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Ubiquitous communication (Ucom), International Conference on

作者： Xulong Li Xinyi Xu Jiahao Huo Wei Huangfu Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China

Unmanned aerial vehicles (UAVs) are often used to assist internet of things (IoT) networks to collect data from IoT devices more securely and efficiently by virtue of their flexibility and low deployment cost. In this paper, we consider a UAV-assisted IoT network in which a large number of IoT devices are deployed along with eavesdroppers. The communication UAV and the jamming UAV cooperating to collect data from IoT devices. In order to minimize the age of information (AoI) of data from IoT devices at the communication UAV, this paper introduces reinforcement learning (RL) solutions. Considering the number of IoT devices is not fixed in the coverage area of communication UAV, which incurs the dimensional variations for the observation and action space, so we propose an effective twin delayed deep deterministic policy gradient (TD3) algorithm based on attention mechanisms to design the policy and critic networks. Simulation results show that the proposed algorithm is able to significantly reduce the average AoI of data from IoT devices at the communication UAV compared to baseline algorithms.

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Machine Learning Based Study of QoE Metrics in *** Live Streaming

Machine Learning Based Study of QoE Metrics in *** Live Stre...

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IEEE Symposium on Network Operations and Management

作者： Frank Loh Kathrin Hildebrand Florian Wamser Stefan Geißler Tobias Hoßfeld Chair of Communication Networks University of Würzburg Germany School of Computer Science and Information Technology Lucerne University of Applied Sciences and Arts Switzerland

Video streaming generates most network traffic in today’s Internet. For that reason, video on demand streaming is researched heavily in recent years, and many traffic monitoring mechanisms, flow and stream models, and models to predict the user perceived quality are well established. However, the quickly growing live streaming sector is not considered in these Quality of Experience models and not even the relation between network traffic and playback quality has been studied so far, forming a gap in literature. For that reason, we investigate *** streaming as one of the largest live streaming platforms based on a large dataset and investigate the possibility to predict live streaming quality based on uplink request information. We apply approaches that are well studied for on demand streaming to predict quality changes, playback quality, and video interruption events as the most important metrics impairing user perceived quality. In this context, we answer whether these models are suitable for live streaming, if small changes are sufficient for satisfactory prediction results, or if fundamental changes and new models are required.

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Joint Waveform and Passive Beamforming Design for IRS-Aided ISAC via Deep Unfolding 16

Joint Waveform and Passive Beamforming Design for IRS-Aided ...

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16th International Conference on Wireless communications and Signal Processing, WCSP 2024

作者： Zhang, Jifa Liu, Mingqian Tang, Jie Zhao, Nan Niyato, Dusit Wang, Xianbin School of Information and Communication Engineering Dalian University of Technology Dalian China Xidian University State Key Laboratory of Integrated Service Networks Xi'an China School of Electronic and Information Engineering South China University of Technology Guangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore Western University Department of Electrical and Computer Engineering LondonONN6A 5B9 Canada

ISBN: (纸本)9798350390643

Integrated sensing and communication (ISAC) has become a promising technique to alleviate the spectrum congestion via sharing the same spectrum for communication and sensing. Nevertheless, many ISAC schemes encounter the challenges of high complexity. In this paper, we propose a dual-functional waveform design scheme for reconfigurable intelligent surface (RIS) aided ISAC based on deep unfolding learning. Specifically, the weighted sum of multi-user interference energy and waveform discrepancy is minimized via the joint waveform and phase-shift design. We first develop an alternating direction method of multipliers (ADMM) based iterative algorithm to handle the non-convex optimization problem. Then, we develop a deep unfolding neural network (NN), named ADMM-NET, which unfolds the proposed ADMM-based iterative algorithm to a layer-wise architecture. Simulation results verify the effectiveness of the proposed ADMM-NET. © 2024 IEEE.

关键词： Convex optimization

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Multi-UAV Trajectory Optimization for Fair Coverage Considering No-fly Zones

Multi-UAV Trajectory Optimization for Fair Coverage Consider...

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Ubiquitous communication (Ucom), International Conference on

作者： Jianfang Zhang Xinyi Xu Yaxi Liu Wei Huangfu Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China

Optimizing trajectories of unmanned aerial vehicles base stations (UAV-BSs) is crucial in maximizing the usage of UAV. However, existing works often fail to consider the mobility of ground users. Therefore, we propose a multi-UAV trajectory optimization problem that maximizes fair coverage of dynamic users within no-fly zones. We present an efficient UAV trajectory optimization algorithm based on improved Soft Actor Critic (SAC) for solving complex constrained problems. Finally, simulation experiments demonstrate the feasibility of our proposed algorithm.

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Near-Far Field Three-Stage Beam Training for RIS-Assisted Wideband OFDM communications

Near-Far Field Three-Stage Beam Training for RIS-Assisted Wi...

