention. This paper explores the implementation and performance of optical multi-flow transponders in mobile mid-haul networks. It evaluates the performance of mid-haul mesh topologies with different DU placement methods. Numerical results show the achievable throughput and blocking performance. We also derive the number of Multi-flow optical transponders (MOTP) units deployed, as well as the related trade-offs.

关键词： Optical interconnections network topology Multiprocessor interconnection Throughput Optical network units Topology Transponders Resource management Next generation networking Radio access networks

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The Future of Teaching: Exploring the Integration of Machine Learning in Higher Education

The Future of Teaching: Exploring the Integration of Machine...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Taufikin Supa'at Sharma, Monika Chinmulgund, Avanti Kuanr, Jayasmita Fatma, Gulnaz Kudus Kudus Indonesia State Islamic Institute Iain Kudus Indonesia Lebanese French University College of Engineering and Computer Science Department of Computer Network Kurdistan Erbil Iraq Pune Maharashtra Pune412115 India Kalinga Institute of Social Sciences Department of English Odisha Bhubaneswar India University College Al Ardah Jazan University Jazan Saudi Arabia

ISBN: (纸本)9798350359688

The landscape of higher education is evolving rapidly, and as we stand on the brink of a new era, the integration of machine learning (ML) emerges as a transformative force. This paper delves into the future of teaching, exploring the profound impact of incorporating ML technologies into higher education. Traditional teaching methodologies face challenges in adapting to the dynamic needs of modern learners, making it imperative to explore innovative solutions. Our research delves into existing literature, shedding light on the diverse applications of ML in educational settings. With a focus on addressing current educational challenges, we propose a comprehensive framework that leverages ML algorithms to enhance the teaching and learning experience. The methodology section outlines our research approach, encompassing data collection methods and analytical techniques. The implementation model introduces a conceptual framework, detailing the integration of ML components to optimize educational processes. Results of our study demonstrate the tangible benefits of our proposed model, supported by quantitative and qualitative analyses. The conclusion synthesizes our findings, emphasizing the significance of ML integration in shaping the future of higher education. While acknowledging limitations, this paper provides a roadmap for future research in this transformative domain. As institutions navigate the evolving landscape of education, the integration of ML stands as a promising avenue to cultivate enriched learning environments and foster a generation of adaptive, tech-savvy scholars. © 2024 IEEE.

关键词： Adversarial machine learning

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A Model Based on Multiple Intermediate Entity for Cross-Domain Authentication in Public Key Infrastructure and Blockchain System 3

A Model Based on Multiple Intermediate Entity for Cross-Doma...

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3rd International Conference on Electronics, Communications and Information Technology, CECIT 2022

作者： Ma, Mengcheng Wang, Shanshan Li, Yuehong Guangdong Polytechnic of Water Resouces and Electric Engineering Network and Cloud Computing Department Guangzhou China Guangdong Industry Polytechnic Information Technology Department Guangzhou China Harbin Engineering University College of Computer Science and Technology Harbin China

ISBN: (纸本)9798350331974

In the cross-domain authentication architecture, the current method is based on public key infrastructure (PKI) and blockchain to enhance the security of data. However, most models need to be improved about the efficiency, robustness and security in the authentication phase, and in the revocation phase the most solutions cannot meet the real-time revocation or waste storage on-chain space. To solve the problem, this paper proposes a model based on multiple intermediate entities. Simulation experiments show that the model can improves the efficiency of cross-domain authentication, improves the robustness of the system, resists single-point attacks, meet the real-time in revocation, and saves space on the chain. © 2022 IEEE.

关键词： Blockchain

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AQROM:A quality of service aware routing optimization mechanism based on asynchronous advantage actor-critic in software-defined networks

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Digital Communications and networks 2024年第5期10卷 1405-1414页

作者： Wei Zhou Xing Jiang Qingsong Luo Bingli Guo Xiang Sun Fengyuan Sun Lingyu Meng School of Information and Communication Guilin University of Electronic TechnologyGuilin541004China The 34th Research Institute of China Electronics Technology Group Corporation Guilin541004China Guangxi Key Laboratory of Optical Network and Optical Information Security Guilin541004China State Key Laboratory of Information Photonics and Optical Communications BeijingUniversity of Posts and TelecommunicationsBeijing100876China Department of Electrical and Computer Engineering University of New MexicoAlbuquerqueNM87131USA

