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Prof. Dr. Joongheon Kim
School of Electrical Engineering, Korea University, Seoul, Korea

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Special issue paper
Published: 17 August 2021 in Journal of Real-Time Image Processing
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Nowadays, there are active research for deep learning applications to smart cities, e.g., smart factory, smart and micro grids, and smart logistics. Among them, for industrial smart harbor and logistics platforms, this paper proposes a novel two-stage algorithm for large-scale surveillance. For the purpose, this paper utilizes drones for flexible localization, and thus, the algorithm for scheduling between multiple drones and multiple multi-access edge computing (MEC) systems is proposed under the consideration of stability in this first-stage. After the scheduling, each drone transmits its own data to its associated MEC for enhancing the quality and then eventually the data will be used for surveillance. For improving the quality, super-resolution is used. In the second-stage algorithm, the self-adaptive super-resolution control is proposed for time-average performance maximization subject to stability, inspired by Lyapunov optimization. Based on data-intensive simulation results, it has been verified that the proposed algorithm achieves desired performance.

ACS Style

Soyi Jung; Joongheon Kim. Adaptive and stabilized real-time super-resolution control for UAV-assisted smart harbor surveillance platforms. Journal of Real-Time Image Processing 2021, 1 -11.

AMA Style

Soyi Jung, Joongheon Kim. Adaptive and stabilized real-time super-resolution control for UAV-assisted smart harbor surveillance platforms. Journal of Real-Time Image Processing. 2021; ():1-11.

Chicago/Turabian Style

Soyi Jung; Joongheon Kim. 2021. "Adaptive and stabilized real-time super-resolution control for UAV-assisted smart harbor surveillance platforms." Journal of Real-Time Image Processing , no. : 1-11.

Journal article
Published: 15 August 2021 in Electronics
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Experience sharing among multiple users in virtual reality (VR) is one of the key applications in next generation wireless systems. In this VR application, one object can be reproduced as a virtual object based on recorded/captured multiple real-time images from multiple observation points. At this time, VR applications require a lot of bandwidth to provide seamless services to users in wireless links, and thus, a certain level of data rates should be maintained. As the number of users increases, the server allocates more data rates to users on top of the limited bandwidth in wireless networks. At this time, users who utilize the VR streaming services will suffer from a lower quality, due to the limited bandwidth. This paper reports the measurement study and also analyzes the fluctuations in terms of the data rates as the number of users increases while sharing point cloud information in real-time authorized reality environments over IEEE 802.11ac wireless networks. Moreover, it measures and analyzes fluctuations in terms of frames-per-second and Jitters, which are practical quality reduction indicators.

ACS Style

Gusang Lee; Won Joon Yun; Yoo Jeong Ha; Soyi Jung; Jiyeon Kim; Sunghoon Hong; Joongheon Kim; Youn Kyu Lee. Measurement Study of Real-Time Virtual Reality Contents Streaming over IEEE 802.11ac Wireless Links. Electronics 2021, 10, 1967 .

AMA Style

Gusang Lee, Won Joon Yun, Yoo Jeong Ha, Soyi Jung, Jiyeon Kim, Sunghoon Hong, Joongheon Kim, Youn Kyu Lee. Measurement Study of Real-Time Virtual Reality Contents Streaming over IEEE 802.11ac Wireless Links. Electronics. 2021; 10 (16):1967.

Chicago/Turabian Style

Gusang Lee; Won Joon Yun; Yoo Jeong Ha; Soyi Jung; Jiyeon Kim; Sunghoon Hong; Joongheon Kim; Youn Kyu Lee. 2021. "Measurement Study of Real-Time Virtual Reality Contents Streaming over IEEE 802.11ac Wireless Links." Electronics 10, no. 16: 1967.

Journal article
Published: 07 July 2021 in IEEE Transactions on Vehicular Technology
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This paper proposes on-driving experience sharing algorithms at junctions in infrastructure-assisted vehicles-to-everything networks. For the purpose, a millimeter-wave (mmWave) technology is used because it provides multi-Gbps data rates which is helpful for handling users' short stay times at junctions and spatial reuse due to high beam directionality which is helpful for interference-avoidance among densely deployed vehicles at junctions. To realize on-driving experience sharing, the proposed algorithms focus on joint resource allocation and scheduling for 3GPP-compliant multiple unicast vehicle-to-vehicle (V2V) communications where the vehicles are group leaders (GLs) in 3GPP Mode 4(d). The resource allocation stands for the roadside unit (RSU) allocation to scheduled V2V GL links where RSU is essentially required for overcoming blockage by establishing two-hop relaying. Because vehicles stay for short times at junctions, this paper designs two algorithms without or with delay considerations. Without delay considerations, the joint optimization of RSU allocation and scheduling was originally formulated as mixed 0-1 non-convex optimization. However our proposed algorithm reformulates the problem into mixed 0-1 convex optimization, which is computationally easier to solve. With delay considerations, our proposed algorithm dynamically controls video contents frame rates for time-average on-driving video sharing quality maximization subject to delay constraints, inspired by Lyapunov optimization. Extensive simulation results demonstrate that our algorithms can significantly outperform in a variety of scenarios. Furthermore, we conduct the cost analysis for the proposed algorithms in terms of capital expenditure (CAPEX) and operating expenditure (OPEX).

