• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.4
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• pp.14-27
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• 2009

Collection of invaluable real-time traffic data becomes available under ubiquitous transportation sensor networks (UTSN). Various research efforts have been made to utilize such useful data for deriving more accurate and reliable traffic information. This study presented a novel concept of decentralized traffic information and method to process traffic data which are obtained from inter-vehicle communications under the UTSN. In addition, an experimental evaluation to investigate the feasibility of the proposed method using probe vehicle data. Predictive travel times were estimated and evaluated for the feasibility investigation. Technical issues were derived and discussed to fully implement the proposed system. The outcomes of this study would be used as a guideline in designing better next-generation traffic information systems.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.8
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• pp.543-554
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• 2013

Model-Driven Development(MDD) helps developers verify requirements and design issues of a software system in the early stage of development process by taking advantage of a software model which is the most highly abstracted form of a software system. In practice, however, many software systems have been developed through a code-centric method that builds a software system bottom-up rather than top-down. So, without support of appropriate tools, it is not easy to introduce MDD to real development process. Although there are many researches about extracting a model from code to help developers introduce MDD to code-centrically developed system, most of them only extracted base-level models. However, using concept of abstract component one can continuously extract higher level model from base-level model. In this paper we propose a practical method for automatic extraction of base level abstract component from source code, which is the first stage of continuous extraction process of abstract component, and validate the method by implementing an extraction tool based on the method. Target code chosen is the source code of TinyOS, an operating system for wireless sensor networks. The tool is applied to the source code of TinyOS, written in nesC language.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.53-59
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• 2016

Recently, huge stream data have been generated in real time from various applications such as wireless sensor networks, Internet of Things services, and social network services. For this reason, to develop an efficient method have become one of significant issues in order to discover useful information from such data by processing and analyzing them and employing the information for better decision making. Since stream data are generated continuously and rapidly, there is a need to deal with them through the minimum access. In addition, an appropriate method is required to analyze stream data in resource limited environments where fast processing with low power consumption is necessary. To address this issue, the sliding window model has been proposed and researched. Meanwhile, one of data mining techniques for finding meaningful information from huge data, pattern mining extracts such information in pattern forms. Frequency-based traditional pattern mining can process only binary databases and treats items in the databases with the same importance. As a result, frequent pattern mining has a disadvantage that cannot reflect characteristics of real databases although it has played an essential role in the data mining field. From this aspect, high utility pattern mining has suggested for discovering more meaningful information from non-binary databases with the consideration of the characteristics and relative importance of items. General high utility pattern mining methods for static databases, however, are not suitable for handling stream data. To address this issue, sliding window based high utility pattern mining has been proposed for finding significant information from stream data in resource limited environments by considering their characteristics and processing them efficiently. In this paper, we conduct various experiments with datasets for performance evaluation of sliding window based high utility pattern mining algorithms and analyze experimental results, through which we study their characteristics and direction of improvement.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.12
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• pp.301-312
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• 2018

In industrial wireless sensor networks, the continuous object detection such as fire or toxic gas detection is one of major applications. A continuous object occurs at a specific point and then diffuses over a wide area. Therefore, many studies have focused on accurately detecting a continuous object and delivering data to a static sink with an energy-efficient way. Recently, some applications such as fire suppression require mobile sinks to provide real-time response. However, the sink mobility support in continuous object detection brings challenging issues. The existing approaches supporting sink mobility are designed for individual object detection, so they establish one-to-one communication between a source and a mobile sink for location update. But these approaches are not appropriate for a continuous object detection since a mobile sink should establish one-to-many communication with all sources. The one-to-many communication increases energy consumption and thus shortens the network lifetime. In this paper, we propose the origin-centric communication scheme to support sink mobility in a continuous object detection. Simulation results verify that the proposed scheme surpasses all the other work in terms of energy consumption.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.175-186
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• 2019

Efforts have been made to introduce the climate smart agriculture (CSA) for adaptation to future climate conditions, which would require collection and management of site specific meteorological data. The objectives of this study were to identify requirements for construction of agricultural meteorology information service system (AMISS) using technologies that lead to the fourth industrial revolution, e.g., internet of things (IoT), artificial intelligence, and cloud computing. The IoT sensors that require low cost and low operating current would be useful to organize wireless sensor network (WSN) for collection and analysis of weather measurement data, which would help assessment of productivity for an agricultural ecosystem. It would be recommended to extend the spatial extent of the WSN to a rural community, which would benefit a greater number of farms. It is preferred to create the big data for agricultural meteorology in order to produce and evaluate the site specific data in rural areas. The digital climate map can be improved using artificial intelligence such as deep neural networks. Furthermore, cloud computing and fog computing would help reduce costs and enhance the user experience of the AMISS. In addition, it would be advantageous to combine environmental data and farm management data, e.g., price data for the produce of interest. It would also be needed to develop a mobile application whose user interface could meet the needs of stakeholders. These fourth industrial revolution technologies would facilitate the development of the AMISS and wide application of the CSA.