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Computing (ICOCO), IEEE International Conference on

作者： Muntadher Saadoon Siti Hafizah Ab. Hamid Hazrina Sofian Hamza Altarturi Nur Nasuha Zati Hakim Azizul Haqi Khalid Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Artificial Intelligence Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Computer and Communication Faculty of Engineering Universiti Putra Malaysia Selangor Malaysia

Internet of Things, edge computing devices, the widespread use of artificial intelligence and machine learning applications, and the extensive adoption of cloud computing pose significant challenges to maintaining fault tolerance in distributed systems. As the volume and complexity of systems continue to grow, the probability of system failures also grows, leading to downtime and poor performance. Fault tolerance is pivotal in modern computing systems by preventing downtime and service disruptions, even during failures. However, adopting fault tolerance comes with additional resource consumption, such as memory, CPU, disk, and network bandwidth usage, impacting system performance. Balancing fault tolerance with system performance is crucial to optimize resource utilization while minimizing the costs associated with detecting and recovering from faults. This paper introduces a node-to-node fault tolerance coordination mechanism for distributed systems to facilitate the tradeoff between fault tolerance and the cost of performance penalties and resource consumption. The proposed mechanism is preliminarily implemented on HDFS cluster. Our proposal enables opportunities to minimize the cost of the fault tolerance impact on performance and resource usage compared to the existing fault tolerance coordination mechanism.

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Ontological Model for Cohesive Smart Health Services Management

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Computers, Materials & Continua 2023年第2期74卷 3679-3695页

作者： Muhammad Raza Naqvi Muhammad Waseem Iqbal Syed Khuram Shahzad M.Usman Ashraf Khalid Alsubhi Hani Moaiteq Aljahdali Le Laboratoire Génie de Production de l’École Nationale d’Ingénieurs de Tarbes(LGP-INP-ENIT) Universitéde ToulouseCEDEXTarbes65016France Department of Software Engineering Superior UniversityLahore53700Pakistan Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan Department of Computer Science University of Management and TechnologySialkot51310Pakistan Department of Computer Science King Abdul-Aziz UniversityJeddah21577Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21577Saudi Arabia

Health care has become an essential social-economic concern for all stakeholders(e.g.,patients,doctors,hospitals etc.),health needs,private care and the elderly class of *** massive increase in the usage of health care Internet of things(IoT)applications has great technological evolvement in human *** are various smart health care services like remote patient monitoring,diagnostic,disease-specific remote treatments and *** applications are available in a split fashion and provide solutions for variant diseases,medical resources and remote service *** main objective of this research is to provide a management platform where all these services work as a single unit to facilitate the *** ontological model of integrated healthcare services is proposed by getting requirements from various existing healthcare *** were 26 smart health care services and 26 smart health care services to classify the knowledge-based ontological *** proposed ontological model is derived from different classes,relationships,and constraints to integrate health care *** model is developed using Protégébased on each interrelated/correlated health care service having different *** querying SPARQL protocol and RDF query language(SPARQL)were used for knowledge *** Pellet Reasoner is used to check the validity and relations coherency of the proposed ontology *** to other smart health care services integration systems,the proposed ontological model provides more cohesiveness.

关键词： Ontology internet of things smart health services integration

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An software Defined Networking (SDN) Enhanced Edge Computing Framework for Internet of Healthcare Things (IoHT)

An Software Defined Networking (SDN) Enhanced Edge Computing...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Dang Van Anh Abdellah Chehri Nguyen Minh Quy Chu Thi Minh Hue Dinh C. Nguyen Vu Khanh Quy Faculty of Information Technology Hung Yen University of Technology and Education Vietnam Department of Mathematics and Computer Science Royal Military College of Canada Kingston Canada Faculty of Software Engineering FPT University Hanoi Vietnam Department of Electrical and Computer Engineering The University of Alabama in Huntsville Alabama USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The rapid proliferation of intelligent Internet of Health Things (IoHT) applications within the context of the COVID-19 pandemic has exerted significant strain on the backhaul network infrastructure. This paper aims to introduce a novel framework that leverages software-defined networking (SDN) to enhance edge computing capabilities. This framework will expect to facilitate dynamic and adaptable communication between edge and cloud servers, specifically designed to support real-time Internet of Health Things applications. Through the establishment of a connection between servers and the software-Defined Networking controller, the system is expected to facilitate load balancing, network optimization, and the utilization of resources in an efficient manner. This, in turn, enables the provision of real-time healthcare services. Ultimately, the efficacy of the suggested framework is assessed by analyzing its impact on service response time. The research results indicate that the proposed framework significantly benefits IoHT systems’ service response times across various workloads and traffic.

