• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.1-10
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• 2023

The Multi-access Edge Computing (MEC) paradigm equips network edge telecommunication infrastructure with cloud computing resources. It seeks to transform the edge into an IT services platform for hosting resource-intensive and delay-stringent services for mobile users, thereby significantly enhancing perceived service quality of experience. However, erratic user mobility impedes seamless service continuity as well as satisfying delay-stringent service requirements, especially as users roam farther away from the serving MEC resource, which deteriorates quality of experience. This work proposes a deep reinforcement learning based service mobility management approach for ensuring seamless migration of service instances along user mobility. The proposed approach focuses on the problem of selecting the optimal MEC resource to host services for high mobility users, thereby reducing service migration rejection rate and enhancing service availability. Efficacy of the proposed approach is confirmed through simulation experiments, where results show that on average, the proposed scheme reduces service delay by 8%, task computing time by 36%, and migration rejection rate by more than 90%, when comparing to a baseline scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5669-5684
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• 2018

The new mobile edge network architecture has been required for an increasing amount of traffic, quality requirements, advanced driver assistance system for autonomous driving and new cloud computing demands on highway. This article proposes a hierarchical cloud computing architecture to enhance performance by using adaptive data load distribution for buses that play the role of edge computing server. A vehicular dynamic cloud is based on wireless architecture including Wireless Local Area Network and Long Term Evolution Advanced communication is used for data transmission between moving buses and cars. The main advantages of the proposed architecture include both a reduction of data loading for top layer cloud server and effective data distribution on traffic jam highway where moving vehicles require video on demand (VOD) services from server. Through the description of real environment based on NS-2 network simulation, we conducted experiments to validate the proposed new architecture. Moreover, we show the feasibility and effectiveness for the connected car media service on highway.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1551-1558
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• 2022

We propose a two-tier cooperative computing system comprised of a mobile device and an edge server for partial offloading of multi-branch structures in Fog/Edge Computing environments in this paper. The proposed system includes an algorithm for splitting up application service processing by using reconstructive linearization techniques for multi-branch structures, as well as an optimal collaboration algorithm based on partial offloading between mobile device and edge server. Furthermore, we formulate computation offloading and CNN layer scheduling as latency minimization problems and simulate the effectiveness of the proposed system. As a result of the experiment, the proposed algorithm is suitable for both DAG and chain topology, adapts well to different network conditions, and provides efficient task processing strategies and processing time when compared to local or edge-only executions. Furthermore, the proposed system can be used to conduct research on the optimization of the model for the optimal execution of application services on mobile devices and the efficient distribution of edge resource workloads.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.383-403
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• 2021

With the development of mobile edge computing (MEC), some late-model application technologies, such as self-driving, augmented reality (AR) and traffic perception, emerge as the times require. Nevertheless, the high-latency and low-reliability of the traditional cloud computing solutions are difficult to meet the requirement of growing smart cars (SCs) with computing-intensive applications. Hence, this paper studies an efficient offloading decision and resource allocation scheme in collaborative vehicular edge computing networks with multiple SCs and multiple MEC servers to reduce latency. To solve this problem with effect, we propose a context-aware offloading strategy based on differential evolution algorithm (DE) by considering vehicle mobility, roadside units (RSUs) coverage, vehicle priority. On this basis, an autoregressive integrated moving average (ARIMA) model is employed to predict idle computing resources according to the base station traffic in different periods. Simulation results demonstrate that the practical performance of the context-aware vehicular task offloading (CAVTO) optimization scheme could reduce the system delay significantly.

• Journal of Convergence for Information Technology
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• v.10 no.11
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• pp.30-39
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• 2020

As 5G mobile communication services are provided, efforts are being made to provide various services to users with ultra-low latency. This raises interest in edge computing, which can provide high performance computing services near users instead of cloud computing at the network core. This paper presents an efficient operation and management scheme of a micro data center, which is an essential equipment for realizing edge computing. First, we present the functional structure and deployment plan of edge computing. Next, we present the requirements for the micro data centers for edge computing and the operation and management scheme accordingly. Finally, in order to efficiently manage resources in the micro data centers, we present resource management items to be collected and monitored, and propose a performance indicator to measure the energy efficiency.

• Electronics and Telecommunications Trends
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• v.37 no.1
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• pp.32-41
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• 2022

With the development of computing technology, the convergence of ICT with existing traditional industries is being attempted. In particular, with the recent advent of 5G, connectivity with numerous AuT (autonomous Things) in the real world as well as simple mobile terminals has increased. As more devices are deployed in the real world, the need for technology for devices to learn and act autonomously to communicate with humans has begun to emerge. This article introduces "Device to the Edge," a new computing paradigm that enables various devices in smart spaces (e.g., factories, metaverse, shipyards, and city centers) to perform ultra-reliable, low-latency and high-speed processing regardless of the limitations of capability and performance. The proposed technology, referred to as EdgeCPS, can link devices to augmented virtual resources of edge servers to support complex artificial intelligence tasks and ultra-proximity services from low-specification/low-resource devices to high-performance devices.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2167-2175
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• 2017

In recent years, an IoT is dramatically developing according to the enhancement of AI, the increase of connected devices, and the high-performance cloud systems. Huge data produced by many devices and sensors is expanding the scope of services, such as an intelligent diagnostics, a recommendation service, as well as a smart monitoring service. The studies of edge computing are limited as a role of small server system with high quality HW resources. However, there are specialized requirements in a smart factory domain needed edge computing. The edges are needed to pre-process containing tiny filtering, pre-formatting, as well as merging of group contexts and manage the regional rules. So, in this paper, we extract the features and requirements in a scope of efficiency and robustness. Our edge offers to decrease a network resource consumption and update rules and learning models. Moreover, we propose architecture of edge computing based on learning factors sharing with a cloud system in a smart factory.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.23-29
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• 2022

Recently, due to the increase in the use of Internet of Things (IoT) devices, the amount of data transmitted and processed to cloud computing servers has increased rapidly. As a result, network problems (delay, server overload and security threats) are emerging. In particular, edge computing with lower computational capabilities than cloud computing requires a lightweight authentication algorithm that can easily authenticate numerous IoT devices.In this paper, we proposed a key-agreement protocol of a lightweight algorithm that guarantees anonymity and forward and backward secrecy between IoT and edge devices. and the proposed algorithm is stable in MITM and replay attacks for edge device and IoT. As a result of comparing and analyzing the proposed key-agreement protocol with previous studies, it was shown that a lightweight protocol that can be efficiently used in IoT and edge devices.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10a
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• pp.221-222
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• 2007

• Journal of information and communication convergence engineering
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• v.18 no.3
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• pp.183-187
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• 2020

The general Wi-Fi network connection structure is that a number of IoT (Internet of Things) sensor nodes are directly connected to one AP (Access Point) node. In this structure, the range of the network that can be established within the specified specifications such as the range of signal strength (RSSI) to which the AP node can connect and the maximum connection capacity is limited. To overcome these limitations, multiple middleware bridge technologies for dynamic scalability and load balancing were studied. However, these network expansion technologies have difficulties in terms of the rules and conditions of AP nodes installed during the initial network deployment phase In this paper, an intelligent edge computing IoT device is developed for constructing an intelligent autonomous cluster edge computing network and applying it to real-time road danger context aware and notification system through an intelligent risk situation recognition algorithm.