• Journal of Software Assessment and Valuation
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• v.15 no.2
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• pp.85-93
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• 2019

In an open, heterogeneous environment based on machine-to-machine (M2M) interactions, service selection is a critical issue and the concept of social trust can be applied to service selection so that IoT devices can make the best choice for interaction. In this paper, we propose a method for evaluating the trust level of the service and for estimating the QoS of the composite service using a profile created based on social trust relationship in IoT environment. As the service selection is made through quantitative evaluation, it is expected that the result of a more reliable service combination can be obtained.

• Convergence Security Journal
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• v.18 no.2
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• pp.49-58
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• 2018

As the demand for Internet of Things(IoT) increases, the need for the security of IoT devices is increasing steadily. It is difficult to apply the conventional security theory to IoT devices because IoT devices are subject to be constrained by some factors such as hardware, processor, and energy. Nowadays we have several security guidelines and related documents on IoT device. Most of them, however, do not consider the characteristics of specific IoT devices. Since they describes the security issues comprehensively, it is not easy to explain the specific security level reflecting each characteristics of IoT devices. In addition, most existing guidelines and related documents are described in view of developers and service proposers, and thus ordinary users are not able to assess whether a specific IoT device can protect their information securely or not. We propose an security evaluation model, based on the existing guidelines and related documents, for more specific IoT devices and prove that this approach is more convenient to ordinary users by creating checklists for the smart watch.

• Journal of Software Assessment and Valuation
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• v.15 no.2
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• pp.129-140
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• 2019

In the IoT environment, there is a huge amount of heterogeneous devices with limited capacity. Existing trust evaluation methods are not adequate to accommodate this requirement due to the limited storage space and computational resources. In addition, since IoT devices are mainly human operated devices, the trust evaluation should reflect the social relations among device owners. There is also a need for a mechanism that reflects the tendency of the trustor and environmental factors. In this paper, we propose an adaptable trust evaluation method for SOA-based IoT system to deal with these issues. The proposed model is designed to minimize the confidence bias and to dynamically respond to environmental changes by combining direct evaluation and indirect evaluation. It is expected that it will be possible to secure trust through quantitative evaluation by providing feedback based on social relationships.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.10
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• pp.71-79
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• 2018

The Internet of Things is a technology that makes it possible for objects made up of hardware and software to exchange information with one another via the Internet, thereby facilitating the servitization of the objects. An IoT service, which is composed of an IoT device and a web service, has recently been applied to the marketing field and is being used as a means to meet customer needs. However, applying appropriate marketing elements to IoT services is not easy. Therefore, analysis tools are needed to properly apply marketing elements to IoT services. This study aims to construct an analysis model for marketing evaluation of IoT services. In this study, the technical elements and marketing elements of IoT were derived through a literature review, and the analysis model for marketing evaluation of IoT services was established by exploring the relationship between the elements based on a service blueprint. We also applied real cases to verify the analytical model. This study is expected to contribute to the development of tools for evaluating IoT services.

• Convergence Security Journal
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• v.14 no.7
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• pp.45-51
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• 2014

The most important point in the QoS management system to ensure the quality of the IoT system design goal is quality measurement system and the quality evaluation system. This research study is a matrix model for the IoT based on key quality measures by diagnosis system structure and function. Developing for the quality metrics measured Internet of Things environment will provide the foundation for the Internet of Things quality measurement/analysis. IoT matrix system for quality evaluation is a method to describe the functional requirements and the quality requirements in a single unified table for quality estimation performed. Comprehensive functional requirements and quality requirements by assessing the association can improve the reliability and usability evaluation. When applying the proposed method IoT quality can be improved while reducing the QoS signaling, the processing, the basis for more efficient quality assurances as a whole.

• Journal of the Korea Convergence Society
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• v.8 no.11
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• pp.63-69
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• 2017

The middleware in the IoT system is software that acts as a messenger to connect and exchange data between humans and objects, objects and objects. IoT middleware exists in various forms in all areas, including hardware, protocol, and communication of different kinds, which are different in form and purpose. However, IoT middleware exists in various forms across different areas, including hardware, protocol, and communication of different types and purposes. Therefore, even if the system is designed differently for each role, it is necessary to strengthen the security in common. In this paper, we analyze the structure of IoT middleware using Service Oriented Architecture(SOA) approach and design system security requirements based on it. It was defined: Target Of Evaluation(TOE) existing system development method and the object is evaluated by Common Criteria(CC) for verification based otherwise. The proposed middleware system will be correlated with the security problem definition and the security purpose, which will be the basis for implementing the security enhanced IoT system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.664-671
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• 2020

A Smart Building, one that uses automated processes to control its operations, refers in this study to one that uses both Internet of Things (IoT) devices and cloud services software. Cloud service providers (e.g. Amazon, Google, and Microsoft) have recently providedIoT cloud platform application services on IoT devices. According to Postscapes, there are now 152 IoT cloud platforms. Choosing one for a smart building is challenging. We selected Microsoft Azure IoT Hub and Amazon's AWS (Amazon Web Services) IoT. The two platforms were evaluated and selected from a smart building perspective. Each prototype was evaluated on two different IoTplatforms, assuming a typical smart building scenario. The selection was based on information and experience gained from developing the prototype system using the IoT cloud platform. The assessment made in this evaluation may be used to select an IoTcloud platform for smart buildings in the future.

• Journal of Convergence for Information Technology
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• v.10 no.4
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• pp.18-24
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• 2020

Recently, research on Society 5.0 has been actively carried out in Japan. The Society 5.0 is used in various areas using IoT sensors. This paper proposes a privacy approach model of multiple approaches to IoT users based on Society 5.0. The proposed model used multiple methods of synchronizing important information of IoT devices with one another in the virtual environment. The proposed model improved the efficiency of IoT information by accumulating the weight of IoT information on a probability-based basis. Further, it improves the accuracy of IoT information by segmenting it so that attribute information is linked to IoT information. As a result of the performance evaluation, the efficiency of IoT devices has improved by an average of 5.6 percent, depending on the number of IoT devices and the number of IoT hub devices. Accuracy has improved by an average of 15.9% depending on information collection and processing.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.7
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• pp.1342-1354
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• 2016

As Internet, and hardware technology are in rapid process, using rate and penetration rate of Internet of Things are increasing. Internet of Things is the physical objects with network which embedded with electronics, software, sensors, and network. Smart Home-kit to operate refrigerators, washing machines, light bulbs, and such internet of things by a smartphone has been realized. However, it is difficult to use a good quality of software based on IoT. It is because that the study related to quality evaluation of software based on IoT is deficient compared with increase amount of IoT devices. Software based on IoT includes mobility, transportability, real time accessibility and hardware characteristics. Therefore, it is necessary to have differentiated quality standards and quality model. Software quality evaluation model for IoT is proposed to satisfy these needs. Evaluation model is mapped by characteristics of IoT software based on ISO/IEC 25000's quality characteristics. Scenario based studies were applied to quality model for verification.

• Journal of Software Assessment and Valuation
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• v.15 no.1
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• pp.123-131
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• 2019

The Internet of Things (IoT) environment must be able to meet the needs of users by providing access to various services that can be used to develop diverse user applications. However, QoS issues arise due to the characteristics of the IoT environment, such as numerous heterogeneous devices and potential resource constraints. In this paper, we propose a QoS prediction method that reflects trust between users in SOA based IoT. In order to increase the accuracy of QoS prediction, we analyze the trust and distrust relations between users and identify similarities among users and predict QoS based on them. The centrality is calculated to enhance trust relationships. Experimental results show that QoS prediction can be improved.