Choong Seon Hong

Choong Seon Hong
Kyung Hee University · Department of Computer Science and Engineering

Keio University

About

1,000
Publications
156,785
Reads
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14,827
Citations
Citations since 2016
475 Research Items
12268 Citations
201620172018201920202021202205001,0001,5002,0002,5003,000
201620172018201920202021202205001,0001,5002,0002,5003,000
201620172018201920202021202205001,0001,5002,0002,5003,000
201620172018201920202021202205001,0001,5002,0002,5003,000

Publications

Publications (1,000)
Preprint
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...
Preprint
Today's wireless systems are posing key challenges in terms of quality of service and quality of physical experience. Metaverse has the potential to reshape, transform, and add innovations to the existing wireless systems. A metaverse is a collective virtual open space that can enable wireless systems using digital twins, digital avatars, and inter...
Preprint
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...
Preprint
In contrast to centralized model training that involves data collection, federated learning (FL) enables remote clients to collaboratively train a model without exposing their private data. However, model performance usually degrades in FL due to the heterogeneous data generated by clients of diverse characteristics. One promising strategy to maint...
Preprint
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...
Preprint
Full-text available
Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks. A reliable XAI twin system for ZSM requires two composites: an extreme analytical ability for discretizing the physical behavior of the Internet of Everything (IoE) and...
Article
The recent trend towards the smart transportation system has spurred the development of the smart railway. However, enabling trains with seamless wireless connectivity using the roadside units (RSUs) is extremely challenging, mostly due to the lack of line of sight link. To address this issue, we propose a novel framework that uses intelligent refl...
Conference Paper
In this era of sophisticated technology for smart cities, when communication between smart things is crucial, Internet of Everything (IoE) networks play a key role in merging the cyber and physical worlds. IoE networks are used in a range of applications, including smart agriculture, smart housing, and smart medical services, thanks to the implemen...
Conference Paper
Fifth-generation (5G) networks use millimeter-wave (mmWave) technology to process high-speed and capacity data services. However, wireless communication losses occur due to mmWave limitations, i.e., penetration, rain attenuation, and coverage range. Furthermore, many base stations (BSs) are needed to support stable wireless communications and overc...
Preprint
THz band communication technology will be used in the 6G networks to enable high-speed and high-capacity data service demands. However, THz-communication losses arise owing to limitations, i.e., molecular absorption, rain attenuation, and coverage range. Furthermore, to maintain steady THz-communications and overcome coverage distances in rural and...
Preprint
Full-text available
Combination of the industrial Internet of Things (IIoT) and federated learning (FL) is deemed as a promising solution to realizing Industry 4.0 and beyond. In this paper, we focus on a hierarchical collaborative FL architecture over the IIoT systems, where the three-layer architectural design is conceived for supporting the training process. To eff...
Article
Federated learning and analytics are proposed to collaboratively learn models or statistics from decentralized data. However, the accuracy of global models relies heavily on the participation of data owners. Furthermore, for a multi-task system, data owners need to allocate limited computation resources efficiently among different local tasks. In t...
Conference Paper
Internet of Everything (IoE) plays a significant role in integrating the cyber and physical worlds in this period of advanced technology, where communication between physical objects is critical. Through the deployment of smart sensor networks (SSN), IoE is employed in a variety of applications such as agriculture, smart homes, and smart cities. Ho...
Preprint
Full-text available
In recent years, unmanned aerial vehicles (UAVs) assisted mobile edge computing systems have been exploited by researchers as a promising solution for providing computation services to mobile users outside of terrestrial infrastructure coverage. However, it remains challenging for the standalone MEC-enabled UAVs in order to meet the computation req...
Article
In this article, the design of a rational decision support system (RDSS) for a connected and autonomous vehicle charging infrastructure (CAV-CI) is studied. In the considered CAV-CI, the distribution system operator (DSO) deploys electric vehicle supply equipment (EVSE) to provide an electrical vehicle (EV) charging facility for human-driven connec...