关键词： Cloud computing Collaboration Internet of Medical Things Load management Real-time systems Servers Time factors software defined networking Optimization Edge computing

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Smart Parking Dapp: A Trust Minimised Smart Parking Solution

Smart Parking Dapp: A Trust Minimised Smart Parking Solution

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IEEE International Conference on Blockchain (Blockchain)

作者： Nazim Rizvic Amin Sakzad Arash Mirzaei Taha Hossein Rashidi Department of Software Systems & Cybersecurity Faculty of Information Technology Monash University Melbourne VIC Australia School of Civil and Environmental Engineering Faculty of Engineering University of New South Wales Kensington NSW Australia

Blockchain-based smart parking applications provide an opportunity to increase transparency and efficiency while protecting user data. This paper presents a decentralised smart parking application built on the Ethereum blockchain, that incorporates existing protocols and standards to improve upon previous blockchain smart parking solutions. The end result is an application that allows the tokenisation and reservation of parking spots between users. An analysis of the gas consumption of the application has indicated that scaling the application to be used by thousands of concurrent users remains as a challenge, and is primarily bottlenecked by data storage requirements. The findings from this research alongside the code repository form a strong foundation for the development of consumer-facing blockchain smart parking solutions.

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Early Bearing Fault Detection of Industrial Fans based on Vibration Analysis using Interpretable Deep Learning Framework

Early Bearing Fault Detection of Industrial Fans based on Vi...

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IEEE International Conference on Multi Topic

作者： Ali Saeed M. Usman Akram Muazzam A. Khan M. Belal Afsar Department of Computer Science Quaid-i-Azam University Islamabad Pakistan Department of Computer and Software Engineering National University of Sciences & Technology Islamabad Pakistan Faculty of Electrical Engineering and Information Technology Otto-von-Guericke University Maddeburg Germany

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

The global market of machine condition monitoring is projected to grow at a rate of 8.3% in next five years. The recent technological advancement in IoTs and AI has driven predictive maintenance to be one of the most effective approach in this domain. Vibration analysis is the most efficient technique used to carry out predictive maintenance, using advanced data- driven intelligent approaches. The vibration data carries most significant information regarding the health state of machine components especially bearings. The proposed hybrid framework utilizes CNN and transformer utilizing the complex weightsharing capabilities of CNNs, combined with ability of transformer to capture the broader context of spatial relationships in large-scale patterns, making it suitable for datasets of varying sizes. A fault detection accuracy of 98.86% is achieved through experimentation on a run-to-failure real-industrial environment dataset composed of vibration data of large-scale coaxial fans.

关键词： Vibrations Deep learning Condition monitoring Fans Accuracy Fault detection Transformers Data models Maintenance Predictive maintenance

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Performance Optimization Blockchain-Enabled Fog Computing with Deep Reinforcement Learning 22

Performance Optimization Blockchain-Enabled Fog Computing wi...

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Proceedings of the 2022 12th International Conference on Communication and Network Security

作者： Yihe Zhang Shanshan Tu Shengju Zhang Yongjie Yang Aiming Wu Xuetao Bai Engineering Research Center of Intelligent Perception and Autonomous Control faculty of Information Technology Beijing University of Technology China China Mobile (Suzhou) Software Technology Co. Ltd China Mobile (Suzhou) Software Technology Co. Ltd China

ISBN: (纸本)9781450397520

With the rapid development of Internet of Things technology and cloud computing technology, Fog Computing emerges with the demand of distributed and decentralized. Blockchain is considered to be an advanced technology, which enables Fog Computing data to be stored and shared safely, greatly improving the security and privacy of Fog Computing. However, the Blockchain technology currently applied in Fog Computing still has some significant disadvantages, one of which is the low throughput performance. In order to meet the demand of improving throughput, this paper proposes a Blockchain-Enabled Fog Computing performance optimization framework based on Deep Reinforcement Learning (DRL), which can dynamically adjust according to the system state to improve throughput. Dueling Deep Reinforcement Learning is adopted to obtain the optimal configuration, which can be dynamically adjusted according to Fog node status, network bandwidth and user's quality of service (QoS) requirements, including dynamic selection of block producers, block size and network bandwidth allocation. Simulation results show that the proposed scheme can effectively improve throughput performance.

关键词： Blockchain quality of service Deep Reinforcement Learning Fog Computing

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Evaluation of Valued Tolerance Rough Set and Decision Rules Method for WiFi-Based Indoor Localization in Different Environments

Evaluation of Valued Tolerance Rough Set and Decision Rules ...

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Proceedings of the International Conference on Intelligent Systems and Network, ICISN 2023

作者： Duong-Bao, Ninh He, Jing Thi, Luong Nguyen Lee, Seon-Woo Nguyen-Huu, Khanh College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Faculty of Mathematics and Informatics Dalat University Dalat66100 Viet Nam Faculty of Information Technology Dalat University Dalat66100 Viet Nam Division of Software Hallym University Chuncheon24252 Korea Republic of Department of Electronics and Telecommunications Dalat University Dalat66100 Viet Nam

ISBN: (纸本)9789819947249

Among various technologies being applied for indoor localization, WiFi has become a common source of information to determine the pedestrian’s position due to the widespread of WiFi access points in indoor environments. However, the fluctuation of the WiFi signals makes it difficult to achieve a good localization result. In this paper, to handle this problem, the valued tolerance rough set and decision rules method (VTRS-DR), which is firstly registered to WiFi fingerprinting-based localization, will be implemented and evaluated in big and complicated environments using two public datasets. The first one was conducted by a subject using a smartphone at a multi-floor library for several months. Furthermore, to evaluate the localization accuracy when WiFi data was collected from different pedestrians as well as different smartphones, a crowdsourced WiFi fingerprinting dataset was utilized. From the deep analyses of localization results, the VTRS-DR method shows high accuracy and high robustness when testing in different environments with a mean error of 3.84 m, which is 27.87% lower than other compared methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smartphones

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SPoW: A solution to the problem of centralization of computing power 22

SPoW: A solution to the problem of centralization of computi...

