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Handover Mechanism Based on Underwater Hybrid Software-Defined Modem in Advanced Diver Networks

Computers, Materials & Continua 2022年第3期70卷 5721-5743页

作者： K.M.Delphin Raj Sun-Ho Yum Jinyoung Lee Eunbi Ko Soo-Yong Shin Soo-Hyun Park Department of Financial Information Security Kookmin UniversitySeoul02707Korea special communication&convergence service research center Kookmin UniversitySeoul02707Korea Department of Computer Science Kookmin UniversitySeoul02707Korea

For the past few decades,the internet of underwater things(IoUT)otained a lot of attention in mobile aquatic applications such as oceanography,diver network monitoring,unmanned underwater exploration,underwater surveillance,location tracking system,etc.Most of the IoUT applications rely on acoustic medium.The current IoUT applications face difficulty in delivering a reliable communication system due to the various technical limitations of IoUT environment such as low data rate,attenuation,limited bandwidth,limited battery,limited memory,connectivity problem,etc.One of the significant applications of IoUT include monitoring underwater diver networks.In order to perform a reliable and energy-efficient communication system in the underwater diver networks,a smart underwater hybrid softwaredefined modem(UHSDM)for the mobile ad-hoc network was developed that is used for selecting the best channel/medium among acoustic,visible light communication(VLC),and infrared(IR)based on the criteria established within the system.However,due to the mobility of underwater divers,the developed UHSDMmeets the challenges such as connectivity errors,frequent link failure,transmission delay caused by re-routing,etc.During emergency,the divers are most at the risk of survival.To deal with diver mobility,connectivity,energy efficiency,and reducing the latency in ADN,a handover mechanism based on pre-built UHSDM is proposed in this paper.This paper focuses on(1)design of UHSDM for ADN(2)propose the channel selection mechanism in UHSDM for selecting the best medium for handover and(3)propose handover protocol inADN.The implementation result shows that the proposed mechanism can be used to find the new route for divers in advance and the latency can be reduced significantly.Additionally,this paper shows the real field experiment of air tests and underwater tests with various distances.This research will contribute much to the profit of researchers in underwater diver networks and underwater networks,for improving the quality of services(QoS)

关键词： Internet of underwater things(IoUT) underwater hybrid software-defined modem(UHSDM) advanced diver networks(ADN) channel selection mechanism(CSM) handover mechanism acoustic visible light communication(VLC) infrared(IR)

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Adaptive Momentum-Backpropagation Algorithm for Flood Prediction and Management in the Internet of Things

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Computers, Materials & Continua 2023年第10期77卷 1053-1079页

作者： Jayaraj Thankappan Delphin Raj Kesari Mary Dong Jin Yoon Soo-Hyun Park Department of Computer Science Muslim Arts CollegeManonmaniam Sundaranar UniversityThiruvitancodeTamil Nadu629174India laboratory special communication&convergence service research center Kookmin UniversitySeoul02707Korea Development and Operation Support of Polar research Equipment Korea Polar Research Institute(KOPRI)Incheon21990Korea Department of Financial Information Security Kookmin UniversitySeoul02707Korea

Flooding is a hazardous natural calamity that causes significant damage to lives and infrastructure in the real world.Therefore,timely and accurate decision-making is essential for mitigating flood-related damages.The traditional flood prediction techniques often encounter challenges in accuracy,timeliness,complexity in handling dynamic flood patterns and leading to substandard flood management strategies.To address these challenges,there is a need for advanced machine learning models that can effectively analyze Internet of Things(IoT)-generated flood data and provide timely and accurate flood predictions.This paper proposes a novel approach-the Adaptive Momentum and Backpropagation(AM-BP)algorithm-for flood prediction and management in IoT networks.The AM-BP model combines the advantages of an adaptive momentum technique with the backpropagation algorithm to enhance flood prediction accuracy and efficiency.Real-world flood data is used for validation,demonstrating the superior performance of the AM-BP algorithm compared to traditional methods.In addition,multilayer high-end computing architecture(MLCA)is used to handle weather data such as rainfall,river water level,soil moisture,etc.The AM-BP’s real-time abilities enable proactive flood management,facilitating timely responses and effective disaster mitigation.Furthermore,the AM-BP algorithm can analyze large and complex datasets,integrating environmental and climatic factors for more accurate flood prediction.The evaluation result shows that the AM-BP algorithm outperforms traditional approaches with an accuracy rate of 96%,96.4%F1-Measure,97%Precision,and 95.9%Recall.The proposed AM-BP model presents a promising solution for flood prediction and management in IoT networks,contributing to more resilient and efficient flood control strategies,and ensuring the safety and well-being of communities at risk of flooding.