ACS Style

Soyi Jung; Joongheon Kim; Marco Levorato; Carlos Cordeiro; Jae-Hyun Kim. Infrastructure-Assisted On-Driving Experience Sharing for Millimeter-Wave Connected Vehicles. IEEE Transactions on Vehicular Technology 2021, 70, 7307 -7321.

AMA Style

Soyi Jung, Joongheon Kim, Marco Levorato, Carlos Cordeiro, Jae-Hyun Kim. Infrastructure-Assisted On-Driving Experience Sharing for Millimeter-Wave Connected Vehicles. IEEE Transactions on Vehicular Technology. 2021; 70 (8):7307-7321.

Chicago/Turabian Style

Soyi Jung; Joongheon Kim; Marco Levorato; Carlos Cordeiro; Jae-Hyun Kim. 2021. "Infrastructure-Assisted On-Driving Experience Sharing for Millimeter-Wave Connected Vehicles." IEEE Transactions on Vehicular Technology 70, no. 8: 7307-7321.

Journal article
Published: 10 June 2021 in IEEE Systems Journal
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The rapid increase of diverse Internet of Things (IoT) services and devices has raised numerous challenges in terms of connectivity, interoperability, and security. The heterogeneity of the networks, devices, and services introduces serious vulnerabilities to security, especially distributed denial-of-service (DDoS) attacks, which exploit massive IoT devices to exhaust both network and victim resources. As such, this article proposes FOGshield, which is a localized DDoS prevention framework leveraging the federated computing power of the fog computing-based access networks to deploy multiple smart endpoint defenders at the border of relevant attack-source/destination networks. Cooperation among the smart endpoint defenders is supervised by a central orchestrator. The central orchestrator localizes each smart endpoint defender by feeding appropriate training parameters into its self-organizing map component, based on the attacking behavior. Performance of the FOGshield framework is verified using three typical IoT traffic scenarios. Numerical results reveal that the FOGshield outperforms existing solutions.

ACS Style

Nhu-Ngoc Dao; Trung V. Phan; Umar Sa'Ad; Joongheon Kim; Thomas Bauschert; Dinh-Thuan Do; Sungrae Cho. Securing Heterogeneous IoT With Intelligent DDoS Attack Behavior Learning. IEEE Systems Journal 2021, PP, 1 -10.

AMA Style

Nhu-Ngoc Dao, Trung V. Phan, Umar Sa'Ad, Joongheon Kim, Thomas Bauschert, Dinh-Thuan Do, Sungrae Cho. Securing Heterogeneous IoT With Intelligent DDoS Attack Behavior Learning. IEEE Systems Journal. 2021; PP (99):1-10.

Chicago/Turabian Style

Nhu-Ngoc Dao; Trung V. Phan; Umar Sa'Ad; Joongheon Kim; Thomas Bauschert; Dinh-Thuan Do; Sungrae Cho. 2021. "Securing Heterogeneous IoT With Intelligent DDoS Attack Behavior Learning." IEEE Systems Journal PP, no. 99: 1-10.

Journal article
Published: 25 April 2021 in Electronics
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With the wide use of the Internet of Things and artificial intelligence, energy management systems play an increasingly important role in the management and control of energy consumption in modern buildings. Load forecasting for building energy management systems is one of the most challenging forecasting tasks as it requires high accuracy and stable operating conditions. In this study, we propose a novel multi-behavior with bottleneck features long short-term memory (LSTM) model that combines the predictive behavior of long-term, short-term, and weekly feature models by using the bottleneck feature technique for building energy management systems. The proposed model, along with the unique scheme, provides predictions with the accuracy of long-term memory, adapts to unexpected and unpatternizable intrinsic temporal factors through the short-term memory, and remains stable because of the weekly features of input data. To verify the accuracy and stability of the proposed model, we present and analyze several learning models and metrics for evaluation. Corresponding experiments are conducted and detailed information on data preparation and model training are provided. Relative to single-model LSTM, the proposed model achieves improved performance and displays an excellent capability to respond to unexpected situations in building energy management systems.

