• Journal of Institute of Control, Robotics and Systems
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• v.18 no.8
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• pp.780-786
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• 2012

Surveillance has become one of promising application areas of wireless sensor networks which allow for pervasive monitoring of concerned environmental phenomena by facilitating context awareness through sensor fusion. Existing systems that depend on a postmortem context analysis of sensor data on a centralized server expose several shortcomings, including a single point of failure, wasteful energy consumption due to unnecessary data transfer as well as deficiency of scalability. As an opposite direction, this paper proposes an energy-efficient distributed context-aware surveillance in which sensor nodes in the wireless sensor network collaborate with neighbors in a distributed manner to analyze and aware surrounding context. We design and implement multi-modal sensor stations for use as sensor nodes in our wireless sensor network implementing our distributed context awareness. This paper presents an initial experimental performance result of our proposed system. Results show that multi-modal sensor performance of our sensor station, a key enabling factor for distributed context awareness, is comparable to each independent sensor setting. They also show that its initial performance of context-awareness is satisfactory for a set of introductory surveillance scenarios in the current interim stage of our ongoing research.

• Journal of the Korean Society of Safety
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• v.23 no.6
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• pp.51-56
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• 2008

As RFID/USN technology is used in the latest industry trend, the information analysis paradigm shifts to intelligence service environment. The intelligent service includes autonomic operation, which select activity by defining itself to the status of industry facilities. Furthermore, information analysis based on IT used to frequently data mining for detecting the meaning information and deriving new pattern. This paper suggest self-classifying of context-aware by applying data mining in gas facilities for serving the intelligent gas safety management. We modify data algorithm for fitting the domain of gas safety, construct context-aware model by using the proposed algorithm, and demonstrate our method. As the accuracy of our model is improved over 90%, the our approach can apply to intelligent gas safety management based on RFID/USN environments.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1803-1810
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• 2018

Recently, many researches related to IoT (Internet of Things) have been actively conducted. In order to improve the context aware function of smart wearable devices using the IoT, we proposed a noise reduction method for the event data of the sensor part. In thisstudy, the adoption of the low - pass filter induces the attenuation of the abnormally measured value, and the benefit was obtained from the situation recognition using the event data of the sensor. As a result, we have validated attenuation for abnormal or excessive noise using event data detected and reported by 3-axis acceleration sensors on some devices, such as smartphones and smart watches. In addition, various pattern data necessary for real - time context aware were obtained through noise pattern analysis.

• The KIPS Transactions:PartA
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• v.11A no.7 s.91
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• pp.529-536
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• 2004

Smart homes integrated with sensors, actuators, wireless networks and context-aware middleware will soon become part of our daily life. This paper describes a context-aware middleware providing an automatic home service based on a user's preference. The context-aware middle-ware utilizes 6 basic data for learning and predicting the user's preference on the multimedia content : the pulse, the body temperature, the facial expression, the room temperature, the time, and the location. The six data sets construct the context model and are used by the context manager module. The log manager module maintains history information for multimedia content chosen by the user. The user-pattern learning and pre-dicting module based on a neural network predicts the proper home service for the user. The testing results show that the pattern of an in-dividual's preferences can be effectively evaluated and predicted by adopting the proposed context model.

• Journal of Advanced Navigation Technology
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• v.14 no.3
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• pp.432-438
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• 2010

In this paper, we propose a system which can perceive status of semiconductor equipment and evaluate its performance. The proposed system acquires the information such acceleration, pressure, temperature and gas sensors in the surrounding semiconductor equipment. After acquiring information, it is sent to server through multi hop transmission. The transmitted data generates 3 steps alarm using context-aware algorithm of unit or multiple event. From the experiment's result of the proposed system, we confirm that the reliability and efficiency of information is more improved about 80% than a system that doesn't use context-aware algorithm. Moreover, this system can be effective status monitoring of semiconductor equipment because lots of client nodes acquire surrounding information.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.253-255
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• 2015

In a variety of network environments, the provision of context-aware services, it is difficult to integrate and share because of the heterogeneity problem between distributed data. This paper proposes the integration model using the ontology as a method for solving the above. This uses an ontology to integrate the context-aware informations that are collected. The ontology is generated by the acquisition, semantic analysis and inference of the metadata of the context-aware information. This is the basis of the analysis and analysis of the additional system. Accordingly, this paper studies ways to create an ontology and apply them. The advantage of the proposed scheme can be used without modifying the existing tools, it is possible to easily perform the expansion and consolidation of the system.

• Journal of Communications and Networks
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• v.7 no.2
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• pp.207-221
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• 2005

The proliferation of wireless local area networks (WLANs) offering high data rate in hot spot area have spurred the demand for possible WLANs and third-generation (3G) cellular network integration solutions as the initiative step towards 4G systems. This paper provides a novel architecture for seamless location-aware integration of WLANs into 3G cellular networks and also an analysis for the efficient handover techniques. We introduce location as a key context in secure roaming mechanism for context-aware interworking in 4G systems. The fast secure roaming with location-aware authentication is implemented at an entity called location-aware service (LAS) broker that utilizes the concepts of direction of user and pre-warming zone. The location-ware interworking architecture supports seamless roaming services among heterogeneous wireless networks including WLANs, wireless metropolitan area networks (WMANs), and 3G cellular networks. This paper also includes a description of procedures needed to implement efficient mobility and location management. We show how the LAS broker with pre-warming and context transfer can obtain significant lower latency in the vertical handover.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.6
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• pp.3023-3031
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• 2013

In this paper, we propose a robot control data generation system applying context aware mechanism in order to control the robot manipulator which automatically sorts parcels. The context aware mechanism generates intelligent information to control a robot using context data such as the parcel shape, weight, location and barcodes. The proposed system collects context data of the parcel and generates robot control data to pick up and drop parcels. Then a robot manipulator, which receives control data of picking-up and dropping, processes the automated sorting of parcels according to delivery persons and delivery routes. It will contribute not only to save much time and cost but also to reduce the industrial accidents.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.481-484
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• 2010

In this paper, we implements the USN based context-aware vessel system for a group of ships which receives the specific sensor data that is the result of context-aware after collecting from each of the among a group of ships. The received date is shared and managed by monitoring system. If this monitoring system have trouble the sensor from any ship, the it's control was transferred from the main monitoring ship to the any ship and the second main ship collect the data then recognize the context-aware. Also, this system have the advantage of that reduce the load due to the sensor node communication because just collect the optional sensor data.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.3
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• pp.59-70
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• 2021

With the recent advancements in the Internet of Things, context-aware system that provides customized services become important to consider. The existing context-aware systems analyze data generated around the user and abstract the context information that expresses the state of situations. However, these datasets is mostly unstructured and have difficulty in processing with simple approaches. Therefore, providing context-aware services using the datasets should be managed in simplified method. One of examples that should be considered as the unstructured datasets is a deep learning application. Processes in deep learning applications have a strong coupling in a way of abstracting dataset from the acquisition to analysis phases, it has less flexible when the target analysis model or applications are modified in functional scalability. Therefore, an abstraction model that separates the phases and process the unstructured dataset for analysis is proposed. The proposed abstraction utilizes a description name Analysis Model Description Language(AMDL) to deploy the analysis phases by each fat client is a specifically designed instance for resource-oriented tasks in edge computing environments how to handle different analysis applications and its factors using the AMDL and Fat client profiles. The experiment shows functional scalability through examples of AMDL and Fat client profiles targeting a vehicle image recognition model for vehicle access control notification service, and conducts process-by-process monitoring for collection-preprocessing-analysis of unstructured data.