关键词： Internet of Things flood prediction artificial neural network adaptive momentum backpropagation optimization disaster management

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UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse services: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 430-445页

作者： Jiawen Kang Junlong Chen Minrui Xu Zehui Xiong Yutao Jiao Luchao Han Dusit Niyato Yongju Tong Shengli Xie IEEE School of Automation Guangdong University of TechnologyGuangzhou 510006China 111 center for Intelligent Batch Manufacturing based on IoT Technology Guangzhou 510006China Key laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingaporeSingapore Pillar of Information Systems Technology and Design Singapore University of Technology and DesignSingaporeSingapore College of communication Engineering Army Engineering University of PLANanjing 210007China National Natural Science Foundation of China Beijing 100085China Key laboratory of Intelligent Detection and IoT in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong Key laboratory of IoT Information Technology Guangzhou 510006China

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and

关键词： Avatar blockchain metaverses multi-agent deep reinforcement learning transformer UAVs

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An Optimization Approach of IoD Deployment for Optimal Coverage Based on Radio Frequency Model

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 2627-2647页

作者： Tarek Sheltami Gamil Ahmed Ansar Yasar Computer Engineering Department Interdisciplinary Research Center of Smart Mobility and LogisticsKing Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Transportation research Institute(IMOB) Hasselt UniversityHasselt3500Belgium

Recently,Internet of Drones(IoD)has garnered significant attention due to its widespread applications.However,deploying IoD for area coverage poses numerous limitations and challenges.These include interference between neighboring drones,the need for directional antennas,and altitude restrictions for drones.These challenges necessitate the development of efficient solutions.This research paper presents a cooperative decision-making approach for an efficient IoDdeployment to address these challenges effectively.The primary objective of this study is to achieve an efficient IoDdeployment strategy thatmaximizes the coverage regionwhile minimizing interference between neighboring drones.In deployment problem,the interference increases as the number of deployed drones increases,resulting in bad quality of communication.On the other hand,deploying a few drones cannot satisfy the coverage demand.To accomplish this,an enhanced version of a concise population-based meta-heuristic algorithm,namely Improved Particle SwarmOptimization(IPSO),is applied.The objective function of IPSO is defined based on the coverage probability,which is primarily influenced by the characteristics of the antennas and drone altitude.A radio frequency(RF)model is derived to evaluate the coverage quality,considering both Line of Sight(LOS)and Non-Line of Sight(NLOS)down-link coverage probabilities for ground communication.It is assumed that each drone is equipped with a directional antenna to optimize coverage in a given region.Extensive simulations are conducted to assess the effectiveness of the proposed approach.Results demonstrate that the proposed method achieves maximum coverage with minimum transmission power.Furthermore,a comparison is made against Collaborative Visual Area Coverage Approach(CVACA),and a game-based approach in terms of coverage quality and convergence speed.The simulation results reveal that our approach outperforms both CVACA and the gamebased schemes in terms of coverage and convergence speed.Comparisons validate the sup

关键词： IoD line of sight optimal deployment IPSO RF model

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Demand Prediction Based Slice Reconfiguration Using Dueling Deep Q-Network

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China communications 2022年第5期19卷 267-285页

作者： Wanqing Guan Haijun Zhang School of Computer&communication Engineering University of Science and Technology BeijingBeijing 100083China Beijing Engineering and Technology research center for convergence Networks and Ubiquitous services Institute of Artificial IntelligenceUniversity of Science and Technology BeijingBeijing 100083China