In Software-Defined networks(SDNs),determining how to efficiently achieve Quality of Service(QoS)-aware routing is challenging but critical for significantly improving the performance of a network,where the metrics of QoS can be defined as,for example,average latency,packet loss ratio,and *** SDN controller can use network statistics and a Deep Reinforcement Learning(DRL)method to resolve this *** this paper,we formulate dynamic routing in an SDN as a Markov decision process and propose a DRL algorithm called the Asynchronous Advantage Actor-Critic QoS-aware Routing Optimization Mechanism(AQROM)to determine routing strategies that balance the traffic loads in the *** can improve the QoS of the network and reduce the training time via dynamic routing strategy updates;that is,the reward function can be dynamically and promptly altered based on the optimization objective regardless of the network topology and traffic *** can be considered as one-step optimization and a black-box routing mechanism in high-dimensional input and output sets for both discrete and continuous states,and actions with respect to the operations in the *** simulations were conducted using OMNeT++and the results demonstrated that AQROM 1)achieved much faster and stable convergence than the Deep Deterministic Policy Gradient(DDPG)and Advantage Actor-Critic(A2C),2)incurred a lower packet loss ratio and latency than Open Shortest Path First(OSPF),DDPG,and A2C,and 3)resulted in higher and more stable throughput than OSPF,DDPG,and A2C.

关键词： Software-defined networks Asynchronous advantage actor-critic QoS-aware routing optimization mechanism

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Tools and environment for maximum efficiency in complex telecommunication system

Tools and environment for maximum efficiency in complex tele...

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Telecommunications Forum (TELFOR)

作者： Jovan Zlatanovic Zoran Trivic Ivana Milosevic Mirko Milosevic Network Operation Department PE ETV Belgrade Serbia Maintaining the Service Department PE ETV Belgrade Serbia Department of Electrical Engineering and Computer Science School of Technical and Art Applied Studies Academy of Electrical and Computer Engineering Belgrade Serbia

ISBN: (数字)9798350391060

ISBN: (纸本)9798350391077

The contribution of energy efficiency covers a wide range of interests. Energy efficiency affects climate change mitigation goals, energy saving, health benefits, and productivity. The quick expansion of data traffic and connection has caused the telecommunications industry's energy usage to increase significantly. It operated with equipment that is already energy efficient and it will be try to explain the additional energy saving. It will be shows a methodology that uses several parameters that can be varied in order to achieve the desired high-power transmitter efficiency. The result of the work is the saving of energy and resources through the use of tools that keep components of telecommunication systems in the optimal mode of operation using intelligent application. This application uses an intelligent algorithm that allows us to save approximately 20MW per year.

关键词： Productivity Transmitters Electricity Prevention and mitigation Energy conservation Approximation algorithms Energy efficiency Telecommunications Voltage control Intelligent systems Climate change

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A QoS-Aware Software Defined Mobility Architecture for Named Data networking

A QoS-Aware Software Defined Mobility Architecture for Named...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Ali, Jehad Adnan, Muhammad Gadekallu, Thippa Reddy Jhaveri, Rutvij H. Roh, Byeong-Hee Sejong University Department of Computer Science and Engineering Seoul Korea Republic of Kookmin University Department of Electronics Engineering Seoul Korea Republic of Vellore Institute of Technology School of Information Technology and Engineering Vellore India Pandit Deendayal Energy University Department of Electronics Engineering Gandhinagar India Ajou University Department of AI Convergence Network Suwon Korea Republic of

ISBN: (纸本)9781665459754

Named Data networking (NDN) is a future architecture for the internet and is an inventive model to provide content-based services. The unique properties of NDN, like naming schemes for network packets and approaches for in-network caching, makes it more reasonable for content distribution. However, extreme changes are required in the existing network infrastructure in order to implement NDN on it. One of the primary research dimensions for this new internet architecture is mobility. Although some research work has been done to address mobility concerns, there are still mobility issues like maintaining the connection establishment by frequently changing the position of users, loss of interest and data packets, interest and data packet transmission and re-transmission without any correspondence delay. One of the reasonable approaches is the Software Defined networking (SDN) technique, which is used to realize mobility management for NDN. The core concept of SDN is the separation of the network control plane from the data plane. The data plane is capable of data forwarding, whereas the control plane is responsible for the entire network's control. The network control plane is decoupled from the data plane, resulting in a more programmable environment and the ability for external software to define a network's behavior. The SDN's main features, such as programmability, flexibility, and centralized control, make it a simple and scalable network. To cope with the issue of NDN mobility, we propose the design of a software-defined mobility architecture for NDN in which the SDN paradigm is extended to support mobility handling in the NDN architecture. Moreover, the intra-domain and Inter-domain handover handling scenarios in the software-defined mobility architecture for NDN have been described. © 2022 IEEE.

关键词： Internet of things

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