ACS Style

Van Bui; Nam Le; Van Nguyen; Joongheon Kim; Yeong Jang. Multi-Behavior with Bottleneck Features LSTM for Load Forecasting in Building Energy Management System. Electronics 2021, 10, 1026 .

AMA Style

Van Bui, Nam Le, Van Nguyen, Joongheon Kim, Yeong Jang. Multi-Behavior with Bottleneck Features LSTM for Load Forecasting in Building Energy Management System. Electronics. 2021; 10 (9):1026.

Chicago/Turabian Style

Van Bui; Nam Le; Van Nguyen; Joongheon Kim; Yeong Jang. 2021. "Multi-Behavior with Bottleneck Features LSTM for Load Forecasting in Building Energy Management System." Electronics 10, no. 9: 1026.

Journal article
Published: 19 April 2021 in ICT Express
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The electric vehicle (EV) market increases due to the benefits of reducing greenhouse gas emissions using renewable energy resources. In this context, the charging scheme of electric vehicles in charging stations (CSs) is also important. Electronic devices’ charging between EV and multiple CS should consider EV’s short battery capacity, long charging time, residual energy in each CS, and time of use (ToU) for charging. In this paper, multiple CSs compete to offer electricity charging to a single EV. Based on this need, this paper proposes a deep learning-based auction which increases the charging amounts using Myerson auction while preserving truthfulness.

ACS Style

Haemin Lee; Soyi Jung; Joongheon Kim. Truthful electric vehicle charging via neural-architectural Myerson auction. ICT Express 2021, 7, 196 -199.

AMA Style

Haemin Lee, Soyi Jung, Joongheon Kim. Truthful electric vehicle charging via neural-architectural Myerson auction. ICT Express. 2021; 7 (2):196-199.

Chicago/Turabian Style

Haemin Lee; Soyi Jung; Joongheon Kim. 2021. "Truthful electric vehicle charging via neural-architectural Myerson auction." ICT Express 7, no. 2: 196-199.

Journal article
Published: 26 February 2021 in IEEE Transactions on Vehicular Technology
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This paper proposes a cloud-assisted joint charging scheduling and energy management framework for unmanned aerial vehicle (UAV) networks. For charging the UAVs those are extremely power hungry, charging towers are considered for plug-and-play charging during run-time operations. The charging towers should be cost-effective, thus it is equipped with photo-voltaic power generation and energy storage systems functionalities. Furthermore, the towers should be cooperative for more cost-effectiveness by intelligent energy sharing. Based on the needs and setting, this paper proposes 1) charging scheduling between UAVs and towers and 2) cooperative energy managements among towers. For charging scheduling, the UAVs and towers should be scheduled for maximizing charging energy amounts and the scheduled pairs should determine charging energy allocation amounts. Here, two decisions are correlated, i.e., it is a non-convex problem. We re-formulate the non-convex to convex for guaranteeing optimal solutions. Lastly, the cooperative energy sharing among towers is designed and implemented with multi-agent deep reinforcement learning and then intelligent energy sharing can be realized. We can observe that the two methods are related and it should be managed, coordinated, and harmonized by a centralized orchestration manager under the consideration of fairness, energy-efficiency, and cost-effectiveness. Our data-intensive performance evaluation verifies that our proposed framework achieves desired performance.

ACS Style

Soyi Jung; Won Joon Yun; Myungjae Shin; Joongheon Kim; Jae-Hyun Kim. Orchestrated Scheduling and Multi-Agent Deep Reinforcement Learning for Cloud-Assisted Multi-UAV Charging Systems. IEEE Transactions on Vehicular Technology 2021, PP, 1 -1.

AMA Style

Soyi Jung, Won Joon Yun, Myungjae Shin, Joongheon Kim, Jae-Hyun Kim. Orchestrated Scheduling and Multi-Agent Deep Reinforcement Learning for Cloud-Assisted Multi-UAV Charging Systems. IEEE Transactions on Vehicular Technology. 2021; PP (99):1-1.

Chicago/Turabian Style

Soyi Jung; Won Joon Yun; Myungjae Shin; Joongheon Kim; Jae-Hyun Kim. 2021. "Orchestrated Scheduling and Multi-Agent Deep Reinforcement Learning for Cloud-Assisted Multi-UAV Charging Systems." IEEE Transactions on Vehicular Technology PP, no. 99: 1-1.

