• 한국전자파학회지:전자파기술
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• 제28권5호
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• pp.10-16
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• 2017

• 한국전자파학회지:전자파기술
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• 제30권3호
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• pp.24-33
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• 2019

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제10권11호
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• pp.291-296
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• 2021

사물인터넷의 발전으로 인하여 수많은 디바이스가 생겨나고, 큰 계산 자원을 요구하는 태스크들이 많이 발생된다. 이런 사물인터넷 환경에서 Mobile Edge Computing(MEC)는 지리적으로 사용자와 근접하여 서비스를 제공하기 때문에 많은 주목을 받고 있다. MEC 서버로의 태스크 오프로딩은 제한된 배터리 수명과 계산 능력을 갖고 있는 디바이스에게 효율적이다. 본 연구는 높은 신뢰도를 요구하는 산업용 IoT 환경을 가정하였다. 많은 디바이스와 여러 MEC 서버와 같은 환경으로 최적화에 있어서 복잡성이 발생한다. 이를 해결하기 위해 문제를 두 개로 나눠 해결한다. MEC 서버의 큐 상태를 고려하여 큐의 제한 길이를 충족하는 MEC 서버를 선택한 뒤, 유전 알고리즘을 사용하여 신뢰도를 고려하면서도 에너지 소모량을 최적화하는 오프로딩 결정 알고리즘을 제시한다. 본 연구는 실험을 통하여 에너지 소모량과 신뢰성 측면에서 제안 알고리즘의 성능이 효율적임을 분석하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권1호
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• pp.69-85
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• 2019

BeiDou navigation satellite system (BDS) is one of the four main types of global navigation satellite systems. The current system has been widely used by the military and by the aerospace, transportation, and marine fields, among others. However, challenges still remain in the BeiDou system, which requires rapid responses for delay-sensitive devices. A differential positioning algorithm called the data center-based differential positioning (DCDP) method is widely used to avoid the influence of errors. In this method, the positioning information of multiple base stations is uploaded to the data center, and the positioning errors are calculated uniformly by the data center based on the minimum variance or a weighted average algorithm. However, the DCDP method has high delay and overload risk. To solve these problems, this paper introduces edge computing to relieve pressure on the data center. Instead of transmitting the positioning information to the data center, a novel method called edge computing-based differential positioning (ECDP) chooses the nearest reference station to perform edge computing and transmits the difference value to the mobile receiver directly. Simulation results and experiments demonstrate that the performance of the ECDP outperforms that of the DCDP method. The delay of the ECDP method is about 500ms less than that of the DCDP method. Moreover, in the range of allowable burst error, the median of the positioning accuracy of the ECDP method is 0.7923m while that of the DCDP method is 0.8028m.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.689-692
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• 2021

본 논문은 Fog/Edge Computing(FEC) 환경에서 애플리케이션 서비스의 사용자 근접성을 지원하는 컴퓨팅 리소스의 최적 할당 및 지연시간 감소를 위한 이동에이전트 기반 오프로딩의 효율성을 제고하기 위한 선행연구로써, 이동객체들의 방대한 시공간 이동 이력데이터 집합으로부터 복합적인 시공간 제약을 적용한 최적 이동패턴 추출 시 수행시간 및 소요 메모리양을 효과적으로 감소시키는 알고리즘을 제안한다. 제안 알고리즘은 빈발도 기반의 최적경로 추출을 통하여 향후 FEC 환경에서 계산 오프로딩을 위한 컴퓨팅 리소스의 분배 및 배치에 유용하게 사용될 수 있다.

• Journal of Information Processing Systems
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• 제18권3호
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• pp.443-456
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• 2022

An efficient and reasonable resource allocation strategy can greatly improve the service quality of Internet of Vehicles (IoV). However, most of the current allocation methods have overestimation problem, and it is difficult to provide high-performance IoV network services. To solve this problem, this paper proposes a network resource allocation strategy based on deep learning network model DDQN. Firstly, the method implements the refined modeling of IoV model, including communication model, user layer computing model, edge layer offloading model, mobile model, etc., similar to the actual complex IoV application scenario. Then, the DDQN network model is used to calculate and solve the mathematical model of resource allocation. By decoupling the selection of target Q value action and the calculation of target Q value, the phenomenon of overestimation is avoided. It can provide higher-quality network services and ensure superior computing and processing performance in actual complex scenarios. Finally, simulation results show that the proposed method can maintain the network delay within 65 ms and show excellent network performance in high concurrency and complex scenes with task data volume of 500 kbits.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권8호
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• pp.2490-2506
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• 2022