Preprint
Emerging intelligent transportation applications, such as accident reporting, lane change assistance, collision avoidance, and infotainment, will be based on diverse requirements (e.g., latency, reliability, quality of physical experience). To fulfill such requirements, there is a significant need to deploy a digital twin-based intelligent transpor...
Article
Unmanned aerial vehicles (UAVs) are widely deployed to enhance the wireless network capacity and to provide communication services to mobile users beyond the infrastructure coverage. Recently, with the help of a promising technology called network virtualization, multiple service providers (SPs) can share the infrastructures and wireless resources...
Article
Recently, unmanned aerial vehicles (UAVs) assisted multi-access edge computing (MEC) systems emerged as a promising solution for providing computation services to mobile users outside of terrestrial infrastructure coverage. As each UAV operates independently, however, it is challenging to meet the computation demands of the mobile users due to the...
Preprint
Full-text available
In the realm of wireless communications in 5G, 6G and beyond, deploying unmanned aerial vehicle (UAV) has been an innovative approach to extend the coverage area due to its easy deployment. Moreover, reconfigurable intelligent surface (RIS) has also emerged as a new paradigm with the goals of enhancing the average sum-rate as well as energy efficie...
Conference Paper
Full-text available
In moving edge computing, infotainment content caching is a potential strategy to lessen the ever-increasing data supply in wireless networks. Because caches have a limited capacity, it is essential to proactively anticipate content similarity and cache the most similar content in a local cache server. In this study, we consider moving vehicles as...
Conference Paper
Full-text available
In this study, we propose a holography assisted THz single-cell massive multiple-input multiple-output (mMIMO) system that deals with the target oriented communication resource allocation. We propose a mechanism that can minimize the number of active grids from the holographic grid arrays (HGA) for ensuring the requirement of lower power towards th...
Preprint
Full-text available
Recently, significant research efforts have been initiated to enable the next-generation, namely, the sixth-generation (6G) wireless systems. In this article, we present a vision of metaverse towards effectively enabling the development of 6G wireless systems. A metaverse will use virtual representation (e.g., digital twin), digital avatars, and in...
Chapter
Self-driving cars have shown an immense interest from both academia and industry due to their wide range of features. These features are infotainment, collision avoidance alerts, driving with minimum possible user intervention, lane changing guidance for minimizing congestion, and accident reporting, among others. To enable these features, there is...
Conference Paper
Full-text available
Next-generation networks need to meet ubiquitous and high data-rate demand. Therefore, this paper considers the throughput and trajectory optimization of terahertz (THz)-enabled unmanned aerial vehicles (UAVs) in the sixth-generation (6G) communication networks. In the considered scenario, multiple UAVs must provide on-demand terabits per second (T...
Article
Full-text available
Emerging cross-device artificial intelligence (AI) applications require a transition from conventional centralized learning systems toward large-scale distributed AI systems that can collaboratively perform complex learning tasks. In this regard, democratized learning (Dem-AI) lays out a holistic philosophy with underlying principles for building l...
Conference Paper
Full-text available
Terahertz (THz) communication has the promise of enabling ultra-high data speeds in the sixth-generation (6G) wireless networks. Meanwhile, an intelligent reflecting surface (IRS) may influence incident electromagnetic wave propagation by changing the phase shifts with passive reflecting components. It can enhance spectrum efficiency and coverage c...
Preprint
Full-text available
In a practical setting towards better generalization abilities of client models for realizing robust personalized Federated Learning (FL) systems, efficient model aggregation methods have been considered as a critical research objective. It is a challenging issue due to the consequences of non-i.i.d. properties of client's data, often referred to a...
Article
Recently, the deployment of electric vehicles supply equipment (EVSE) and its market is expanding rapidly to support the massive penetration of electric vehicles (EVs). However, to accomplish an effective EV charging mechanism for urban prosumer communities, it is imperative to tackle the challenges of distinct energy generation among the communiti...
Article
Full-text available
Desktop clouds connect several desktop computers into a cloud computing architecture to reap the potential of untapped commodity computing power over the Internet. In desktop clouds, what benefit (incentive) a participant will get for sharing its computational resources, and how participants will contribute (pay) after consuming computational resou...