To satisfy diversified service demands of vertical industries,network slicing enables efficient resource allocation of a common infrastructure by creating isolated logical networks.However,uncertainty and dynamics of service demands will cause performance degradation.Due to operation costs and resource constraints,it is challenging to maintain high quality of user experience while obtaining high revenue for service providers(SPs).This paper develops an optimal and fast slice reconfiguration(OFSR)framework based on reinforcement learning,where a novel scheme is proposed to offer optimal decisions for reconfiguring diverse slices.A demand prediction model is proposed to capture changes in resource requirements,based on which the OFSR scheme is triggered to determine whether to perform slice reconfiguration.Considering the large state and action spaces generated from uncertain service time and resource requirements,deep dueling architecture is adopted to improve the convergence rate.Extensive simulations validate the effectiveness of the proposed framework in achieving higher long-term revenue for SPs.

关键词： network slicing slice reconfiguration reinforcement learning resource allocation

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Ultra Dense Satellite-Enabled 6G Networks:Resource Optimization and Interference Management

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中国通信:英文版 2023年第10期20卷 262-275页

作者： Xiangnan Liu Haijun Zhang Min Sheng Wei Li Saba Al-Rubaye Keping Long School of Computer&communication Engineering University of Science and Technology BeijingBeijing 100083China Beijing Engineering and Technology research center for convergence Networks and Ubiquitous services University of Science and Technology BeijingBeijing 100083China School of Telecommunications Engineering Xidian UniversityXi’an 710071China State Radio Monitoring center of China Beijing 100037China School of Aerospace Transport and Manufacturing Cranfield UniversityMK430ALUK

With the evolution of the sixth generation(6G)mobile communication technology,ample attention has gone to the integrated terrestrial-satellite networks.This paper notes that four typical application scenarios of integrated terrestrial-satellite networks are integrated into ultra dense satellite-enabled 6G networks architecture.Then the subchannel and power allocation schemes for the downlink of the ultra dense satellite-enabled 6G heterogeneous networks are introduced.Satellite mobile edge computing(SMEC)with edge caching in three-layer heterogeneous networks serves to reduce the link traffic of networks.Furthermore,a scheme for interference management is presented,involving quality-of-service(QoS)and co-tier/cross-tier interference constraints.The simulation results show that the proposed schemes can significantly increase the total capacity of ultra dense satellite-enabled 6G heterogeneous networks.

关键词： satellite-enabled 6G networks network architecture resource optimization interference management

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An Efficient and Spam-Robust Proximity Measure Between communication Entities

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Journal of Computer Science & Technology 2013年第2期28卷 394-400页

作者： Joo Hyuk Jeon Jihwan Song Jeong Eun Kwon Yoon Joon Lee Man Ho Park Myoung Ho Kim Department of Computer Science Korea Advanced Institute of Science and Technology Biz Solution Team SK Telecom Information Technology R&D Center Mobile communication convergence research Team Electronics and Telecommunications Research Institute

Electronic communication service providers are obliged to retain communication data for a certain amount of time by their local laws. The retained communication data or the communication logs are used in various applications such as crime detection, viral marketing, analytical study, and so on. Many of these applications rely on effective techniques for analyzing communication logs. In this paper, we focus on measuring the proximity between two communication entities, which is a fundamental and important step toward further analysis of communication logs, and propose a new proximity measure called ESP （Efficient and Spam-Robust Proximity measure）. Our proposed measure considers only the （graph- theoretically） shortest paths between two entities and gives small values to those between spam-like entities and others. Thus, it is not only computationally efficient but also spam-robust. By conducting several experiments on real and synthetic datasets, we show that our proposed proximity measure is more accurate, computationally efficient and spam-robust than the existing measures in most cases.

关键词： proximity closeness spam electronic communication

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Toward High-Performance Delta-Based Iterative Processing with a Group-Based Approach

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Journal of Computer Science & Technology 2022年第4期37卷 797-813页