Journal article
Published: 25 February 2021 in Electronics
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This paper proposes a novel coordinated multi-agent deep reinforcement learning (MADRL) algorithm for energy sharing among multiple unmanned aerial vehicles (UAVs) in order to conduct big-data processing in a distributed manner. For realizing UAV-assisted aerial surveillance or flexible mobile cellular services, robust wireless charging mechanisms are essential for delivering energy sources from charging towers (i.e., charging infrastructure) to their associated UAVs for seamless operations of autonomous UAVs in the sky. In order to actively and intelligently manage the energy resources in charging towers, a MADRL-based coordinated energy management system is desired and proposed for energy resource sharing among charging towers. When the required energy for charging UAVs is not enough in charging towers, the energy purchase from utility company (i.e., energy source provider in local energy market) is desired, which takes high costs. Therefore, the main objective of our proposed coordinated MADRL-based energy sharing learning algorithm is minimizing energy purchase from external utility companies to minimize system-operational costs. Finally, our performance evaluation results verify that the proposed coordinated MADRL-based algorithm achieves desired performance improvements.

ACS Style

Soyi Jung; Won Yun; Joongheon Kim; Jae-Hyun Kim. Coordinated Multi-Agent Deep Reinforcement Learning for Energy-Aware UAV-Based Big-Data Platforms. Electronics 2021, 10, 543 .

AMA Style

Soyi Jung, Won Yun, Joongheon Kim, Jae-Hyun Kim. Coordinated Multi-Agent Deep Reinforcement Learning for Energy-Aware UAV-Based Big-Data Platforms. Electronics. 2021; 10 (5):543.

Chicago/Turabian Style

Soyi Jung; Won Yun; Joongheon Kim; Jae-Hyun Kim. 2021. "Coordinated Multi-Agent Deep Reinforcement Learning for Energy-Aware UAV-Based Big-Data Platforms." Electronics 10, no. 5: 543.

Communication
Published: 20 February 2021 in Sensors
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Green tide, which is a serious water pollution problem, is caused by the complex relationships of various factors, such as flow rate, several water quality indicators, and weather. Because the existing methods are not suitable for identifying these relationships and making accurate predictions, a new system and algorithm is required to predict the green tide phenomenon and also minimize the related damage before the green tide occurs. For this purpose, we consider a new network model using smart sensor-based federated learning which is able to use distributed observation data with geologically separated local models. Moreover, we design an optimal scheduler which is beneficial to use real-time big data arrivals to make the overall network system efficient. The proposed scheduling algorithm is effective in terms of (1) data usage and (2) the performance of green tide occurrence prediction models. The advantages of the proposed algorithm is verified via data-intensive experiments with real water quality big-data.

ACS Style

Soohyun Park; Soyi Jung; Haemin Lee; Joongheon Kim; Jae-Hyun Kim. Large-Scale Water Quality Prediction Using Federated Sensing and Learning: A Case Study with Real-World Sensing Big-Data. Sensors 2021, 21, 1462 .

AMA Style

Soohyun Park, Soyi Jung, Haemin Lee, Joongheon Kim, Jae-Hyun Kim. Large-Scale Water Quality Prediction Using Federated Sensing and Learning: A Case Study with Real-World Sensing Big-Data. Sensors. 2021; 21 (4):1462.

Chicago/Turabian Style

Soohyun Park; Soyi Jung; Haemin Lee; Joongheon Kim; Jae-Hyun Kim. 2021. "Large-Scale Water Quality Prediction Using Federated Sensing and Learning: A Case Study with Real-World Sensing Big-Data." Sensors 21, no. 4: 1462.

Journal article
Published: 12 February 2021 in ICT Express
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The urban aerial mobility (UAM) system, such as drone taxi or air taxi, is one of future on-demand transportation networks. Among them, electric vertical takeoff and landing (eVTOL) is one of UAM systems that is for identifying the locations of passengers, flying to the positions where the passengers are located, loading the passengers, and delivering the passengers to their destinations. In this paper, we propose a distributed deep reinforcement learning where the agents are formulated as eVTOL vehicles that can compute the optimal passenger transportation routes under the consideration of passenger behaviors, collisions among eVTOL, and eVTOL battery status.

ACS Style

Won Joon Yun; Soyi Jung; Joongheon Kim; Jae-Hyun Kim. Distributed deep reinforcement learning for autonomous aerial eVTOL mobility in drone taxi applications. ICT Express 2021, 7, 1 -4.

