• Convergence Security Journal
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• v.22 no.1
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• pp.71-79
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• 2022

The smart grid system has emerged to maximize energy efficiency through real-time information exchange between power providers and consumers by combining information technology and power supply systems. The authentication schemes using blockchain in a smart grid system have been proposed, which utilize an edge server's architecture to collect and store electric power-related information and process data between a central cloud server and smart grid-IoT devices. Although authentication schemes are being proposed to enhance security in the smart grid environment, many vulnerabilities are still reported. This paper presents a new mutual authentication scheme to guarantee users' privacy and anonymity in a smart grid based on edge computing using blockchain. In the proposed scheme, we use the smart contract for the key management's efficiency, such as updating and discarding key materials. Finally, we prove that the proposed scheme not only securely establishes a session key between the smart grid-IoT device of the user and the edge server but also guarantees anonymity.

• Electronics and Telecommunications Trends
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• v.35 no.6
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• pp.78-87
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• 2020

With the evolution of the Internet of Things (IoT), a computing paradigm shift from cloud to edge computing is rapidly taking place to effectively manage the rapidly increasing volume of data generated by various IoT devices. Edge computing is computing that occurs at or near the physical location of a user or data source. Placing computing services closer to these locations allows users to benefit from faster and more reliable services, and enterprises can take advantage of the flexibility of hybrid cloud computing. This paper describes the concept and main benefits of edge computing and presents the trends and future prospects for edge computing technology.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.3
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• pp.9-15
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• 2021

In this paper, the Embedded MEMS system to the power apparatus used Edge Impulse machine learning tools and therefore an improved predictive system design is implemented. The proposed MEMS embedded system is developed based on nRF52840 system and the sensor with 3-Axis Digital Magnetometer, I2C interface and magnetic measurable range ±120 uT, BM1422AGMV which incorporates magneto impedance elements to detect magnetic field and the ARM M4 32-bit processor controller circuit in a small package. The MEMS embedded platform is consisted with Edge Impulse Machine Learning and system driver implementation between hardware and software drivers using SensorQ which is special queue including user application temporary sensor data. In this paper by experimenting, TensorFlow machine learning training output is applied to the power apparatus for analyzing the status such as "Normal, Warning, Hazard" and predicting the performance at level of 99.6% accuracy and 0.01 loss.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.6
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• pp.259-266
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• 2021

Edge Computing enables real-time big data processing by performing computing close to the physical location of the user or data source. However, in an edge computing environment, various situations that affect big data processing performance may occur depending on temporary service requirements or changes of physical resources in the field. In this paper, we proposed a BigCrawler system that dynamically configures the computing module and storage module according to the big data collection status and computing resource usage status in the edge computing environment. And the feature of big data processing workload according to the arrangement of computing module and storage module were analyzed.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.450-464
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• 2023

To address the problems of large system overhead and low timeliness when dealing with task scheduling in mobile edge cloud computing, a task scheduling and resource management strategy for edge cloud computing based on an improved genetic algorithm was proposed. First, a user task scheduling system model based on edge cloud computing was constructed using the Shannon theorem, including calculation, communication, and network models. In addition, a multi-objective optimization model, including delay and energy consumption, was constructed to minimize the sum of two weights. Finally, the selection, crossover, and mutation operations of the genetic algorithm were improved using the best reservation selection algorithm and normal distribution crossover operator. Furthermore, an improved legacy algorithm was selected to deal with the multi-objective problem and acquire the optimal solution, that is, the best computing task scheduling scheme. The experimental analysis of the proposed strategy based on the MATLAB simulation platform shows that its energy loss does not exceed 50 J, and the time delay is 23.2 ms, which are better than those of other comparison strategies.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.283-289
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• 2006

The present image segmentation is what user want to segment image and has been studied for technology in composition of segment object with other images. In this paper, we propose a method of novel semi-automatic image segmentation using gradual region merging and genetic algorithm. Proposed algorithm is edge detection of object using genetic algorithm after selecting object which user want. We segment region of object which user want to based on detection edge using watershed algorithm. We separated background and object in indefinite region using gradual region merge from Segment object. And, we have applicable value which user want by making interface based on GUI for efficient perform of algorithm development. In the experiments, we analyzed various images for proving superiority of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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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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• v.15 no.11
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• pp.3892-3912
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• 2021

The recent rapid growth of mobile network traffic places multi-access edge computing in an important position to reduce network load and improve network capacity and service quality. Contrasting with traditional mobile cloud computing, multi-access edge computing includes a base station cooperative cache layer and user cooperative cache layer. Selecting the most appropriate cache content according to actual needs and determining the most appropriate location to optimize the cache performance have emerged as serious issues in multi-access edge computing that must be solved urgently. For this reason, a cache placement algorithm based on comprehensive utility in big data multi-access edge computing (CPBCU) is proposed in this work. Firstly, the cache value generated by cache placement is calculated using the cache capacity, data popularity, and node replacement rate. Secondly, the cache placement problem is then modeled according to the cache value, data object acquisition, and replacement cost. The cache placement model is then transformed into a combinatorial optimization problem and the cache objects are placed on the appropriate data nodes using tabu search algorithm. Finally, to verify the feasibility and effectiveness of the algorithm, a multi-access edge computing experimental environment is built. Experimental results show that CPBCU provides a significant improvement in cache service rate, data response time, and replacement number compared with other cache placement algorithms.

• Convergence Security Journal
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• v.21 no.3
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• pp.25-30
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• 2021

Due to the current COVID-19 pandemic, companies and institutions are introducing virtual desktop technology, one of the logical network separation technologies, to establish a safe working environment in a situation where remote work is provided. With the introduction of virtual desktop technology, companies and institutions can operate the network separation environment more safely and effectively, and can access the business network quickly and safely to increase work efficiency and productivity. However, when introducing virtual desktop technology, there is a cost problem of high-spec server, storage, and license, and it is necessary to supplement in terms of operation and management. As a countermeasure to this, companies and institutions are shifting to cloud computing-based technology, virtual desktop service (DaaS, Desktop as a Service). However, in the virtual desktop service, which is a cloud computing-based technology, the shared responsibility model is responsible for user access control and data security. In this paper, based on the shared responsibility model in the virtual desktop service environment, we propose a cloud-based hardware security module (Cloud HSM) and edge-DRM proxy as an improvement method for user access control and data security.

• Korean Journal of Environment and Ecology
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• v.4 no.1
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• pp.63-68
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• 1990

User's impacts on trail deteriorations and edge vegetation were studied in Sokri Mountain National Park in 1990. The entire width, bare width and maximum depth of trail as the trail condition were significantly greater on the more heavily used trail. Deteriorations of trail which were surveyed at the total of 52 were significantly different from those of non-deteriorated points. The dominant trees in the crown layer of trail edge are as follows. According as the altitude rises, the changes happen from Quercus serrata to Quercus variabilis, Pinus densiflora, and to Quercus mongolica at Joongsajaam course, and from Pinus densiflora, Quercus variabilis to Quercus mongolica at Birosanjang course. And Stephanandra incisa, Symplocos chinesis for. pilosa show high relative dominant value at Joongsajaam course, Lindera obtusiloba, Fraxinus sieboldiana show one at Birosanjang course.