Article
Industrial processes rely on sensory data for decision-making processes, risk assessment, and performance evaluation. Extracting actionable insights from the collected data calls for an infrastructure that can ensure the dissemination of trustworthy data. For the physical data to be trustworthy, it needs to be cross-validated through multiple senso...
Preprint
Next-generation networks need to meet ubiquitous and high data-rate demand. Therefore, this paper considers the throughput and trajectory optimization of terahertz (THz)-enabled unmanned aerial vehicles (UAVs) in the sixth-generation (6G) communication networks. In the considered scenario, multiple UAVs must provide on-demand terabits per second (T...
Preprint
Non-terrestrial networks (NTN), encompassing space and air platforms, are a key component of the upcoming sixth-generation (6G) cellular network. Meanwhile, maritime network traffic has grown significantly in recent years due to sea transportation used for national defense, research, recreational activities, domestic and international trade. In thi...
Preprint
Full-text available
Future wireless services must be focused on improving the quality of life by enabling various applications, such as extended reality, brain-computer interaction, and healthcare. These applications have diverse performance requirements (e.g., user-defined quality of experience metrics, latency, and reliability) that are challenging to be fulfilled b...
Preprint
Full-text available
One of the core envisions of the sixth-generation (6G) wireless networks is to accumulate artificial intelligence (AI) for autonomous controlling of the Internet of Everything (IoE). Particularly, the quality of IoE services delivery must be maintained by analyzing contextual metrics of IoE such as people, data, process, and things. However, the ch...
Preprint
Non-terrestrial networks (NTNs), which integrate space and aerial networks with terrestrial systems, are a key area in the emerging sixth-generation (6G) wireless networks. As part of 6G, NTNs must provide pervasive connectivity to a wide range of devices, including smartphones, vehicles, sensors, robots, and maritime users. However, due to the hig...
Conference Paper
Full-text available
The growing global populations, particularly in major cities, have created new problems, notably in terms of public safety regulation and optimization. As a result, in this paper, a strategy is provided for predicting crime occurrences in a city based on historical events and demographic observation. In particular, this study proposes a crime predi...
Article
Internet of Everything (IoE) applications such as haptics, human-computer interaction, and extended reality, using the sixth-generation (6G) of wireless systems have diverse requirements in terms of latency, reliability, data rate, and user-defined performance metrics. Therefore, enabling IoE applications over 6G requires a new framework that can b...
Article
Sixth-Generation (6G)-based Internet of Everything applications (e.g. autonomous driving cars) have witnessed a remarkable interest. Autonomous driving cars using federated learning (FL) has the ability to enable different smart services. Although FL implements distributed machine learning model training without the requirement to move the data of...
Article
Future wireless services will focus on improving the quality of life by enabling various applications, such as extended reality, brain-computer interaction, and healthcare. These applications will have diverse performance requirements (e.g., user-defined quality of experience metrics, latency, and reliability) which will be challenging to be fulfil...
Article
Full-text available
With the increasing number of electric vehicles, deploying fixed charging stations (FCSs) has been a widely adopted solution for providing charging services to EVs. However, the charging requirement of EVs being near overload FCSs and/or in areas of inadequate charging infrastructures such as highways and rural areas will surpass the capabilities o...
Article
Full-text available
Future generation of Electric Vehicles (EVs) equipped with modern technologies will impose a significant burden on computation and communication to the network due to the vast extension of onboard infotainment services. To overcome this challenge, multi-access edge computing (MEC) or Fog Computing can be employed. However, the massive adoption of n...
Article
Non-terrestrial networks (NTNs), which integrate space and aerial networks with terrestrial systems, are a key area in the emerging sixth-generation (6G) wireless networks. As part of 6G, NTNs must provide pervasive connectivity to a wide range of devices, including smartphones, vehicles, sensors, robots, and maritime users. However, due to the hig...
Conference Paper
Full-text available
Unmanned aerial vehicles (UAVs) are an indispensable component of future wireless networks. UAV can not only provide edge-computing services but also act as a router to the backhaul for the user devices. In this paper, we considered the problem of allocating communication and computing resources to UEs via UAV as a multi-dimensional knapsack proble...
