{"title":"医疗保健和健身服务:智能城市中的区块链、物联网和边缘计算综合评估","authors":"Yang-Yang Liu, Ying Zhang, Yue Wu, Man Feng","doi":"10.1007/s10723-023-09712-8","DOIUrl":null,"url":null,"abstract":"<p>Edge computing, blockchain technology, and the Internet of Things have all been identified as key enablers of innovative city initiatives. A comprehensive examination of the research found that IoT, blockchain, and edge computing are now major factors in how efficiently smart cities provide healthcare. IoT has been determined to be the most used of the three technologies. In this observation, edge computing and blockchain technology are more applicable to the healthcare industry for assessing intelligent and secured data. Edge computing has been touted as an important technology for low-cost remote access, cutting latency, and boosting efficiency. Smart cities are incorporated with intelligent devices to enhance the person's day-to-day life. Intelligent of Medical Things (IoMT) and Edge computing (EC) are these things’ bases. The increasing Quality of Services (QoS) of healthcare services requires supercomputing that connects IoMT with intelligent devices with edge processing. The healthcare applications of smart cities need reduced latencies. Therefore, EC is necessary to reduce latency, energy, bandwidth, and scalability. This paper developed a deep Q reinforcement learning algorithm with evolutionary optimization and compared it with the traditional deep learning approaches for process congestion to reduce the time and latency related to patient health monitoring. The energy consumption, latency computation, and cost computation of the proposed model is less when compared to existing techniques. Among 100 tasks, nearly 95% of the tasks are offloaded efficiently in the minimum time.</p>","PeriodicalId":54817,"journal":{"name":"Journal of Grid Computing","volume":"83 1","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Healthcare and Fitness Services: A Comprehensive Assessment of Blockchain, IoT, and Edge Computing in Smart Cities\",\"authors\":\"Yang-Yang Liu, Ying Zhang, Yue Wu, Man Feng\",\"doi\":\"10.1007/s10723-023-09712-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Edge computing, blockchain technology, and the Internet of Things have all been identified as key enablers of innovative city initiatives. A comprehensive examination of the research found that IoT, blockchain, and edge computing are now major factors in how efficiently smart cities provide healthcare. IoT has been determined to be the most used of the three technologies. In this observation, edge computing and blockchain technology are more applicable to the healthcare industry for assessing intelligent and secured data. Edge computing has been touted as an important technology for low-cost remote access, cutting latency, and boosting efficiency. Smart cities are incorporated with intelligent devices to enhance the person's day-to-day life. Intelligent of Medical Things (IoMT) and Edge computing (EC) are these things’ bases. The increasing Quality of Services (QoS) of healthcare services requires supercomputing that connects IoMT with intelligent devices with edge processing. The healthcare applications of smart cities need reduced latencies. Therefore, EC is necessary to reduce latency, energy, bandwidth, and scalability. This paper developed a deep Q reinforcement learning algorithm with evolutionary optimization and compared it with the traditional deep learning approaches for process congestion to reduce the time and latency related to patient health monitoring. The energy consumption, latency computation, and cost computation of the proposed model is less when compared to existing techniques. Among 100 tasks, nearly 95% of the tasks are offloaded efficiently in the minimum time.</p>\",\"PeriodicalId\":54817,\"journal\":{\"name\":\"Journal of Grid Computing\",\"volume\":\"83 1\",\"pages\":\"\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2023-12-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Grid Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s10723-023-09712-8\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Grid Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10723-023-09712-8","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Healthcare and Fitness Services: A Comprehensive Assessment of Blockchain, IoT, and Edge Computing in Smart Cities
Edge computing, blockchain technology, and the Internet of Things have all been identified as key enablers of innovative city initiatives. A comprehensive examination of the research found that IoT, blockchain, and edge computing are now major factors in how efficiently smart cities provide healthcare. IoT has been determined to be the most used of the three technologies. In this observation, edge computing and blockchain technology are more applicable to the healthcare industry for assessing intelligent and secured data. Edge computing has been touted as an important technology for low-cost remote access, cutting latency, and boosting efficiency. Smart cities are incorporated with intelligent devices to enhance the person's day-to-day life. Intelligent of Medical Things (IoMT) and Edge computing (EC) are these things’ bases. The increasing Quality of Services (QoS) of healthcare services requires supercomputing that connects IoMT with intelligent devices with edge processing. The healthcare applications of smart cities need reduced latencies. Therefore, EC is necessary to reduce latency, energy, bandwidth, and scalability. This paper developed a deep Q reinforcement learning algorithm with evolutionary optimization and compared it with the traditional deep learning approaches for process congestion to reduce the time and latency related to patient health monitoring. The energy consumption, latency computation, and cost computation of the proposed model is less when compared to existing techniques. Among 100 tasks, nearly 95% of the tasks are offloaded efficiently in the minimum time.
期刊介绍:
Grid Computing is an emerging technology that enables large-scale resource sharing and coordinated problem solving within distributed, often loosely coordinated groups-what are sometimes termed "virtual organizations. By providing scalable, secure, high-performance mechanisms for discovering and negotiating access to remote resources, Grid technologies promise to make it possible for scientific collaborations to share resources on an unprecedented scale, and for geographically distributed groups to work together in ways that were previously impossible. Similar technologies are being adopted within industry, where they serve as important building blocks for emerging service provider infrastructures.
Even though the advantages of this technology for classes of applications have been acknowledged, research in a variety of disciplines, including not only multiple domains of computer science (networking, middleware, programming, algorithms) but also application disciplines themselves, as well as such areas as sociology and economics, is needed to broaden the applicability and scope of the current body of knowledge.