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14th International Conference on Information and Communication Technology convergence, ICTC 2023

作者： Lee, Jongtae Lee, Wonjae Kim, Jae-Hyun Ajou University Department of Artificial Intelligence Convergence Network Suwon16499 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

ISBN: (纸本)9798350313277

The high velocity of LEO satellites can result in frequent handover (HO) in non-terrestrial networks (NTN). In a satellite environment, due to the high altitude, the signal strength variation between the cell center and the cell edge is very small. As a result, when using the signal strength-based HO, there are challenges in selecting an appropriate cell during HO. To address this issue, we proposed a location-based conditional HO (CHO) approach instead of the signal strength-based method. This approach uses the distance between the reference location of the cell and the user equipment (UE) to make HO decisions. This allows the UE to choose a suitable cell. Additionally, through an analysis based on the variation of signal strength with the UE's position within the cell and the distance change between the UE and the cell center, the distance offset is determined. Simulation results demonstrate that the proposed approach, when compared to the conventional method, can reduce radio link failure (RLF) and HO failure (HOF). © 2023 IEEE.

关键词： handover LEO satellites mobility non-terrestrial networks

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Performance Evaluation of Distance-Based Handover Using LSTM in LEO-Based NTN 15

Performance Evaluation of Distance-Based Handover Using LSTM...

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15th International Conference on Information and Communication Technology convergence, ICTC 2024

作者： Ahn, Yujeong Lee, Jongtae Kim, Tae-Yoon Kim, Jae-Hyun Ajou University Department of Artificial Intelligence Convergence Network Suwon16499 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

ISBN: (纸本)9798350364637

In terrestrial networks (TN), the user equipment (UE) identifies the cell edge by detecting differences in reference signal received power (RSRP). However, in non-terestrial networks (NTN), these effects are minimal due to the high altitude, and frequent handover (HO) may occur. For address this issue, the 3rd Generation Partnership Project (3GPP) Release-17 introduced novel HO triggering conditions, such as distance-based and time(r)-based HO [1]. Especially, distance-based HO addresses the challenge of minimal RSRP differences in cell overlapping areas. However, it is performed based on distance without considering RSRP, which can present challenges in maintaining the average RSRP of the UE. Therefore, a method is necessary to determine the optimal distance-based offset that can maintain RSRP for the UE. In this paper, we propose a distance-based HO using long short-term memory (LSTM) to predict the average signal-to-interference-plus-noise-ratio (SINR), identify the optimal HO triggering position, and determine the distance-based HO triggering offset. Performance analysis demonstrates that the proposed distance-based HO maintains the average SINR while significantly reducing unnecessary HO compared to the RSRP-based HO. © 2024 IEEE.

关键词： Signal interference

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Optimal Movements of UAV using Reinforcement Learning in Emergency Environment 13

Optimal Movements of UAV using Reinforcement Learning in Eme...

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13th International Conference on Information and Communication Technology convergence, ICTC 2022

作者： Kang, Junghwa Kim, Jae-Hyun Ajou University Department of Artificial Intelligence Convergence Network Suwon16499 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Unmanned aerial vehicles (UAVs) serve as aerial base stations when the cellular network is unavailable in war and disaster environment. In the most papers considering UAV as aerial base station consider only outdoor users. However, the most traffic demand occurs from indoor users, thus, not only outdoor users but also indoor users should be considered. Moreover, it is necessary to find the optimal movement of the UAV to support both outdoor and indoor users. In this paper, we propose an optimal UAV path based on reinforcement learning that can support both indoor and outdoor users in disaster and war scenario where have dynamic changes. Simulation results show the reward composed with throughput and service many users in the presence of both indoor and outdoor users. In addition, the UAV support indoor users first with higher service priority due to an emergency. © 2022 IEEE.

关键词： Reinforcement learning

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An Energy-Efficient Mobile Agent-Based Data Aggregation Scheme for Wireless Body Area networks

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computers, Materials & Continua 2022年第3期70卷 5929-5948页

作者： Gulzar Mehmood Muhammad Zahid Khan Muhammad Fayaz Mohammad Faisal Haseeb Ur Rahman Jeonghwan Gwak Department of Computer Science&Information Technology University of MalakandChakdara Dir Lower23021Pakistan Department of Computer Science University of Central AsiaNarynKyrgyzstan Department of Software Korea National University of TransportationChungju27469Korea Department of Biomedical Engineering Korea National University of TransportationChungju27469Korea Department of AI Robotics Engineering Korea National University of TransportationChungju27469Korea Department of IT and Energy Convergence(BK21 FOUR) Korea National University of TransportationChungju27469Korea