Conference Paper
Full-text available
The objective of future sixth-generation (6G) networks is to provide global access to communication systems. Terrestrial networks are expanding in metropolitan areas, but there is no well-established distant user data traffic transmission infrastructure at the moment (i.e., users who are unable to access terrestrial networks). Our proposal investig...
Conference Paper
Non-terrestrial networks (NTN), encompassing space and air platforms, are a key component of the upcoming sixth-generation (6G) cellular network. Meanwhile, maritime network traffic has grown significantly in recent years due to sea transportation used for national defense, research, recreational activities, domestic and international trade. In thi...
Article
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...
Article
Full-text available
Federated Learning (FL) relies on on-device training to avoid the migration of devices’ data to a centralized server to address privacy leakage. Moreover, FL is feasible for scenarios (e.g., autonomous cars) where an enormous amount of data is generated every day. Transferring only local model updates in the case of FL is highly communication-effic...
Article
The ongoing deployments of the Internet of Things (IoT)-based smart applications are spurring the adoption of machine learning as a key technology enabler. To overcome the privacy and overhead challenges of centralized machine learning, there has been significant recent interest in the concept of federated learning. Federated learning offers on-dev...
Article
Mobile Network Operators (MNO) can reduce their Capital and Operational Expenditure (CAPEX) and (OPEX) with the help of tower sharing approach by utilizing the physical infrastructure equipped by a third party tower provider to expand their network coverage. Moreover, Computing Resource Providers (CRP) are also setting up their micro-datacenters at...
Conference Paper
Full-text available
The recent flourish of diversified distributed energy resources (DERs) such as generators, consumers, and prosumers brings indispensable cybersecurity challenges for the smart grid controller. Therefore, to assure a resilient smart grid operation, in this paper, we study the problem of continuous-time consensus policy-based DERs control mechanism f...
Conference Paper
Multi-access edge computing (MEC) is witnessed to be an integral part of emerging augmented reality (AR) / virtual reality (VR) applications. These applications require contents from the cloud, thus suffer from high latency that is not desirable. To address this issue, one can store the frequently requested content at the MEC server. However, MEC s...
Article
Sharding is a promising solution to achieving scalability within the blockchain network. A sharded blockchain network consists of a beacon chain and several committees powered by the participants (i.e., validators) through the Proof-of-Stake (PoS) consensus protocol. Efficient and scalable as it can be, the sharded blockchain based on PoS is vulner...
Article
In this paper, a novel framework for guaranteeing ultra-reliable millimeter wave (mmW) communications using multiple artificial intelligence (AI)-enabled reconfigurable intelligent surfaces (RISs) is proposed. The use of multiple AI-powered RISs allows changing the propagation direction of the signals transmitted from a mmW access point (AP) thereb...
Chapter
In this chapter, we discuss the role of federated learning for vehicular networks. Due to the high mobility of autonomous cars, there might not be seamless connectivity of the end-devices within cars with the roadside units, and thus traditional federated learning might not work well. To overcome this challenge, we introduced a dispersed federated...
Chapter
This chapter provides an overview of various resources such as computational, communication, and power resources, required for wireless federated learning. We perform convergence analysis of wireless federated learning. Additionally, joint resource and power allocation for wireless federated learning are proposed. Finally, we present a collaborativ...
Chapter
In this chapter, we discuss various components of federated learning that must be given some incentive in terms of monetary cost or other benefits. We design incentive mechanism design for federated learning using game theory and auction theory. Finally, we present extensive numerical results to show the validity of our proposed incentive mechanism...
Chapter
In this chapter, we consider unsupervised learning tasks being implemented within the federated learning framework to satisfy stringent requirements for low-latency and privacy of the emerging applications. The discussed algorithm is based on Dual Averaging (DA), where the gradients of each agent are aggregated at a central node. While having its a...
Chapter
This chapter introduce the use of federated learning (FL) for wireless virtual reality (VR) applications. In particular, we first explain why we use to use FL for wireless VR applications. Then, we provide a detailed literature review of using FL for VR applications. We then introduce a representative work that focuses on the use of FL for the anal...
