A real-time secure medical device authentication for personal E-Healthcare services on cloud computing

Padmaja, K.; Seshadri, Ravi

doi:10.1007/s13198-021-01148-1

Cited by 9 publications

(5 citation statements)

References 19 publications

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“…Blockchain networks enable the development and control of transactions by users (Khezr et al 2019 ). Some existing studies are there (Padmaja and Seshadri 2021 ; Srivastava et al 2019 ; Kumar et al 2021 ; Chinnasamy and Deepalakshmi 2021 ; Parameswari and Ranjani 2022 ) which indirectly take a dig into this to enhance the area of working in healthcare.…”

Section: Taxonomy Of Application Based On the Integration Of Blockcha...mentioning

confidence: 99%

Semantic analysis of blockchain intelligence with proposed agenda for future issues

Saxena

Gayathri

2023

Int J Syst Assur Eng Manag

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Integrating blockchain technology with artificial intelligence (AI) i.e., blockchain Intelligence makes an extremely powerful tool that solves many multidimensional problems in several domains. Blockchain technology has the potential to provide links to shared data, transactions, and records in a decentralized, safe, and reliable manner, including the information and decision-making capability of AI which makes machines similar as capable as humans. This study is intended to present an updated systematic review of the integration of Blockchain and AI in various application areas. We have studied and summarized more than 100 research papers to present an updated version of the review. We also discuss the future of Blockchain technologies with AI. By integrating these two technologies results increases the security, efficiency, and productivity of the applications. Past works feature a few possible advantages of integration of Blockchain and AI, yet just give a restricted hypothetical system to depict forthcoming certifiable combination instances of the two advances. We survey and orchestrate surviving exploration on the integration of AI and Blockchain are other ways around to thoroughly build up a future research plan on the fusion of the two innovations. We also proposed an agenda to develop a secure system of cyber threat intelligence information exchange by using features of blockchain and artificial intelligence. This paper mainly focusses on explaining how collaboration of blockchain and AI gives immense boost in latest domains like Cybersecurity, Healthcare, Supply Chain Management, Finance and Banking and Social Media Analytics.

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Section: Taxonomy Of Application Based On the Integration Of Blockcha...mentioning

confidence: 99%

Semantic analysis of blockchain intelligence with proposed agenda for future issues

Saxena

Gayathri

2023

Int J Syst Assur Eng Manag

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“…The major aim of this research is to perform energy efficient health monitoring with low delay and high security. In WBAN, a patient’s remote healthcare monitoring system design invoked various challenges [ 27 ]. Current WBAN approaches do not ensure end-to-end security, low energy consumption and can cause delays because it is achieved either with intra-WBAN or beyond WBAN.…”

Section: Introductionmentioning

confidence: 99%

Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment

Anbarasan

Jaisankar

2022

Sensors

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Wireless body area networks (WBANs) are a research area that supports patients with healthcare monitoring. In WBAN, the Internet of Things (IoT) is connected with WBAN for a smart/remote healthcare monitoring system in which various medical diseases are diagnosed. Quality of service (QoS), security and energy efficiency achievements are the major issues in the WBAN-IoT environment. Existing schemes for these three issues fail to achieve them since nodes are resource constrained and hence delay and the energy consumption is minimized. In this paper, a blockchain-assisted delay and energy aware healthcare monitoring (B-DEAH) system is presented in the WBAN-IoT environment. Both body sensors and environment sensors are deployed with dual sinks for emergency and periodical packet transmission. Various processes are involved in this paper, and each process is described as follows: Key registration for patients using an extended version of the PRESENT algorithm is proposed. Cluster formation and cluster head selection are implemented using spotted hyena optimizer. Then, cluster-based routing is established using the MOORA algorithm. For data transmission, the patient block agent (PBA) is deployed and authenticated using the four Q curve asymmetric algorithm. In PBA, three entities are used: classifier and queue manager, channel selector and security manager. Each entity is run by a special function, as packets are classified using two stream deep reinforcement learning (TS-DRL) into three classes: emergency, non-emergency and faulty data. Individual packets are put into a separate queue, which is called emergency, periodical and faulty. Each queue is handled using Reyni entropy. Periodical packets are forwarded by a separate channel without any interference using a multi objective based channel selection algorithm. Then, all packets are encrypted and forwarded to the sink nodes. Simulation is conducted using the OMNeT++ network simulator, in which diverse parameters are evaluated and compared with several existing works in terms of network throughput for periodic (41.75 Kbps) and emergency packets (42.5 Kbps); end-to-end delay for periodic (0.036 s) and emergency packets (0.028 s); packet loss rate (1.1%); residual energy in terms of simulation rounds based on periodic (0.039 J) and emergency packets (0.044 J) and in terms of simulation time based on periodic (8.35 J) and emergency packets (8.53 J); success rate for periodic (87.83%) and emergency packets (87.5%); authentication time (3.25 s); and reliability (87.83%).

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“…29 It is defined as the percentage of patient medical data supplied by the digital medical devices to the cloud server to the data received in the same pattern at the corresponding end. 30…”

Section: Introductionmentioning

confidence: 99%

“…Latency: A cloud service latency is a delay between a customer request and the respondent of the cloud service provider 36 . It can be measured using Equation (6), where

R_{i}

refers to request and t is amount of time taken in accessing the cloud data for the corresponding users 30

L = \sum_{i = 1}^{n} R_{i} \times t

…”

Section: Introductionmentioning

confidence: 99%

“…There are some QoS factors to evaluate cloud services as defined below: Scalability: Since e‐health users and utilized tools are continually growing, the mechanism should be able to measure itself with the necessities of the scale to prevent breakdowns in the event of traffic 29 . It is defined as the percentage of patient medical data supplied by the digital medical devices to the cloud server to the data received in the same pattern at the corresponding end 30

S = \frac{Number of data packets received}{Total number of data packets transmitted} \times 100

…”

Section: Introductionmentioning

confidence: 99%

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Cloud healthcare services: A comprehensive and systematic literature review

Rahimi

Navimipour

Hosseinzadeh

et al. 2022

Trans Emerging Tel Tech

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Over the last decade, the landscape of cloud computing has been significantly changed. It has been known as a paradigm in which a shared pool of computing resources is accessible for users. The rapid growth of the healthcare environment provides better medical services to reduce costs and increase competition among healthcare providers. Despite its crucial role in the cloud, no thorough study exists in this domain. This article presents a systematic study for healthcare services in the cloud environment. A well‐organized overview of all the databases has been explored. By clustering the research goals of the found papers, we have derived four main research groups. We have further evaluated the papers concerning the background of the paper, QoS parameters, application area, or methods used for applying and formulating the main ideas presented in the works. This survey emphasizes the challenges, needs, benefits of using cloud computing in healthcare systems and provides a comprehensive and detailed study on cloud healthcare services, strengths, and weaknesses of the existing methods. Highlighting cloud health services can be the major focus of research for developing the urban healthcare system.

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A real-time secure medical device authentication for personal E-Healthcare services on cloud computing

Cited by 9 publications

References 19 publications

Semantic analysis of blockchain intelligence with proposed agenda for future issues

Semantic analysis of blockchain intelligence with proposed agenda for future issues

Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment

Cloud healthcare services: A comprehensive and systematic literature review

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