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IEEE Conference on Wireless communications and Networking

作者： Zhichao Cheng Shupei Zhang Shu Fu Boya Di School of Electronics Engineering and Computer Science Peking University Beijing China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Department of Microelectronics and Communication Engineering Chongqing University Chongqing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Large-scale reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for signal strength enhancement and coverage extension in 6G networks. However, as the antenna scale and the bandwidth increase, large-scale RIS-assisted wideband orthogonal frequency division multiplexing (OFDM) communication systems face new challenges due to the near-field range expansion and the beam split effect, complicating the acquisition of channel state information (CSI). To tackle these challenges, in this paper, we present a wideband beam training scheme by utilizing the beam split effect to bypass the CSI acquisition. Specifically, by analyzing the beam split effect in RIS-assisted OFDM communication systems, we propose a beam-split-aware codebook capable of covering both the near and far fields. The coverage of codewords in the proposed codebook is expanded benefiting from the beam split effect, leading to fewer codewords compared to conventional narrow-band codebooks. Utilizing such a codebook, a three-stage beam training mechanism is performed to obtain the optimal codeword with low time overhead, thereby facilitating the beamforming. Simulation results demonstrate that the proposed scheme outperforms existing codebook-based beam training schemes in terms of training overhead and sum rate in the hybrid near-far field.

关键词： Training Upper bound Array signal processing OFDM Simulation Reconfigurable intelligent surfaces Broadband antennas Wideband Faces Channel state information

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Analytical Model for the Energy Efficiency in Low Power IoT Deployments

Analytical Model for the Energy Efficiency in Low Power IoT ...

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IEEE Conference on Network Softwarization (NetSoft)

作者： Tobias Hoß feld Simon Raffeck Frank Loh Stefan Geiß ler Chair of Communication Networks University of W&#x00FC rzburg Institute of Computer Science W&#x00FC rzburg Germany

ISBN: (数字)9781665406949

ISBN: (纸本)9781665406956

The recent rise of the Internet of Things (IoT) has given way to numerous challenges and research questions. One of the most critical issues in the area of low powered devices is the question of energy efficiency. Here, technologies like LoRa or Zigbee emerged, promising low power consumption while maintaining adequate performance. However, even when using these tailor made technologies, several configuration aspects need to be taken into account to provide high performance, energy efficient operation. To this end, we propose a generic model to compute the energy efficiency of wireless sensors under the assumption of perfect CSMA/CA channel access. We present numerical results for a typical LoRa device and highlight extensions towards other channel access mechanisms. Finally, we apply Kleinrock’s power metric to obtain ideal system configurations for varying load parameters.

关键词： Performance evaluation Wireless communication Wireless sensor networks Power demand Zigbee Energy efficiency Sensors

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Optimising Cloud Computing Performance with an Enhanced Dynamic Load Balancing Algorithm for Superior Task Allocation

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IEEE Access 2024年

作者： Zhanuzak, Raiymbek Ala'anzy, Mohammed Alaa Othman, Mohamed Algarni, Abdulmohsen SDU University Department of Computer Science Almaty Kazakhstan Universiti Putra Malaysia UPM Serdang Department of Communication Technology and Networks Selangor D.E. 43400 Malaysia Selangor D.E. 43400 Malaysia King Khalid University Department of Computer Science Abha61421 Saudi Arabia

Cloud computing, particularly within the Infrastructure as a Service (IaaS) model, faces significant challenges in workload distribution due to limited resource availability and virtual machines (VMs). Efficient task allocation and load balancing are crucial to avoiding overloading or under-loading scenarios that can lead to execution delays or machine failures. This paper presents an Enhanced Dynamic Load Balancing (EDLB) algorithm designed to optimise task scheduling and resource allocation in cloud environments. Unlike benchmark algorithms that rely on static VM selection or post-hoc relocation of cloudlets, the EDLB algorithm dynamically identifies optimal cloudlet placement in real-time. Our approach proactively allocates cloudlets to VMs based on current system states and Service Level Agreement (SLA) deadlines, thereby preemptively addressing potential SLA violations. Additionally, if a VM cannot meet a cloudlet's deadline, the algorithm redirects the cloudlet to a secondary data centre and reconfigures CPU resources among VMs to ensure optimal allocation. Evaluations using CloudSim simulations demonstrate that the EDLB algorithm achieves substantial average improvements over benchmark algorithm and the-state-of-the-art algorithm, including a 59.46% reduction in total makespan, a 12.70% reduction in average makespan, a 22.46% reduction in execution time, and a 3.10% increase in resource utilisation. Furthermore, the EDLB algorithm enhances load balancing by 46.46%. These results highlight the effectiveness of the EDLB algorithm in addressing critical load balancing issues and surpassing existing methods. This research contributes to the field by introducing a novel approach that significantly improves performance metrics and operational efficiency in cloud computing environments. © 2024 IEEE.