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Proceedings of the 2022 12th International Conference on Communication and Network Security

作者： Xuetao Bai Shanshan Tu Haishuang Yan Yongjie Yang Aiming Wu Yihe Zhang Engineering Research Center of Intelligent Perception and Autonomous Control faculty of Information Technology Beijing University of Technology China China Mobile(SuZhou) Software Technology Co. Ltd China Mobile(SuZhou) Software Technology Co.Ltd China

ISBN: (纸本)9781450397520

As the underlying data structure of encrypted digital currency, blockchain technology has become the key technology of current research because of its decentralization characteristics. Engineers and scholars from industry and academia have also extensively discussed the opportunities that the combination of blockchain technology and traditional industries may bring to the development of the industry. However, the consensus algorithm used by the current blockchain technology has the problem of energy waste caused by the centralization of computing power. In response to this problem, after in-depth research on the mechanism of traditional consensus algorithms and the process of reaching consensus, this paper proposes an SPoW algorithm that separates computation and verification to solve the problem of centralization of computing power. At the same time, in order to ensure decentralization, this paper combines the economic Kznets ratio to measure the degree of decentralization and introduces deep reinforcement learning to dynamically balance the number of node tokens.

关键词： Reinforcement learning Kznets ratio Consensus algorithm Blockchain

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The Balance Model for V2X System with Variable Infrastructure

The Balance Model for V2X System with Variable Infrastructur...

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Systems of Signals Generating and Processing in the Field of on Board Communications

作者： Pavel V. Plotnikov Gleb I. Tambovtsev Andrei G. Vladyko Faculty of Information Technology and Software Engineering The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint Petersburg Russia Faculty of Radioelectronic Systems and Robotics The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint Petersburg Russia

ISBN: (数字)9798331532635

ISBN: (纸本)9798331532642

This paper addresses the optimization problem and seeks a balance equation between key performance-influencing parameters in a Vehicle-to-Everything (V2X) network. A novel mathematical optimization model is proposed, focusing on the interaction between On-Board Units (OBUs) and both stationary and mobile Roadside Units (RSUs/mRSUs). The optimization problem is expressed through a multi-objective function considering critical parameters such as data transmission latency, energy consumption, and resource availability. The study demonstrates how RSU density and mobility impact key network performance indicators, including system stability and data loss percentage during peak hours. This work provides a structured analytical approach to optimizing Intelligent Transportation System (ITS) infrastructure, ensuring more reliable and efficient communication between vehicles.

关键词： Energy consumption Computational modeling Throughput Energy efficiency Data models Vehicle-to-everything Optimization Intelligent transportation systems Load modeling Edge computing

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Reachability deficit of variational Grover search

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Physical Review A 2024年第1期109卷 012414-012414页

作者： Xiao-Wei Li Xiao-Ming Zhang Bin Cheng Man-Hong Yung Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Department of Physics Southern University of Science and Technology Shenzhen 518055 China Center on Frontiers of Computing Studies Peking University Beijing 100871 China Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney New South Wales 2007 Australia Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China

The quantum approximate optimization algorithm (QAOA) is promising for achieving quantum computational advantage with near-term quantum devices. It was numerically shown that the QAOA cost functions exhibit a phenomenon called reachability deficit (RD), where the success probability cannot reach unity until the circuit depth exceeds a certain critical value. However, an in-depth theoretical understanding of RD remains lacking. Here we focus on a variational variant of Grover search on multiple marked solutions as a prototype for analyzing the RD problem, where we further relax the criterion of reachability by tolerating a certain probability of failure. Specifically, we obtain a general analytical expression relating the critical depth of the quantum circuit to the solution density. In the dilute limit, the critical depth is consistent with the Grover bound, exhibiting a robust quadratic scaling that is insensitive to the failure probability. Moreover, we also find that the projective mixing Hamiltonian performs significantly better than the traditional mixing Hamiltonian in the QAOA, although it is less favorable in terms of physical implementation. However, by taking into account two-body interactions in the mixing Hamiltonian, the performance becomes on par with the projective mixing Hamiltonian at the cost of O(n2) additional terms. These results represent a simplified but insightful model of the QAOA, fully addressing the dependence of required circuit depth over the ground-state degeneracy.

关键词： Quantum algorithms Search strategy

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