Chapter
In this chapter, we provide an overview of the fundamentals of FL. First, we discuss a brief history of machine learning. Second, we present the key design challenges for FL over wireless networks. Next, we critically discuss the key design aspects, such as resource allocation, incentive mechanism design, security, and privacy of FL over wireless n...
Chapter
Federated learning allows data to be locally trained in their device and only send model updates to the central server for aggregation. But the security of model updates in the aggregation should also be carefully addressed. Existing works mainly focus on secure multiparty computation or differential privacy, which depends on heavy encryption or br...
Chapter
In this chapter, we present several Internet of Things applications that can leverage federated learning. More specifically, we introduce two applications such as smart industry and intelligent reflecting surfaces that can be effectively enabled by federated learning with many advantages compared to centralized machine learning. For both applicatio...
Preprint
Full-text available
In this paper, the design of a rational decision support system (RDSS) for a connected and autonomous vehicle charging infrastructure (CAV-CI) is studied. In the considered CAV-CI, the distribution system operator (DSO) deploys electric vehicle supply equipment (EVSE) to provide an EV charging facility for human-driven connected vehicles (CVs) and...
Preprint
Full-text available
Recently, unmanned aerial vehicles (UAVs) assisted multi-access edge computing (MEC) systems emerged as a promising solution for providing computation services to mobile users outside of terrestrial infrastructure coverage. As each UAV operates independently, however, it is challenging to meet the computation demands of the mobile users due to the...
Preprint
Full-text available
Unmanned aerial vehicles (UAVs) are widely deployed to enhance the wireless network capacity and to provide communication services to mobile users beyond the infrastructure coverage. Recently, with the help of a promising technology called network virtualization, multiple service providers (SPs) can share the infrastructures and wireless resources...
Preprint
Full-text available
The recent trend towards the high-speed transportation system has spurred the development of high-speed trains (HSTs). However, enabling HST users with seamless wireless connectivity using the roadside units (RSUs) is extremely challenging, mostly due to the lack of line of sight link. To address this issue, we propose a novel framework that uses i...
Preprint
In this letter, a novel framework is proposed for analyzing data offloading in a multi-access edge computing system. Specifically, a two-phase algorithm, is proposed, including two key phases: \emph{1) user association phase} and \emph{2) task offloading phase}. In the first phase, a ruin theory-based approach is developed to obtain the users assoc...
Conference Paper
Full-text available
In this paper, we study the problem of energy market regulation decisions at the aggregator in a smart grid framework, in which the dynamics of the average age of information (AAoI) for regulatory status data of distributed energy resources (DERs) are considered. In particular, we capture the dynamics of the AAoI for each regulatory status of DERs...
Conference Paper
Full-text available
Transportation mode detection (TMD) by analyzing smartphones embedded sensors' data is an emerging application for mobility-awareness services at the government or individual level. Split Learning is a relatively new privacy-preserving distributed machine learning technique. In vanilla split learning, a neural network is vertically distributed betw...
Conference Paper
Full-text available
Smart gadgets are increasing with the development of sixth-generation (6G) mobile communication systems. Similarly, the number of internet of maritime things (IoMT) devices are also increasing due to the rapid development in maritime activities for trade, research, defense, and recreation. To fulfill the demand for IoMT devices at the beach and sea...
Article
Edge Intelligence based on federated learning (FL) can be considered to be a promising paradigm for many emerging, strict latency Internet of Things (IoT) applications. Furthermore, a rapid upsurge in the number of IoT devices is expected in the foreseeable future. Although FL enables privacy-preserving, on-device machine learning, it still exhibit...
Article
The Internet of Things (IoT) will be ripe for the deployment of novel machine learning algorithm for both network and application management. However, given the presence of massively distributed and private datasets, it is challenging to use classical centralized learning algorithms in the IoT. To overcome this challenge, federated learning can be...
Article
Recently, a novel machine learning technique, federated learning, attracts ever-increasing interests from academia to industry. The main idea of federated learning is to collaboratively train a global optimal machine learning model among all the participants. During the process of parameters updating, the communication cost of the system or network...