Due to the advancement in wireless technology and miniaturization,Wireless Body Area networks(WBANs)have gained enormous popularity,having various applications,especially in the healthcare *** are intrinsically resource-constrained;therefore,they have specific design and development *** such highly desirable requirement is an energy-efficient and reliable Data Aggregation(DA)mechanism for *** efficient and reliableDAmay ultimately push the network to operate without much human intervention and further extend the network *** conventional client-serverDAparadigm becomes unsuitable and inefficient for WBANs when a large amount of data is generated in the ***,in most of the healthcare applications(patient’s critical conditions),it is highly important and required to send data as soon as possible;therefore,reliable data aggregation in WBANs is of great *** tackle the shortcomings of the client-serverDAparadigm,theMobile Agent-Basedmechanismproved to be a more workable *** aMobile Agent-Based mechanism,a taskspecific mobile agent(code)traverses to the intended sources to gather *** agents travel on a predefined path called itinerary;however,planning a suitable and reliable itinerary for a mobile agent is also a challenging issue *** paper presents a new Mobile Agent-Based DA scheme for WBANs,which is energy-efficient and ***,in the proposed scheme,the network is divided into clusters,and cluster-heads are ***,a mobile agent is generated from the base station to collect the required data from cluster *** the case,if any fault occurs in the existing itinerary,an alternate itinerary is planned in real-time without compromising the network *** our simulation-based validation,we have found that the proposed system delivers significantly improved fault-tolerance and reliability with energy-efficiency and extended network lifetime in WBANs.

关键词： WBANs mobile-agent data aggregation reliability

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A QoS-Aware Software Defined Mobility Architecture for Named Data networking

A QoS-Aware Software Defined Mobility Architecture for Named...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Ali, Jehad Adnan, Muhammad Gadekallu, Thippa Reddy Jhaveri, Rutvij H. Roh, Byeong-Hee Sejong University Department of Computer Science and Engineering Seoul Korea Republic of Kookmin University Department of Electronics Engineering Seoul Korea Republic of Vellore Institute of Technology School of Information Technology and Engineering Vellore India Pandit Deendayal Energy University Department of Electronics Engineering Gandhinagar India Ajou University Department of AI Convergence Network Suwon Korea Republic of

ISBN: (纸本)9781665459754

Named Data networking (NDN) is a future architecture for the internet and is an inventive model to provide content-based services. The unique properties of NDN, like naming schemes for network packets and approaches for in-network caching, makes it more reasonable for content distribution. However, extreme changes are required in the existing network infrastructure in order to implement NDN on it. One of the primary research dimensions for this new internet architecture is mobility. Although some research work has been done to address mobility concerns, there are still mobility issues like maintaining the connection establishment by frequently changing the position of users, loss of interest and data packets, interest and data packet transmission and re-transmission without any correspondence delay. One of the reasonable approaches is the Software Defined networking (SDN) technique, which is used to realize mobility management for NDN. The core concept of SDN is the separation of the network control plane from the data plane. The data plane is capable of data forwarding, whereas the control plane is responsible for the entire network's control. The network control plane is decoupled from the data plane, resulting in a more programmable environment and the ability for external software to define a network's behavior. The SDN's main features, such as programmability, flexibility, and centralized control, make it a simple and scalable network. To cope with the issue of NDN mobility, we propose the design of a software-defined mobility architecture for NDN in which the SDN paradigm is extended to support mobility handling in the NDN architecture. Moreover, the intra-domain and Inter-domain handover handling scenarios in the software-defined mobility architecture for NDN have been described. © 2022 IEEE.

关键词： Internet of things

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The convergence System of an IoT Standard Service Platform and an Autonomous Drone

The Convergence System of an IoT Standard Service Platform a...

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Lee, Seungwoon Roh, Byeong-Hee Kim, S.J. Ajou University Department of AI Convergence Network Suwon Korea Republic of Howard R. Hughes College of Engineering University of Nevada Las VegasNV United States

ISBN: (纸本)9781665441544

IoT and drones have been merged rapidly for their respective fields, and their convergence is expected to create high added-value applications in near future. In this paper, we introduce a novel of integrating an IoT with a drone to provide converged services of well-known an IoT open standard and an autonomous drone. The paper explains the role of a drone in an IoT according to related work and demonstrates our convergence system architecture. A prototype and its validation are provided. © 2022 IEEE.

关键词： Internet of things

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Eight-bit Quantization for Super-Resolution networks and Its Hardware Implementation

Eight-bit Quantization for Super-Resolution Networks and Its...

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2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

作者： Nguyen, Xuan Truong Ko, Younghoon Nguyen, Tuan Nghia Lee, Hyuk-Jae Lee, Kyujoong Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of Sungshin Women's University School of Ai Convergence Seoul Korea Republic of

ISBN: (纸本)9798350320213

This work presents an 8-bit quantization scheme for super-resolution (SR) networks and its hardware implementation. Quantizing SR models to run in 8-bit is a non-trial task, frequently leading to non-negligible restoration performance degradation. To address the problem, we first present a quantization scheme that mitigates accuracy drop by effectively capturing the distributions of both weights and activations. Second, the proposed scheme is implemented using Verilog HDL and the design is synthesized on Synopsys Design Compiler using TSMC 65 nm library. Lastly, experimental results illustrate the effectiveness and efficiency of the proposed method. © 2023 IEEE.

