• KIISE Transactions on Computing Practices
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• v.21 no.12
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• pp.756-761
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• 2015

With the emergence of the Internet-of-Things (IoT) paradigm, there is a growing demand for personalized services using IoT contexts acquired from heterogeneous IoT devices. However, due to the mismatch between IoT context exchange schemes of context-aware services and IoT devices, IoT applications can acquire IoT contexts only from IoT devices that support the same IoT context exchange schemes. In this paper, we propose dynamic methods to mediate those mismatches on the IoT context exchange schemes. With the proposed mediation methods, context-aware services can collect IoT contexts from heterogeneous IoT devices without considering their IoT context exchange schemes.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.8
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• pp.351-360
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• 2016

Sensors equipped in Internet-of-Thing (IoT) devices are used to measure the surrounding contexts, and IoT applications analyze the contexts to infer situations and provide situation-specific smart services. There are different context acquisition schemes including pulling, pushing, and broadcasting. Most IoT devices support only one of the schemes. Hence, there can be an incompatible issue on data acquisition schemes between applications and devices, and consequently it could result in an increased development cost and inefficiency on application maintenance. This paper presents design patterns which can effectively remedy the incompatibility problem. By applying the patterns, IoT applications with incompatibility can be systematically and effectively developed. And, also its maintainability is expected to increase.

• Journal of KIISE
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• v.42 no.12
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• pp.1575-1583
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• 2015

With the emergence of Internet-of-Things (IoT) computing, it has become possible to acquire users' health-related contexts from various IoT devices and to diagnose their biological aging through analysis of the IoT health contexts. However, previous work on methods of aging diagnosis used a fixed list of aging diagnosis factors, making it difficult to handle the variability of users' IoT health contexts and to dynamically adapt the addition and deletion of aging diagnosis factors. This paper proposes a design and methods for a dynamically adaptable aging diagnosis framework that acquires a set of IoT health contexts from various IoT devices based on a set of aging diagnosis factors of the user. By using the proposed aging diagnosis framework, aging diagnosis methods can be applied without considering the variability of IoT health contexts and aging diagnosis factors can be dynamically added and deleted.

• Journal of Service Research and Studies
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• v.2 no.2
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• pp.91-98
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• 2012

The emergence of new types of embedded computing devices and developments in wireless networking are broadening the domain of computing from the work place and home office to other facets of everyday life. This trend is expected to lead to a proliferation of Internet of Things (IoT) environments, in which inexpensive and interconnected computing devices are capable of supporting users in a range of tasks. Context-aware computing is a key source to develop such smart services. However, there are many challenges to enable services to be smart; Heterogeneous Computing Environments, Resource Limitations, Large Amount of Data Produced, and Different Requirements for Context Interpretation. Because of these challenges, there are difficulties in providing smart service by utilizing IoT devices. Currently, many researchers are conducting researches on mobile-computing based smart service development and provisioning and network infrastructure for interconnected IoT devices. Still, there are some limitations on developing core technologies for IoT computing based smart service development. In order to remedy this situation, this thesis presents a reusable framework that provides unique and noble features which are required in developing advanced context-aware IoT applications.

• Journal of IKEEE
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• v.25 no.4
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• pp.598-606
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• 2021

In this paper, we presents a new service binding and resource management model for context based services in mobile edge computing (MEC) networks. The proposed control is composed of two layers: MEC service bindng control layer (MCL) and user context control layer (UCL). The MCL manages service binding construction, resource allocation, and service policy construction from a system point of view; and the UCL manages real-time service adaptation using meta-objects. Through simulations, we confirmed that the proposed control offers enhanced throughput and content transfer time when it is compared to the legacy computing and control models. The proposed control model can be employed as a key component for the context based various internet-of-things (IoT) services in MEC environments.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.71-76
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• 2017

It is necessary to develop a service that meets various environmental requirements so that the Internet of things can be applied to the fourth industry in overall. This paper presents a method for extracting IoT devices information to change the current provider centric service provision environment in order to construct a user centered service provision environment for Web services. Also, it provides users to utilize the appropriate web services through taking advantage of IoT devices information. The existing service provision environment is focused on the method of selecting the service using the provider service information, thus, it is considered necessary for a user-oriented service search method to be in need. In this study, the service list is provided to the user by combining provider information and information extracted from IoT Devices. IoT information collection generates information by separating context information such as information between IoT devices and users and event information between devices through identifiers. In this paper, we propose a development of a user centric service environment by presenting users with a necessary service list through the proposed IoT information.

• The Journal of the Korea Contents Association
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• v.14 no.7
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• pp.29-44
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• 2014

To provide context aware service, certain phenomena and situation information of the user should be detecting and monitoring using sensors. But, the sensor data has heterogeneity about system, syntax, structure, and semantics. So, it is difficult to apply to various services. To solve this issue, we should remove these heterogeneities and convert to context information that is easy to provide context aware services. Also, the existing context aware service mash-up environment require relevant sensor type, criteria of context, programing ability to define the service situation. So, it is difficult to create service for a non-technical user. Therefore, we propose ontology based user-centric service environment to provide context aware IoT services. It provide context awareness and user-desired services using data that acquired via IoT paradigm. The proposed environment consist of the ontology based semantic sensor data processing and the user-centric service environment. It provide convert the object data to the context information and service mash-up process to create user service in easy way.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2167-2175
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• 2017

In recent years, an IoT is dramatically developing according to the enhancement of AI, the increase of connected devices, and the high-performance cloud systems. Huge data produced by many devices and sensors is expanding the scope of services, such as an intelligent diagnostics, a recommendation service, as well as a smart monitoring service. The studies of edge computing are limited as a role of small server system with high quality HW resources. However, there are specialized requirements in a smart factory domain needed edge computing. The edges are needed to pre-process containing tiny filtering, pre-formatting, as well as merging of group contexts and manage the regional rules. So, in this paper, we extract the features and requirements in a scope of efficiency and robustness. Our edge offers to decrease a network resource consumption and update rules and learning models. Moreover, we propose architecture of edge computing based on learning factors sharing with a cloud system in a smart factory.