Eui-nam Huh

Kyung Hee University · Department of Computer Engineering

Federated learning (FL) is a distributed training technology that enhances data privacy in mobile edge networks by allowing data owners to collaborate without transmitting raw data to the edge server. However, data heterogeneity and adversarial attacks pose challenges to develop an unbiased and robust global model for edge deployment. To address th...

Beyond the success of Contrastive Language-Image Pre-training (CLIP), recent trends mark a shift toward exploring the applicability of lightweight vision-language models for resource-constrained scenarios. These models often deliver suboptimal performance when relying solely on a single image-text contrastive learning objective, spotlighting the ne...

Cloud computing has recently gained popularity due to its cost-effective and high-quality services. Cloud-native systems are expected to host more than 95% of digital workloads. Cloud service providers face two significant challenges: real-time workload predictions and effective resource management. Furthermore, allocating resources over time may r...

In the era of industry 4.0 and the widespread use of digital devices, the number of cyber attacks poses an escalating and diverse threat, jeopardizing users' online activities. Intrusion detection systems (IDS) emerge as pivotal solutions, playing a crucial role in detecting anomalous signals within network systems. To counter novel attack patterns...

Sixth-generation (6G) networks leverage simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) to overcome the limitations of traditional RISs. STAR-RISs offer 360-degree full-space coverage and optimized transmission and reflection for enhanced network performance and dynamic control of the indoor propagation en...

The deployment of deep learning architectures on low-computational resource devices is challenging due to their high number of parameters and computational complexity. These heavy and complex architectures result in increased latency in real-time applications. However, splitting the deep architecture in a way that parallelizes the forward propagati...

Cloud computing has become the cornerstone of modern technology, propelling industries to unprecedented heights with its remarkable and recent advances. However, the fundamental challenge for cloud service providers is real-time workload prediction and management for optimal resource allocation. Cloud workloads are characterized by their heterogene...

The identification of suitable feature subsets from High-Dimensional Low-Sample-Size (HDLSS) data is of paramount importance because this dataset often contains numerous redundant and irrelevant features, leading to poor classification performance. However, the selection of an optimal feature subset from a vast feature space creates a significant c...

One of the primary objectives for future wireless communication networks is to facilitate the provision of ultra-reliable and low-latency communication services while simultaneously ensuring the capability for vast connection. In order to achieve this objective, we examine a hybrid multi-access scheme inside the finite blocklength (FBL) regime. Thi...

The coming 6G wireless communication system requires an intelligent networking system with higher network capacity. Therefore, we consider an intelligent omni surface (IOS)-assisted cell-free massive MIMO (ICFMM) system that extends the network coverage by providing services on both sides of the IOS with minimized power. The base stations and IOSs...

Convergence technologies including the Internet of Things, Big Data, and Artificial Intelligence can detect and respond to dangerous situations through real-time monitoring. Meanwhile, the cafeteria environment has required safety management as it is exposed to various accident risks. Therefore, we conducted a study that applied multimodal data and...

Recently, the contactless lifestyle has shifted to a new paradigm due to social issues including diseases and natural disasters caused by COVID-19. In this study, the core technology ‘MetaOps’ is derived from ‘Metaverse' and ‘Operation', which employed to data processing unit (DPU) in a core–edge distributed cloud environment that enables real-time...

Nowadays, salient object detection (SOD) has become a prominent research area in computer vision, which has various applications in real life. With the advancement of convolutional neural networks (CNN), numerous SOD methods have been proposed to imitate the human visual system to identify salient objects in images. Their marvelous performance come...

The Internet of Things (IoT) environment, which enables networking and computing in everything, is rapidly spreading. IoT environments cause bottlenecks and service delays to process data and provide services to users through a cloud-based central processing structure. In order to solve this problem, edge computing, which provides services to users...

The traditional client-based HTTP adaptation strategies do not explicitly coordinate between the clients, servers, and cellular networks. A lack of coordination leads to suboptimal user experience. In addition to optimizing Quality of Experience (QoE), other challenges in adapting HTTP adaptive streaming (HAS) to the cellular environment are overco...