AMA Style

Won Joon Yun, Soyi Jung, Joongheon Kim, Jae-Hyun Kim. Distributed deep reinforcement learning for autonomous aerial eVTOL mobility in drone taxi applications. ICT Express. 2021; 7 (1):1-4.

Chicago/Turabian Style

Won Joon Yun; Soyi Jung; Joongheon Kim; Jae-Hyun Kim. 2021. "Distributed deep reinforcement learning for autonomous aerial eVTOL mobility in drone taxi applications." ICT Express 7, no. 1: 1-4.

Journal article
Published: 29 January 2021 in Journal of Network and Computer Applications
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Blockchain-based cryptocurrencies, such as Bitcoin, have seen on the rise in their popularity and value, making them a target to several forms of Denial-of-Service (DoS) attacks, and calling for a better understanding of their attack surface from both security and distributed systems standpoints. In this paper, and in the pursuit of understanding the attack surface of blockchains, we explore a new form of attack that can be carried out on the memory pools (mempools), and mainly targets blockchain-based cryptocurrencies. We study this attack on Bitcoin's mempool and explore the attack's effects on transactions fee paid by benign users. To counter this attack, this paper further proposes Contra-∗, a set of countermeasures utilizing fee, age, and size (thus, Contra-F, Contra-A, and Contra-S) as prioritization mechanisms. Contra-∗ optimize the mempool size and help in countering the effects of DoS attacks due to spam transactions. We evaluate Contra-∗ by simulations and analyze their effectiveness under various attack conditions.

ACS Style

Muhammad Saad; Joongheon Kim; Daehun Nyang; David Mohaisen. Contra-∗: Mechanisms for countering spam attacks on blockchain's memory pools. Journal of Network and Computer Applications 2021, 179, 102971 .

AMA Style

Muhammad Saad, Joongheon Kim, Daehun Nyang, David Mohaisen. Contra-∗: Mechanisms for countering spam attacks on blockchain's memory pools. Journal of Network and Computer Applications. 2021; 179 ():102971.

Chicago/Turabian Style

Muhammad Saad; Joongheon Kim; Daehun Nyang; David Mohaisen. 2021. "Contra-∗: Mechanisms for countering spam attacks on blockchain's memory pools." Journal of Network and Computer Applications 179, no. : 102971.

Journal article
Published: 19 January 2021 in IEEE Systems Journal
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This article presents LiteZKP a framework for supporting multiple anonymous payments using a smart contract-based zero-knowledge proof (ZKP) protocol on resource-limited devices. Specifically, to address challenges related to minimizing the computational overhead and offer a fully anonymous system, LiteZKP includes novel schemes such a new merkle tree mechanism to reduce the burden of ZKP operations, and integrates smart contract-based ZKP with an off-chain payment channel to reduce the amount of ZKP operations when performing continuous data exchange. We present evaluation results of LiteZKP from both PC-scale and resource-limited client devices and our results suggest that LiteZKP reduces the latency and energy consumption by more than 55% on Internet of Things (IoT)/mobile edge computing platforms, while requiring only 8% of block processing fee compared to a naive ZKP-based scheme.

ACS Style

Eunseong Boo; Joongheon Kim; Jeonggil Ko. LiteZKP: Lightening Zero-Knowledge Proof-Based Blockchains for IoT and Edge Platforms. IEEE Systems Journal 2021, PP, 1 -12.

AMA Style

Eunseong Boo, Joongheon Kim, Jeonggil Ko. LiteZKP: Lightening Zero-Knowledge Proof-Based Blockchains for IoT and Edge Platforms. IEEE Systems Journal. 2021; PP (99):1-12.

Chicago/Turabian Style

Eunseong Boo; Joongheon Kim; Jeonggil Ko. 2021. "LiteZKP: Lightening Zero-Knowledge Proof-Based Blockchains for IoT and Edge Platforms." IEEE Systems Journal PP, no. 99: 1-12.

Book chapter
Published: 09 December 2020 in Advances and Applications in Deep Learning
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This chapter introduces a dynamic and low-complexity decision-making algorithm which aims at time-average utility maximization in real-time deep learning platforms, inspired by Lyapunov optimization. In deep learning computation, large delays can happen due to the fact that it is computationally expensive. Thus, handling the delays is an important issue for the commercialization of deep learning algorithms. In this chapter, the proposed algorithm observes system delays at first formulated by queue-backlog, and then it dynamically conducts sequential decision-making under the tradeoff between utility (i.e., deep learning performance) and system delays. In order to evaluate the proposed decision-making algorithm, the performance evaluation results with real-world data are presented under the applications of super-resolution frameworks. Lastly, this chapter summarizes that the Lyapunov optimization algorithm can be used in various emerging applications.