关键词： Infrastructure as a service (IaaS)

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A Review of Pseudonym Change Strategies for Location Privacy Preservation Schemes in Vehicular networks

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ACM Computing Surveys 2025年第8期57卷 1-37页

作者： Benarous, Leila Zeadally, Sherali Boudjit, Saadi Mellouk, Abdelhamid Department of Computer science Amar Telidji University of Laghouat Laghouat Algeria LISSI-Tinc-NET Laboratory University of Paris-Est Creteil Créteil France College of Communication and Information University of Kentucky Lexington United States Department of Electronic Engineering Kyung Hee University Seoul Korea Republic of Litis Lab Intelligent Transportation Systems Group University of Rouen Normandy Mont-Saint-Aignan France Lissi Laboratory Department of Networks and Telecom University Paris-Est Créteil Val de Marne Créteil France

Location privacy protection in vehicular networks has been a primary priority to ensure because of its direct impact on human physical safety. Leakage and violation of road users' location privacy may be perilous beyond simple curiosity as it may scale to cyberstalking and tracking. This may lead to road users being subjected to mental and physical distress, from blackmailing to targeted advertising, and trap planning. The results of tracking may be fatal to road users. Over the past 17 years, researchers have been actively investigating location privacy protection mechanisms for vehicular networks. Numerous research results have been published that examined the use of pseudonym change strategies, which have been considered as the most appropriate solution that maintains the tradeoff between ease of application, good protection level, and the correct functionality of the network. We evaluate, classify, and analyze pseudonym change strategies aimed at location privacy preserving solutions for vehicular networks and discuss their performance. We also review the simulation tools used for evaluating pseudonym change techniques used in location privacy preserving solutions for the vehicular environment. The results of this survey will help researchers in this field to develop more robust, efficient, and cost-effective location privacy protection schemes in the future. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Differential privacy

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In-situ manipulation of wireless link with reinforcement-learningdriven programmable metasurface in indoor environment

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Journal of Information and Intelligence 2023年第3期1卷 217-227页

作者： Jiawen Xu Rong Zhang Jie Ma Hanting Zhao Lianlin Li School of Electronic Engineering and Computer Science Peking UniversityBeijing 100871China State Key Laboratory of Advanced Optical Communication Systems and Networks School of ElectronicsPeking UniversityBeijing 100871China

It is of great importance to control flexibly wireless links in the modern society,especially with the advent of the Internet of Things(IoT),fifth-generation communication(5G),and ***,we have witnessed that programmable metasurface(PM)or reconfigurable intelligent surface(RIS)has become a key enabling technology for manipulating flexibly the wireless link;however,one fundamental but challenging issue is to online design the PM's control sequence in a complicated wireless environment,such as the real-world indoor ***,we propose a reinforcement learning(RL)approach to online control of the PM and thus in-situ improve the quality of the underline wireless *** designed an inexpensive one-bit PM working at around 2.442 GHz and developed associated RL algorithms,and demonstrated experimentally that it is capable of enhancing the quality of commodity wireless link by a factor of about 10 dB and beyond in multiple scenarios,even if the wireless transmitter is in the glancing angle of the PM in the realworld indoor ***,we also prove that our RL algorithm can be extended to improve the wireless signals of receivers in dual-receiver *** faithfully expect that the presented technique could hold important potentials in future wireless communication,smart homes,and many other fields.

关键词： Programmable metasurface Reconfigurable intelligent surface Reinforcement learning Wireless link

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Increasing Depth of Neural networks for Life-Long Learning

SSRN

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SSRN 2023年

作者： Wozniak, Michal Kozal, Jedrzej Department of Systems and Computer Networks Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wybrzeze Wyspianskiego 27 Wroclaw50-370 Poland

PurposeWe propose a novel method for continual learning based on the increasing depth of neural networks. This work explores whether extending neural network depth may be beneficial in a life-long learning *** propose a novel approach based on adding new layers on top of existing ones to enable the forward transfer of knowledge and adapting previously learned representations. We employ a method of determining the most similar tasks for selecting the best location in our network to add new nodes with trainable parameters. This approach allows for creating a treelike model, where each node is a set of neural network parameters dedicated to a specific task. The Progressive Neural Network concept inspires the proposed method. Therefore, it benefits from dynamic changes in network structure. However, Progressive Neural Network allocates a lot of memory for the whole network structure during the learning process. The proposed method alleviates this by adding only part of a network for a new task and utilizing a subset of previously trained weights. At the same time, we may retain the benefit of PNN, such as no forgetting guaranteed by design, without needing a memory *** on Split CIFAR and Split Tiny ImageNet show that the proposed algorithm is on par with other continual learning methods. In a more challenging setup with a single computer vision dataset as a separate task, our method outperforms Experience *** is compatible with commonly used computer vision architectures and does not require a custom network structure. As an adaptation to changing data distribution is made by expanding the architecture, there is no need to utilize a rehearsal buffer. For this reason, our method can be used for sensitive applications where data privacy must be considered. © 2023, The Authors. All rights reserved.

关键词： Knowledge management

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