作者：余辉姜新宇赵进齐豪张宇廖小飞刘海坤毛伏兵金海 National Engineering research center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China service Computing Technology and System laboratory Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Many systems have been built to employ the delta-based iterative execution model to support iterative algorithms on distributed platforms by exploiting the sparse computational dependencies between data items of these iterative algorithms in a synchronous or asynchronous approach. However, for large-scale iterative algorithms, existing synchronous solutions suffer from slow convergence speed and load imbalance, because of the strict barrier between iterations;while existing asynchronous approaches induce excessive redundant communication and computation cost as a result of being barrier-free. In view of the performance trade-off between these two approaches, this paper designs an efficient execution manager, called Aiter-R, which can be integrated into existing delta-based iterative processing systems to efficiently support the execution of delta-based iterative algorithms, by using our proposed group-based iterative execution approach. It can efficiently and correctly explore the middle ground of the two extremes. A heuristic scheduling algorithm is further proposed to allow an iterative algorithm to adaptively choose its trade-off point so as to achieve the maximum efficiency. Experimental results show that Aiter-R strikes a good balance between the synchronous and asynchronous policies and outperforms state-of-the-art solutions. It reduces the execution time by up to 54.1% and 84.6% in comparison with existing asynchronous and the synchronous models, respectively.

关键词： iterative algorithm delta-based execution model efficiency

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Joint Power-Trajectory-Scheduling Optimization in A Mobile UAV-Enabled Network via Alternating Iteration

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China communications 2022年第1期19卷 136-152页

作者： Xiaohan Qi Minxin Yuan Qinyu Zhang Zhihua Yang communications Engineering research center Harbin Institute of TechnologyShen zhen 518055China Peng Cheng laboratory Shenzhen 518052China

This work focuses on an unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) system based on device-to-device(D2D) communication. In this system, the UAV exhibits caching,computing and relaying capabilities to periodically provide specific service to cellular users and D2D receiver nodes in the appointed time slot. Besides, the D2D transmitter can provide additional caching services to D2D receiver to reduce the pressure of the UAV. Note that communication between multi-type nodes is mutually restricted and different links share spectrum resources. To achieve an improved balance between different types of node, we aim to maximize the overall energy efficiency while satisfying the quality-of-service requirements of the cellular nodes.To address this problem, we propose an alternating iteration algorithm to jointly optimize the scheduling strategies of the user, transmitting power of the UAV and D2D-TX nodes, and UAV trajectory. The successive convex approximation, penalty function, and Dinkelbach method are employed to transform the original problem into a group of solvable subproblems and the convergence of the method is proved. Simulation results show that the proposed scheme performs better than other benchmark algorithms, particularly in terms of balancing the tradeoff between minimizing UAV energy consumption and maximizing throughput.

关键词： UAV MEC network D2D joint optimization energy efficiency

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An Intelligent Admission Control Scheme for Dynamic Slice Handover Policy in 5G Network Slicing

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Computers, Materials & Continua 2023年第5期75卷 4611-4631页

作者： Ratih Hikmah Puspita Jehad Ali Byeong-hee Roh Department of AI convergence Network Ajou UniversitySuwon16499Korea Department of Computer Engineering and Department of AI Convergence NetworkAjou UniversitySuwon16499Korea

5G use cases,for example enhanced mobile broadband(eMBB),massive machine-type communications(mMTC),and an ultra-reliable low latency communication(URLLC),need a network architecture capable of sustaining stringent latency and bandwidth requirements;thus,it should be extremely flexible and dynamic.Slicing enables service providers to develop various network slice architectures.As users travel from one coverage region to another area,the callmust be routed to a slice thatmeets the same or different expectations.This research aims to develop and evaluate an algorithm to make handover decisions appearing in 5G sliced networks.Rules of thumb which indicates the accuracy regarding the training data classification schemes within machine learning should be considered for validation and selection of the appropriate machine learning strategies.Therefore,this study discusses the network model’s design and implementation of self-optimization Fuzzy Qlearning of the decision-making algorithm for slice handover.The algorithm’s performance is assessed by means of connection-level metrics considering the Quality of service(QoS),specifically the probability of the new call to be blocked and the probability of a handoff call being dropped.Hence,within the network model,the call admission control(AC)method is modeled by leveraging supervised learning algorithm as prior knowledge of additional capacity.Moreover,to mitigate high complexity,the integration of fuzzy logic as well as Fuzzy Q-Learning is used to discretize state and the corresponding action spaces.The results generated from our proposal surpass the traditional methods without the use of supervised learning and fuzzy-Q learning.

关键词： 5g network slice fuzzy q-Learning slice handover

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