ACS Style

Soohyun Park; Dohyun Kim; Joongheon Kim. Dynamic Decision-Making for Stabilized Deep Learning Software Platforms. Advances and Applications in Deep Learning 2020, 1 .

AMA Style

Soohyun Park, Dohyun Kim, Joongheon Kim. Dynamic Decision-Making for Stabilized Deep Learning Software Platforms. Advances and Applications in Deep Learning. 2020; ():1.

Chicago/Turabian Style

Soohyun Park; Dohyun Kim; Joongheon Kim. 2020. "Dynamic Decision-Making for Stabilized Deep Learning Software Platforms." Advances and Applications in Deep Learning , no. : 1.

Journal article
Published: 21 October 2020 in Sustainability
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Content-Centric Networking (CCN) is one of the emerging paradigms for the future Internet, which shifts the communication paradigm from host-centric to data-centric. In CCN, contents are delivered by their unique names, and a public-key-based signature is built into data packets to verify the authenticity and integrity of the contents. To date, research has tried to accelerate the validation of the given data packets, but existing techniques were designed to improve the performance of content verification from the requester’s viewpoint. However, we need to efficiently verify the validity of data packets in each forwarding engine, since the transmission of invalid packets influences not only security but also performance, which can lead to a DDoS (Distributed Denial of Service) attack on CCN. For example, an adversary can inject a number of meaningless packets into CCN to consume the forwarding engines’ cache and network bandwidth. In this paper, a novel authentication architecture is introduced, which can support faster forwarding by accelerating the performance of data validation in forwarding engines. Since all forwarding engines verify data packets, our authentication architecture can eliminate invalid packets before they are injected into other CCN nodes. The architecture utilizes public-key based authentication algorithms to support public verifiability and non-repudiation, but a novel technique is proposed in this paper to reduce the overhead from using PKI for verifying public keys used by forwarding engines and end-users in the architecture. The main merit of this work is in improving the performance of data-forwarding in CCN regardless of the underlying public-key validation mechanism, such as PKI, by reducing the number of accesses to the mechanism. Differently from existing approaches that forgive some useful features of the Naive CCN for higher performance, the proposed technique is the only architecture which can support all useful features given by the Naive CCN.

ACS Style

Taek-Young Youn; Joongheon Kim; David Mohaisen; Seog Seo. Faster Data Forwarding in Content-Centric Network via Overlaid Packet Authentication Architecture. Sustainability 2020, 12, 8746 .

AMA Style

Taek-Young Youn, Joongheon Kim, David Mohaisen, Seog Seo. Faster Data Forwarding in Content-Centric Network via Overlaid Packet Authentication Architecture. Sustainability. 2020; 12 (20):8746.

Chicago/Turabian Style

Taek-Young Youn; Joongheon Kim; David Mohaisen; Seog Seo. 2020. "Faster Data Forwarding in Content-Centric Network via Overlaid Packet Authentication Architecture." Sustainability 12, no. 20: 8746.

Journal article
Published: 13 October 2020 in Electronics
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This paper proposes an energy-efficient cluster head selection method in the wireless ad hoc network by using a hybrid quantum-classical approach. The wireless ad hoc network is divided into several clusters via cluster head selection, and the performance of the network topology depends on the distribution of these clusters. For an energy-efficient network topology, none of the selected cluster heads should be neighbors. In addition, all the selected cluster heads should have high energy-consumption efficiency. Accordingly, an energy-efficient cluster head selection policy can be defined as a maximum weight independent set (MWIS) formulation. The cluster head selection policy formulated with MWIS is solved by using the quantum approximate optimization algorithm (QAOA), which is a hybrid quantum-classical algorithm. The accuracy of the proposed energy-efficient cluster head selection via QAOA is verified via simulations.

ACS Style

Jaeho Choi; Seunghyeok Oh; Joongheon Kim. Energy-Efficient Cluster Head Selection via Quantum Approximate Optimization. Electronics 2020, 9, 1669 .

AMA Style

Jaeho Choi, Seunghyeok Oh, Joongheon Kim. Energy-Efficient Cluster Head Selection via Quantum Approximate Optimization. Electronics. 2020; 9 (10):1669.

Chicago/Turabian Style

Jaeho Choi; Seunghyeok Oh; Joongheon Kim. 2020. "Energy-Efficient Cluster Head Selection via Quantum Approximate Optimization." Electronics 9, no. 10: 1669.