关键词： 8-bit quantization SR accelerator SR networks

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SlowFast Based Real-Time Human Motion Recognition with Action Localization

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computer Systems Science & engineering 2023年第11期47卷 2135-2152页

作者： Gyu-Il Kim Hyun Yoo Kyungyong Chung Department of Computer Science Kyonggi UniversitySuwon16227Korea Contents Convergence Software Research Institute Kyonggi UniversitySuwon16227Korea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon16227Korea

Artificial intelligence is increasingly being applied in the field of video analysis,particularly in the area of public safety where video surveillance equipment such as closed-circuit television(CCTV)is used and automated analysis of video information is ***,various issues such as data size limitations and low processing speeds make real-time extraction of video data *** analysis technology applies object classification,detection,and relationship analysis to continuous 2D frame data,and the various meanings within the video are thus analyzed based on the extracted basic *** recognition is key in this *** recognition is a challenging field that analyzes human body movements,requiring the interpretation of complex movements of human joints and the relationships between various *** deep learning-based human skeleton detection algorithm is a representative motion recognition ***,motion analysis models such as the SlowFast network algorithm,have also been developed with excellent ***,these models do not operate properly in most wide-angle video environments outdoors,displaying low response speed,as expected from motion classification extraction in environments associated with high-resolution *** proposed method achieves high level of extraction and accuracy by improving SlowFast’s input data preprocessing and data structure *** input data are preprocessed through object tracking and background removal using YOLO and DeepSORT.A higher performance than that of a single model is achieved by improving the existing SlowFast’s data structure into a frame unit *** on the confusion matrix,accuracies of 70.16%and 70.74%were obtained for the existing SlowFast and proposed model,respectively,indicating a 0.58%increase in *** detection,based on behavioral classification,the existing SlowFast detected 2,341,164 cases,whereas the proposed model detected 3,11

关键词： Artificial intelligence convolutional neural network video analysis human action recognition skeleton extraction

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Improved Sequencing Heuristic DSDV Protocol Using Nomadic Mobility Model for FANETS

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computers, Materials & Continua 2022年第2期70卷 3653-3666页

作者： Inam Ullah Khan Muhammad Abul Hassan Muhammad Fayaz Jeonghwan Gwak Muhammad Adnan Aziz Department of Electronic Engineering Isra University(SEAS)Islamabad44000Pakistan Department of Computing and Technology Abasyn University Peshawar25000Pakistan Deparment of Computer Engineering Jeju National UniversityJejuKorea Department of Software Korea National University of TransportationChungju27469Korea Department of Biomedical Engineering Korea National University of TransportationChungju27469Korea Department of AI Robotics Engineering Korea National University of TransportationChungju27469Korea Department of IT&Energy Convergence(BK21 FOUR) Korea National University of TransportationChungju27469Korea

Most interesting area is the growing demand of flying-IoT mergers with smart ***,aerial vehicles,especially unmanned aerial vehicles(UAVs),have limited capabilities for maintaining node energy *** order to communicate effectively,IoT is a key element for smart *** improving network performance,routing protocols can be deployed in flying-IoT to improve latency,packet drop rate,packet delivery,power utilization,and average-end-to-end ***,in literature,proposed techniques are verymuch complex which cannot be easily implemented in realworld *** issue leads to the development of lightweight energyefficient routing in flying-IoT *** paper addresses the energy conservation problem in *** paper presents a novel approach for the internet of flying vehicles using DSDV ***-DSDV gives the notion of bellman-ford algorithm consisting of routing updates,information broadcasting,and stale *** shows optimal results in comparison with other contemporary routing *** mobility model is utilized in the scenario of flying networks to check the performance of routing protocols.

关键词： Flying-IoT DSR smart cities UAV’s FANETS

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Abnormal Behavior Detection Using Deep-Learning-Based Video Data Structuring

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Intelligent Automation & Soft Computing 2023年第8期37卷 2371-2386页

作者： Min-Jeong Kim Byeong-Uk Jeon Hyun Yoo Kyungyong Chung Department of Computer Science Kyonggi UniversitySuwon-si16227Gyeonggi-doKorea Contents Convergence Software Research Institute Kyonggi UniversitySuwon-si16227Gyeonggi-doKorea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon-si16227Gyeonggi-doKorea

With the increasing number of digital devices generating a vast amount of video data,the recognition of abnormal image patterns has become more ***,it is necessary to develop a method that achieves this task using object and behavior information within video *** methods for detecting abnormal behaviors only focus on simple motions,therefore they cannot determine the overall behavior occurring throughout a *** this study,an abnormal behavior detection method that uses deep learning(DL)-based video-data structuring is *** and motions are first extracted from continuous images by combining existing DL-based image analysis *** weight of the continuous data pattern is then analyzed through data structuring to classify the overall *** performance of the proposed method was evaluated using varying parameter settings,such as the size of the action clip and interval between action *** model achieved an accuracy of 0.9817,indicating excellent ***,we conclude that the proposed data structuring method is useful in detecting and classifying abnormal behaviors.

关键词： Deep learning object detection abnormal behavior recognition classification data structuring

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