• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1157-1170
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• 2007

LBS must manage various context data and make the best use of this data for application service in ubiquitous environment. Conventional mobile object data management architecture did not consider process of context data. Therefore a new mobile data management framework is needed to process location context data. In this paper, we design a new context management framework for a location based application service. A suggestion framework is consisted of context collector, context manager, rule base, inference engine, and mobile object context database. It describes a form of rule base and a movement process of inference engine that are based on location based application scenario. It also presents an embodiment instance of interface which suggested framework is applied to location context interference of mobile object.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.333-336
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• 2005

The advance of wireless telecommunication and observation technologies leads developing sensor and sensor network for serving the context information continuously. Besides, in order to understand and cope with the context awareness based on the sensor network, it is becoming important issue to deal with plentiful data transmitted from various sensors. Therefore, we propose a context awareness system to deal with the plentiful sensor data in a vast area such as the prevention of a forest fire, the warning system for detecting environmental pollution, and the analysis of the traffic information, etc. The proposed system consists of the context acquisition to collect and store various sensor data, the knowledge base to keep context information and context log, the rule manager to process context information depending on user defined rules, and the situation information manager to analysis and recognize the context, etc. The proposed system is implemented for managing renewable energy data management transmitted from a large scale area.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.122-125
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• 2008

Sensors used in many USN (Ubiquitous Sensor Network) domain applications generate a large amount of sensor stream data. The volume of sensor stream data is too huge to store the whole data and data speed is too fast to control each of them. In order to provide rapid and reliable context analysis service over sensor stream data, we propose a WHEN-DO context analysis model that supports the functionality of sliding window. This model is designed to be used as follows: If the sensor stream data satisfies condition in 'WHEN' clause, then it will execute actions in 'DO' clause in WHEN-DO context analysis model. The proposed WHEN-DO context analysis model can be applied to many other USN environment applications such as monitoring the status of a building and then taking actions in corresponding context condition.

• Korea Information Processing Society Review
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• v.11 no.6
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• pp.56-75
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• 2004

In this article we address some system characteristics and challenging issues in developing Context-aware Middleware for Ubiquitous Computing. The functionalities of a Context-aware Middleware includes gathering context data from hardware/software sensors, reasoning and inferring high-level context data, and disseminating/delivering appropriate context data to interested applications/services. The Middleware should facilitate the query, aggregation, and discovery for the contexts, as well as facilities to specify their privacy policy. Following a formal context model using ontology would enable syntactic and semantic interoperability, and knowledge sharing between different domains. Moddleware should also provide different kinds of context classification mechanical as pluggable modules, including rules written in different types of logic (first order logic, description logic, temporal/spatial logic, fuzzy logic, etc.) as well as machine-learning mechanical (supervised and unsupervised classifiers). Different mechanisms have different power, expressiveness and decidability properties, and system developers can choose the appropriate mechanism that best meets the reasoning requirements of each context. And finally, to promote the context-trigger actions in application level, it is important to provide a uniform and platform-independent interface for applications to express their need for different context data without knowing how that data is acquired. The action could involve adapting to the new environment, notifying the user, communicating with another device to exchange information, or performing any other task.

• Spatial Information Research
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• v.18 no.1
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• pp.69-76
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• 2010

Spatial data streams from sensors are useful in context-awareness for many types of applications. However, an important gap is found between spatial data stream management in real-time and complex computation for spatial context-awareness, and this brings about serious difficulty to integrate spatial data stream processing and context-awareness. In this paper, we present a system called SCONSTREAM(Spatial CONtext STREAm Management) that we have developed to resolve the gap between spatial data stream and context-awareness. The key approach of our system is to filter off unnecessary spatial data streams and convert them to the spatial context streams, which are smaller and more suitable to be processed by the context-awareness module than raw data from sensors. By experimentation, We show that SCONSTREAM resolves the functional gap between spatial stream processing and spatial context-awareness module.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.1009-1016
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• 2015

In this paper, we propose EPCM(Efficient Prediction-based Context-awareness Matrix) algorithm analyzing connectivity by predicting cluster's context data such as velocity and direction. In the existing DTN, unrestricted relay node selection causes an increase of delay and packet loss. The overhead is occurred by limited storage and capability. Therefore, we propose the EPCM algorithm analyzing predicted context data using context matrix and adaptive revision weight, and selecting relay node by considering connectivity between cluster and base station. The proposed algorithm saves context data to the context matrix and analyzes context according to variation and predicts context data after revision from adaptive revision weight. From the simulation results, the EPCM algorithm provides the high packet delivery ratio by selecting relay node according to predicted context data matrix.

• Journal of Information Technology Services
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• v.9 no.1
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• pp.113-125
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• 2010

Recently, a smart space with Ubiquitous Environment is expanding rapidly due to the development of Ubiquitous Sensor Network. Therefore, more appropriate and intelligent services of the context awareness system is being required. The previous context awareness system can provide a service to the user through the inference only on the current situation. But, it does not handle certain situation properly when the system provides abnormal result. Also it does not have any proper method of generating reliable semantic data from sensed raw data. In this paper, we are trying to solve the problems as the following approaches. First, the system recognizes abnormal result and corrects it by learning feedback from the user. Second, we suggest a method of converting sensed data into more reliable semantic data. Third, we build the system based on an Ontological context model that is capable of interoperability and reusability. Therefore, the context awareness system of our study can enhance the previous system that can generate more reliable context data, can provide more effective inference method, and can provide more intelligent system structure.

• The Journal of Korea Robotics Society
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• v.4 no.1
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• pp.56-61
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• 2009

A probability-based data generation is a typical context-generation method that is a not only simple and strong data generation method but also easy to update generation conditions. However, the probability-based context-generation method has been found its natural-born ambiguousness and confliction problems in generated context data. In order to compensate for the disadvantages of the probabilistic random data generation method, a situation propagation network is proposed in this paper. The situation propagating network is designed to update parameters of probability functions are included in probability-based data generation model. The proposed probability-based context-generation model generates two kinds of contexts: one is related to independent contexts, and the other is related to conditional contexts. The results of the proposed model are compared with the results of the probabilitybased model with respect to performance, reduction of ambiguity, and confliction.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.10
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• pp.1079-1085
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• 2014

The analysis of the variable continuous big data stram should reach the destination context awareness. This study presented a novel way of context inference of the variable data stream from sensor motes. For assessment of the sensor data, we calculated the difference of each measured value at the time window and determined the belief value of each focal element. It was beneficial that calculate and assessment of factor of situation for context inference with the Dempster-Shfer evidence theory.

• Journal of the Korea Society of Computer and Information
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• v.21 no.2
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• pp.25-32
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• 2016

In this paper, we design a middleware for context-awareness which provides compound contexts from diverse sensors on a mobile device. Until now, most of context-aware application developers have taken responsibility for context processing from sensing data. Such application-level context processing causes heavily redundant data processing and leads to significant resource waste in energy as well as computing. In the proposed scheme, we define primitive and compound context map which consists of relavant sensors and features. Based on the context definition, each application demands a context of interest to the middleware, and thus similar context-aware applications inherently share context information and procesing within the middleware. We show that the proposed scheme significantly reduces the resource amounts of cpu, memory, and battery, and that the performance gain gets much more when multiple applications which need similar contexts are running.