The forthcoming 6G wireless communication systems are required to meet the increasing demand for network connectivity that requires power savings for generating effective beamforming. Therefore, joint sensing and communication framework is proposed with the coexistence between holographic MIMO (HMIMO) and Intelligent Omni-Surface (IOS) which ensure...

Sixth-generation (6G) communication networks will fulfill users’ requests for high data speeds and low latency without causing network outages throughout the world. However, marine communication in deep-sea waters is expanding as maritime traffic grows. To serve mission-critical applications, a growing number of maritime end-users require high thro...

The edge computing paradigm has emerged as a new scope within the domain of the Internet of Things (IoT) by bringing cloud services to the network edge in order to construct distributed architectures. To efficiently deploy latency-sensitive and bandwidth-hungry IoT application services, edge computing paradigms make use of devices on the network pe...

The development of salient object detection is crucial in ubiquitous applications. Existing state-of-the-art models tend to have complex designs and a significant number of parameters, prioritizing performance improvement over efficiency. Hence, there pose significant challenges to deploying them in edge devices. The intricacy in these models stems...

With the rapid development of IoT applications and multi-access edge computing (MEC) technology, massive amounts of sensing data can be collected and transmitted to MEC servers for rapid processing. On the other hand, as the number of IoT devices grows, the MEC server cannot perform tremendous computing tasks because of its limited computation capa...

p>The immediate adoption of deep learning models into domain-specific tasks for edge intelligence-based services still poses several challenges to overcome. The first is efficiently constructing the most suitable neural network architecture amongst the numerous types of available architectures. Once addressing this challenge, the second is understa...

p>The immediate adoption of deep learning models into domain-specific tasks for edge intelligence-based services still poses several challenges to overcome. The first is efficiently constructing the most suitable neural network architecture amongst the numerous types of available architectures. Once addressing this challenge, the second is understa...

p>The immediate adoption of deep learning models into domain-specific tasks for edge intelligence-based services still poses several challenges to overcome. The first is efficiently constructing the most suitable neural network architecture amongst the numerous types of available architectures. Once addressing this challenge, the second is understa...

Real-time moving object detection is an emerging method in Industry 5.0, that is applied in video surveillance, video coding, human-computer interaction, IoT, robotics, smart home, smart environment, edge and fog computing, cloud computing, and so on. One of the main issues is accurate moving object detection in real-time in a video with challengin...

Parked vehicle-assisted multi-access edge computing (PVMEC) is a paradigm that exploits the under-utilized resources of parked vehicles (PVs) to assist MEC servers for offloaded task execution. This article investigates a partial offloading strategy for multi-user PVMEC, where each mobile device (MD)’s task can be partially offloaded to the MEC ser...

In the era of heterogeneous 5G networks, Internet of Things (IoT) devices have significantly altered our daily life by providing innovative applications and services. However, these devices process large amounts of data traffic and their application requires an extremely fast response time and a massive amount of computational resources, leading to...

Vehicular edge computing (VEC) is one of the prominent ideas to enhance the computation and storage capabilities of vehicular networks (VNs) through task offloading. In VEC, the resource-constrained vehicles offload their computing tasks to the local road-side units (RSUs) for rapid computation. However, due to the high mobility of vehicles and the...

In virtual desktop infrastructure (VDI) environments, the remote display protocol has a big responsibility to transmit video data from a data center-hosted desktop to the endpoint. The protocol must ensure a high level of client perceived end-to-end quality of service (QoS) under heavy work load conditions. Each remote display protocol works differ...

The sheer unpredictability of content popularity, diversified user preferences and demands, and privacy concerns for data sharing all create hurdles to develop proactive content caching strategies in self-driving cars. Therefore, to address these concerns, we investigate in detail the role of proactive content caching methods in self-driving cars f...