Journal article
Published: 13 October 2020 in Applied Sciences
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This paper proposes an application algorithm based on a quantum approximate optimization algorithm (QAOA) for wireless scheduling problems. QAOA is one of the promising hybrid quantum-classical algorithms to solve combinatorial optimization problems and it provides great approximate solutions to non-deterministic polynomial-time (NP) hard problems. QAOA maps the given problem into Hilbert space, and then it generates the Hamiltonian for the given objective and constraint. Then, QAOA finds proper parameters from the classical optimization loop in order to optimize the expectation value of the generated Hamiltonian. Based on the parameters, the optimal solution to the given problem can be obtained from the optimum of the expectation value of the Hamiltonian. Inspired by QAOA, a quantum approximate optimization for scheduling (QAOS) algorithm is proposed. The proposed QAOS designs the Hamiltonian of the wireless scheduling problem which is formulated by the maximum weight independent set (MWIS). The designed Hamiltonian is converted into a unitary operator and implemented as a quantum gate operation. After that, the iterative QAOS sequence solves the wireless scheduling problem. The novelty of QAOS is verified with simulation results implemented via Cirq and TensorFlow-Quantum.

ACS Style

Jaeho Choi; Seunghyeok Oh; Joongheon Kim. Quantum Approximation for Wireless Scheduling. Applied Sciences 2020, 10, 7116 .

AMA Style

Jaeho Choi, Seunghyeok Oh, Joongheon Kim. Quantum Approximation for Wireless Scheduling. Applied Sciences. 2020; 10 (20):7116.

Chicago/Turabian Style

Jaeho Choi; Seunghyeok Oh; Joongheon Kim. 2020. "Quantum Approximation for Wireless Scheduling." Applied Sciences 10, no. 20: 7116.

Journal article
Published: 21 September 2020 in IEEE Transactions on Mobile Computing
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We present Supremo, a cloud-assisted system for low-latency image Super-Resolution (SR) in mobile devices. As SR is extremely compute-intensive, we first further optimize state-of-the-art DNN to reduce the inference latency. Furthermore, we design a mobile-cloud cooperative execution pipeline composed of specialized data compression algorithms to minimize end-to-end latency with minimal image quality degradation. Finally, we extend Supremo to video applications by formulating a dynamic optimal control algorithm to design Supremo-Opt, which aims to maximize the impact of SR while satisfying latency and resource constraints under practical network conditions. Supremo upscales 360p image to 1080p in 122 ms, which is 43.68 x faster than on-device GPU execution. Compared to cloud offloading-based solutions, Supremo reduces wireless network bandwidth consumption and end-to-end latency by 15.23 x and 4.85 x compared to baseline approach of sending and receiving whole images, and achieves 2.39 dB higher PSNR compared to using conventional JPEG to achieve similar data size compression. Furthermore, Supremo-Opt guarantees robust performance in practical scenarios.

ACS Style

Juheon Yi; Seongwon Kim; Joongheon Kim; Sunghyun Choi. Supremo: Cloud-Assisted Low-Latency Super-Resolution in Mobile Devices. IEEE Transactions on Mobile Computing 2020, PP, 1 -1.

AMA Style

Juheon Yi, Seongwon Kim, Joongheon Kim, Sunghyun Choi. Supremo: Cloud-Assisted Low-Latency Super-Resolution in Mobile Devices. IEEE Transactions on Mobile Computing. 2020; PP (99):1-1.

Chicago/Turabian Style

Juheon Yi; Seongwon Kim; Joongheon Kim; Sunghyun Choi. 2020. "Supremo: Cloud-Assisted Low-Latency Super-Resolution in Mobile Devices." IEEE Transactions on Mobile Computing PP, no. 99: 1-1.

Journal article
Published: 09 September 2020 in Electronics
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In modern surveillance systems, the use of unmanned aerial vehicles (UAVs) has been actively discussed in order to extend target monitoring areas, even for an extreme circumstances. This paper proposes an energy-efficient UAV-based surveillance system that operates from two different sequential methods. First, the proposed algorithm pursues energy-efficient operations by deactivating selected surveillance cameras on the UAVs located in overlapping areas. For this objective, a message-passing based algorithm is used because the overlapping situations can be formulated using a max-weight independent set. Next, the unscheduled UAVs based on the message-passing fly to the charging towers to be charged. This algorithm computes the optimal matching between the UAVs and charging towers and the amount of energy allocation for the scheduled UAV-tower pairs. This joint optimization is initially formulated as non-convex, and it is then reformulated to be convex, which can guarantee optimal solutions. The proposed framework achieves the desired performance, as presented in the performance evaluation.

