• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.1224-1227
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• 2017

포그 컴퓨팅 기술은 물리적 환경과 빈번하게 상호작용이 일어나는 사이버-물리 시스템에서 네트워크의 엣지에 있는 시스템이 컴퓨팅 작업을 수행하도록 함으로써 지역의 데이터를 실시간으로 수집하고 처리할 수 있다. 본 논문에서는 스쿨존내에서 안전을 높이기 위한 횡단보도의 신호등에 포그 컴퓨팅 기술을 적용한다. 신호등 시스템은 횡단보도에 접근하는 자동차를 인지하고, 위험 상황을 미리 방지하기 위해 자동차를 제어할 수 있다. 실험을 위해 사물인터넷 기술을 이용해 소형 테스트베드를 만들었으며, 신호 정보를 변화시키며 실험을 수행한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.162-169
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• 2022

In a dynamically changing time-varying network environment, the optimal moving pattern of edge devices can be applied to distributing computing resources to edge cloud servers or deploying new edge servers in the FEC(Fog/Edge Computing) environment. In addition, this can be used to build an environment capable of efficient computation offloading to alleviate latency problems, which are disadvantages of cloud computing. This paper proposes an algorithm to extract the optimal moving pattern by analyzing the moving path of multiple edge devices requiring application services in an arbitrary spatio-temporal environment based on frequency. A comparative experiment with A^* and Dijkstra algorithms shows that the proposed algorithm uses a relatively fast execution time and less memory, and extracts a more accurate optimal path. Furthermore, it was deduced from the comparison result with the A^* algorithm that applying weights (preference, congestion, etc.) simultaneously with frequency can increase path extraction accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.786-792
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• 2022

In the cloud computing environment, there has been a lot of research into the Fog/Edge Computing (FEC) paradigm for securing user proximity of application services and computation offloading to alleviate service delay difficulties. The method of predicting dynamic location change patterns of edge devices (moving objects) requesting application services is critical in this FEC environment for efficient computing resource distribution and deployment. This paper proposes an optimal moving pattern extraction algorithm in which variable weights (distance, time, congestion) are applied to selected paths in addition to a support factor threshold for frequency patterns (moving objects) of edge devices. The proposed algorithm is compared to the OPE_freq [8] algorithm, which just applies frequency, as well as the A^* and Dijkstra algorithms, and it can be shown that the execution time and number of nodes accessed are reduced, and a more accurate path is extracted through experiments.