Conventional cloud computing, where compute, storage, and networking resources reside in one or a few centralized data centers, has become unable to meet the stringent latency requirements of new applications. Along with that, the rapid development of 5 G network introduces the trend of network cloudification and network service provisioning accord...

In recent years, Knowledge Distillation has obtained a significant interest in deep learning-based applications on mobile and IoT devices due to its ability to transfer knowledge from the large and complex teacher to the lightweight student network. Intuitively, Knowledge Distillation refers to forcing the student to mimic the teacher’s neuron resp...

Large-scale IoT applications with dozens of thousands of geo-distributed IoT devices creating enormous volumes of data pose a big challenge for designing communication systems that provide data delivery with low latency and high scalability. In this paper, we investigate a hierarchical Edge-Cloud publish/subscribe brokers model using an efficient t...

Seamless streaming of high quality video under unstable network condition is a big challenge. HTTP adaptive streaming (HAS) provides a solution that adapts the video quality according to the network conditions. Traditionally, HAS algorithm runs at the client side while the clients are unaware of bottlenecks in the radio channel and competing client...

Online workload balancing guarantees that the incoming workloads are processed to the appropriate servers in real time without any knowledge of future resource requests. Currently, by matching the characteristics of incoming Internet of Things (IoT) applications to the current state of computing and networking resources, a mobile edge orchestrator...

Over the decades, robotics technology has acquired sufficient advancement through the progression of 5G Internet, Artificial Intelligence (AI), Internet of Things (IoT), Cloud, and Edge Computing. Though nowadays, Cobot and Service Oriented Architecture (SOA) supported robots with edge computing paradigms have achieved remarkable performances in di...

Video clients employ HTTP-based adaptive bitrate (ABR) algorithms to optimize users’ quality of experience (QoE). ABR algorithms adopt video quality based on the network conditions during playback. The existing state-of-the-art ABR algorithms ignore the fact that video streaming services deploy segment durations differently in different services, a...

Due to the budget and environmental issues, adaptive energy efficiency receives a lot of attention these days, especially for cloud computing. In the previous research, we developed a combined methodology based on nonparametric prediction and convex optimization to produce proactive energy efficiency-oriented solution. In this work, the predictive...

Multi-access edge computing (MEC) is a new leading technology for meeting the demands of key performance indicators (KPIs) in 5G networks. However, in a rapidly changing dynamic environment, it is hard to find the optimal target server for processing offloaded tasks because we do not know the end users’ demands in advance. Therefore, quality of ser...

With the increasing depth of deep learning neural network thus complexity to increase it's accuracy of different tasks(classification, recognition etc.) training time for deep neural networks is also increasing. Very high number of parameters make the deep learning heavy to run into mobile and stationary devices. Though having high performance comp...

Most of the recent methods are proposed for action recognition usually use two stream network. These two streams are used to predict individually by extracting RGB and Flow Features, final prediction is obtained after averaging these two predictions. In this work, We propose a novel online concatenation technique of RGB and RF feature spaces using...

Moving object detection is the detection of moving targets from an input video. Academy and industry have much interest in moving target detection. In the IoT era, moving object detection plays a key role in object recognition, and tracking for an autonomous car, military, smart home, smart city, health care, agriculture, etc. Additionally, moving...

Moving object detection has become a very emerging research area because of its broad range of applications. Some manual image processing-and deep learning-based methods have gained success in the moving object detection for the non-dynamic video. However, the approaches fail to detect moving objects for the dynamic video case such as dynamic backg...

In recent years, multi-access edge computing (MEC) is a key enabler for handling the massive expansion of Internet of Things (IoT) applications and services. However, energy consumption of a MEC network depends on volatile tasks that induces risk for energy demand estimations. As an energy supplier, a microgrid can facilitate seamless energy supply...

With the rapid increase in the development of the Internet of Things, a large amount of data are expected to be generated, which result to in increased latency. To reduce the latency, service placement method has been researched for resource management from mobile devices to nearby edge server. However, most of the related studies did not provide s...