ACS Style

Soyi Jung; Joongheon Kim; Jae-Hyun Kim. Joint Message-Passing and Convex Optimization Framework for Energy-Efficient Surveillance UAV Scheduling. Electronics 2020, 9, 1475 .

AMA Style

Soyi Jung, Joongheon Kim, Jae-Hyun Kim. Joint Message-Passing and Convex Optimization Framework for Energy-Efficient Surveillance UAV Scheduling. Electronics. 2020; 9 (9):1475.

Chicago/Turabian Style

Soyi Jung; Joongheon Kim; Jae-Hyun Kim. 2020. "Joint Message-Passing and Convex Optimization Framework for Energy-Efficient Surveillance UAV Scheduling." Electronics 9, no. 9: 1475.

Journal article
Published: 21 August 2020 in Electronics
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Federated learning-enabled edge devices train global models by sharing them while avoiding local data sharing. In federated learning, the sharing of models through communication between several clients and central servers results in various problems such as a high latency and network congestion. Moreover, battery consumption problems caused by local training procedures may impact power-hungry clients. To tackle these issues, federated edge learning (FEEL) applies the network edge technologies of mobile edge computing. In this paper, we propose a novel control algorithm for high-performance and stabilized queue in FEEL system. We consider that the FEEL environment includes the clients transmit data to associated federated edges; these edges then locally update the global model, which is downloaded from the central server via a backhaul. Obtaining greater quantities of local data from the clients facilitates more accurate global model construction; however, this may be harmful in terms of queue stability in the edge, owing to substantial data arrivals from the clients. Therefore, the proposed algorithm varies the number of clients selected for transmission, with the aim of maximizing the time-averaged federated learning accuracy subject to queue stability. Based on this number of clients, the federated edge selects the clients to transmit on the basis of resource status.

ACS Style

Joohyung Jeon; Soohyun Park; Minseok Choi; Joongheon Kim; Young-Bin Kwon; Sungrae Cho. Optimal User Selection for High-Performance and Stabilized Energy-Efficient Federated Learning Platforms. Electronics 2020, 9, 1359 .

AMA Style

Joohyung Jeon, Soohyun Park, Minseok Choi, Joongheon Kim, Young-Bin Kwon, Sungrae Cho. Optimal User Selection for High-Performance and Stabilized Energy-Efficient Federated Learning Platforms. Electronics. 2020; 9 (9):1359.

Chicago/Turabian Style

Joohyung Jeon; Soohyun Park; Minseok Choi; Joongheon Kim; Young-Bin Kwon; Sungrae Cho. 2020. "Optimal User Selection for High-Performance and Stabilized Energy-Efficient Federated Learning Platforms." Electronics 9, no. 9: 1359.

Journal article
Published: 17 August 2020 in IEEE Systems Journal
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The 5G new radio standard defines various functions for forwarding treatments under differentiated quality of services requirements. In particular, a gNB base station helps combat overbuffering due to massive packet flows from associated mobile devices. Several active queue management (AQM) schemes have been proposed previously, with controlled delay (CoDel) proving to be an efficient method that can properly handle excessive buffering, that is called bufferbloat. This article proposes a novel and enhanced AQM scheme based on CoDel for gNB that guarantees low queuing delays, minimizes packet drop rate, and furthermore stabilizes the built-in queue. According to data-intensive performance evaluation results, the proposed scheme verifies that the queuing delay for served packets are decreased and supports ultrareliable low latency communication applications. Especially, the proposed algorithm achieves the packet drop rates and queuing delays tradeoff as $[\mathcal {O}(1/V), {O}(V)]$ where $V$ is a tradeoff parameter; providing efficient control decisions to improve performance in terms of time-average packet drop rate minimization subject to queue stability on demand in 5G mobile system.

ACS Style

Soyi Jung; Joongheon Kim; Jae-Hyun Kim. Intelligent Active Queue Management for Stabilized QoS Guarantees in 5G Mobile Networks. IEEE Systems Journal 2020, 15, 4293 -4302.

AMA Style

Soyi Jung, Joongheon Kim, Jae-Hyun Kim. Intelligent Active Queue Management for Stabilized QoS Guarantees in 5G Mobile Networks. IEEE Systems Journal. 2020; 15 (3):4293-4302.

Chicago/Turabian Style

Soyi Jung; Joongheon Kim; Jae-Hyun Kim. 2020. "Intelligent Active Queue Management for Stabilized QoS Guarantees in 5G Mobile Networks." IEEE Systems Journal 15, no. 3: 4293-4302.