Research on a Method for Efficient u-Healthcare Data Transmission in M2M Environment

Cho, Gyoun-Yon

doi:10.14400/jdc.2014.12.4.251

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…In [2], the concept of a restructured VC was suggested. It was considered to provide minimum information for purposes other than statistics, academics, and research to others who are not participants in the general SOVRIN SSI creation and verification process, and it is possible to create a restructured VC with limited information in comparison to the original VC.…”

Section: Restructured Verifiable Credentialmentioning

confidence: 99%

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Hierarchical Sovereignty Management and Access Control based on Self-Sovereign Identity

Cho¹,

Jeon²,

Shin³

2021

ReBICTE

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Self-sovereign identity is a type of decentralized identity technology and has been proposed to solvevarious problems of the existing public key infrastructure based centralized identity authenticationservice. In addition, the demand for a secure decentralized access control management (ACM)technique has increased according to the demand for such next-generation identity identificationservice. Accordingly, although various ACM techniques that can be applied to decentralized serviceshave been proposed, most fall into the introductory stage of research. In this paper, based on theSOVRIN-based restructured verifiable credential concept proposed in our previous study, we providea hierarchical sovereignty and ACM technique that does not use a smart contract.

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Section: Restructured Verifiable Credentialmentioning

confidence: 99%

“…In this paper, we extend the restructured verifiable credential (VC) concept proposed in [2]. Each participant in an identity data transaction uses a different restructured VC for one credential to control access to the identity attribute [3].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Sovereignty Management and Access Control based on Self-Sovereign Identity

Cho¹,

Jeon²,

Shin³

2021

ReBICTE

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“…However, strength training should be conducted mostly through a weight training room, and there was no way to determine quantity of exercise. To solve this problem, various fitness systems have been developed [1][2][3]. Existing fitness systems sense the motion of the user and inform the consumed calorie value based on the sensor of a mobile device.…”

mentioning

confidence: 99%

User Analysis Mechanisms based Mobile Fitness System

Lee¹,

Kim²,

Jung³

2016

IJECE

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INTRODUCTIONIn the modern society, obesity has been increasing due to a number of reasons, like improper food intakes and lack of exercise. Lack of muscle mass induces various diseases. As a result, the importance of strength training is increasing day by day. However, strength training should be conducted mostly through a weight training room, and there was no way to determine quantity of exercise. To solve this problem, various fitness systems have been developed [1][2][3]. Existing fitness systems sense the motion of the user and inform the consumed calorie value based on the sensor of a mobile device. However, the data provided to existing systems are not high accuracy. Also it notifes only the bumed calories value, and information on recommendations for strength training is insufficient. In another method the system can analyze the user's body information, and provide the data to indicate how the exercise and use the fitness equipment. In this way it is difficult for the system to provide a generic data analysis, because only the information of individual users. There are also other problems with the information that is available and the user needs to actually [4][5][6][7].In this paper, we develop a system that can be used indoor and outdoor, to manage fitness due to lack of strength exercise. Mechanisms of the system analysis the users information and provide the processed data. It also provides three different recommendation data, based on different algorithm in order to solve the shortcomings of the existing systems of not providing fitness data required by the user.Recommendation algorithm is made based on the user's BMI index, BMR value and exercise level, etc. through which the user is able to receive the correct data than other fitness system. In addition, group of users with similar levels of BMI index as the user, notifies info and exercise machines with users belonging to the same group as the users. Because of this it is possible to proceed with the strength training based on various informations. Also we have used the inductive reasoning method, which allows reflecting the choice of the user to provide the data.

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