In Mobile Edge Computing (MEC), attackers can speculate and mine sensitive user information by eavesdropping wireless channel status and offloading usage pattern, leading to user privacy leakage. To solve this problem, this paper proposes a Privacy-preserving and Energy-efficient Offloading Algorithm (PEOA) based on Lyapunov optimization. In this method, a continuous Markov process offloading model with a buffer queue strategy is built first. Then the amount of privacy of offloading usage pattern in wireless channel is defined. Finally, by introducing the Lyapunov optimization, the problem of minimum average energy consumption in continuous state transition process with privacy constraints in the infinite time domain is transformed into the minimum value problem of each timeslot, which reduces the complexity of algorithms and helps obtain the optimal solution while maintaining low energy consumption. The experimental results show that, compared with other methods, PEOA can maintain the amount of privacy accumulation in the system near zero, while sustaining low average energy consumption costs. This makes it difficult for attackers to infer sensitive user information through offloading usage patterns, thus effectively protecting user privacy and safety.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.3043-3067
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• 2022

With the mobile traffic in the network increases exponentially, multi-access edge computing (MEC) develops rapidly. MEC servers are deployed geo-distribution, which serve many mobile terminals locally to improve users' QoE (Quality of Experience). When the cache space of a MEC server is full, how to replace the cached videos is an important problem. The problem is also called the cache replacement problem, which becomes more complex due to the dynamic video popularity and the varied video sizes. Therefore, we proposed a new cache replacement scheme based on local video popularity and video size to solve the cache replacement problem of MEC servers. First, we built a local video popularity model, which is composed of a popularity rise model and a popularity attenuation model. Furthermore, the popularity attenuation model incorporates a frequency-dependent attenuation model and a frequency-independent attenuation model. Second, we formulated a utility based on local video popularity and video size. Moreover, the weights of local video popularity and video size were quantitatively analyzed by using the information entropy. Finally, we conducted extensive simulation experiments based on the proposed scheme and some compared schemes. The simulation results showed that our proposed scheme performs better than the compared schemes in terms of hit rate, average delay, and server load under different network configurations.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2023년도 봄 학술논문 발표대회
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• pp.381-382
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• 2023

Frequent accidents occur when workers at construction sites leave the safety zone, and particularly in the past 5 years, 9 fatal accidents occurred at the Korea Railroad Corporation due to train accidents on other tracks during track work. With the Severe Accident Punishment Act taking effect in January 2022, it is a priority to secure a safe work environment for workers at industrial (construction) sites. Therefore, there is a need to manage workers' departure from the safety zone (construction zone) and to facilitate communication within the construction zone. In this study, a mobile edge computing CCTV system is proposed that uses geofencing to determine whether workers are working in the danger zone, which can judge and respond in real-time to the ever-changing field environment. The proposed system is mobile and flexible, rather than server-based fixed CCTV. However, since it is designed mainly based on images, it has limitations in recognition rate depending on the environment such as distance, viewing angle, and illumination. As a way to compensate for this, it is required to develop more reliable equipment by combining technologies such as LiDAR and Radar.

• ETRI Journal
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• 제42권5호
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• pp.721-733
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• 2020

Recently, there is an increasing demand for ultra-low-latency (ULL) services such as factory automation, autonomous driving, and telesurgery that must meet an end-to-end latency of less than 10 ms. Fifth-generation (5G) New Radio guarantees 0.5 ms one-way latency, so the feasibility of ULL services is higher than in previous mobile communications. However, this feasibility ensures performance at the radio access network level and requires an innovative 5G network architecture for end-to-end ULL across the entire 5G system. Hence, we survey in detailed two the 3rd Generation Partnership Party (3GPP) standardization activities to ensure low latency at network level. 3GPP standardizes mobile edge computing (MEC), a low-latency solution at the edge network, in Release 15/16 and is standardizing time-sensitive communication in Release 16/17 for interworking 5G systems and IEEE 802.1 time-sensitive networking (TSN), a next-generation industry technology for ensuring low/deterministic latency. We developed a 5G system based on 3GPP Release 15 to support MEC with a potential sub-10 ms end-to-end latency in the edge network. In the near future, to provide ULL services in the external network of a 5G system, we suggest a 5G-IEEE TSN interworking system based on 3GPP Release 16/17 that meets an end-to-end latency of 2 ms.