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Towards Intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Framework

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IEEE Network 2025年

作者： Li, Xiaolong Wei, Jianhao Wang, Haidong Dong, Li Chen, Ruoyang Yi, Changyan Cai, Jun Niyato, Dusit Shen, Xuemin Hunan Provincial General University Key Laboratory of IoT Intelligent Sensing and Distributed Collaborative Optimization Hunan University of Technology and Business Xiangjiang Laboratory Changsha410205 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China University of Concordia Department of Electrical and Computer Engineering Montreal Canada Nanyang Technological University College of Computing and Data Science Singapore Singapore University of Waterloo Department of Electrical and Computer Engineering Canada

In intelligent transportation systems (ITSs), integrating pedestrians and vehicles into traffic management models is essential for developing realistic and safe solutions. However, current systems often fail to simulate complex, real-world scenarios due to the absence of a comprehensive digital twin framework across diverse traffic environments and effective modeling of pedestrian-vehicle interactions. In this article, we propose a surveillance video-assisted federated digital twin (SV-FDT) framework to enhance ITSs by incorporating pedestrians and vehicles into the control loop. SV-FDT improves computational efficiency and communication performance by transmitting only semantic data and agent parameters, rather than raw video streams. The proposed framework adopts three-layer architecture and constructs detailed pedestrian-vehicle interaction models using multi-source traffic surveillance videos. The three-layer architecture includes: (i) an end layer that collects surveillance videos from multiple sources;(ii) an edge layer that performs self-supervised semantic segmentation to extract interactions, converts them into executable traffic codes, and generates local digital twin systems (LDTSs) for regional traffic modeling;and (iii) a cloud layer that integrates LDTSs into a real-time global digital twin model. Key design considerations, challenges, and practical implementation guidelines are discussed for SV-FDT, and a testbed evaluation is used to show that SV-FDT improves traffic flow, reduces mirroring delay, and enhances recognition accuracy and system efficiency compared to traditional terminal-server frameworks. Finally, we outline open challenges and potential directions for future research in digital twin-enabled ITS. © 1986-2012 IEEE.

关键词： Air traffic control

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Privacy-preserving Intelligent Resource Allocation for Federated Edge Learning in Quantum Internet

arXiv

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arXiv 2022年

作者： Xu, Minrui Niyato, Dusit Yang, Zhaohui Xiong, Zehui Kang, Jiawen Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering Nanyang Technological University Singapore The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310007 China Hangzhou310007 China Zhejiang Laboratory Hangzhou31121 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore 487372 Singapore The School of Automation Guangdong University of Technology China Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Waterloo ON N2L 3G1 Canada

Federated edge learning (FEL) is a promising paradigm of distributed machine learning that can preserve data privacy while training the global model collaboratively. However, FEL is still facing model confidentiality issues due to eavesdropping risks of exchanging cryptographic keys through traditional encryption schemes. Therefore, in this paper, we propose a hierarchical architecture for quantum-secured FEL systems with ideal security based on the quantum key distribution (QKD) to facilitate public key and model encryption against eavesdropping attacks. Specifically, we propose a stochastic resource allocation model for efficient QKD to encrypt FEL keys and models. In FEL systems, remote FEL workers are connected to cluster heads via quantum-secured channels to train an aggregated global model collaboratively. However, due to the unpredictable number of workers at each location, the demand for secret-key rates to support secure model transmission to the server is unpredictable. The proposed systems need to efficiently allocate limited QKD resources (i.e., wavelengths) such that the total cost is minimized in the presence of stochastic demand by formulating the optimization problem for the proposed architecture as a stochastic programming model. To this end, we propose a federated reinforcement learning-based resource allocation scheme to solve the proposed model without complete state information. The proposed scheme enables QKD managers and controllers to train a global QKD resource allocation policy while keeping their private experiences local. Numerical results demonstrate that the proposed schemes can successfully achieve the cost-minimizing objective under uncertain demand while improving the training efficiency by about 50% compared to state-of-the-art schemes. © 2022, CC BY.

关键词： Stochastic systems

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Molecular Communication for Quorum Sensing Inspired Cooperative Drug Delivery

arXiv

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arXiv 2023年

作者： Fang, Yuting Johnston, Stuart T. Faria, Matt Huang, Xinyu Eckford, Andrew W. Evans, Jamie Department of Electrical and Electronic Engineering School of Mathematics and Statistics and Systems Biology Laboratory Department of Biomedical Engineering Department of Biomedical Engineering ARC Centre of Excellence in Covergent Bio-Nano Science and Technology The University of Melbourne ParkvilleVIC3010 Australia School of Engineering Australian National University CanberraACT2600 Australia Department of Electrical Engineering and Computer Science York University TorontoONM3J 1P3 Canada

A cooperative drug delivery system is proposed, where quorum sensing (QS), a density-dependent bacterial behavior coordination mechanism, is employed by synthetic bacterium-based nanomachines (B-NMs) for controllable drug delivery. In our proposed system, drug delivery is only triggered when there are enough QS molecules, which in turn only happens when there are enough B-NMs. This makes the proposed system can be used to achieve a high release rate of drug molecules from a high number of B-NMs when the population density of B-NMs may not be known. Analytical expressions for i) the expected activation probability of the B-NM due to randomly-distributed B-NMs and ii) the expected aggregate absorption rate of drug molecules due to randomly-distributed QS activated B-NMs are derived. Analytical results are verified by particle-based simulations. The derived results can help to predict and control the impact of environmental factors (e.g. diffusion coefficient and degradation rate) on the absorption rate of drug molecules since rigorous diffusion-based molecular channels are considered. Our results show that the activation probability at the B-NM increases as this B-NM is located closer to the center of the B-NM population and the aggregate absorption rate of the drug molecules non-linearly increases as the population density increases. © 2023, CC BY.

关键词： Aggregates

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Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization

arXiv

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arXiv 2024年

作者： Deng, Xiumei Li, Jun Wei, Kang Shi, Long Xiong, Zeihui Ding, Ming Chen, Wen Jin, Shi Poor, H. Vincent School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China The Department of Computing Hong Kong Polytechnic University 999077 Hong Kong The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Data61 CSIRO SydneyNSW2015 Australia Department of Electronics Engineering Shanghai Jiao Tong University Shanghai200240 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Department of Electrical and Computer Engineering Princeton University NJ08544 United States

Adaptive moment estimation (Adam), as a Stochastic Gradient Descent (SGD) variant, has gained widespread popularity in federated learning (FL) due to its fast convergence. However, federated Adam (FedAdam) algorithms suffer from a threefold increase in uplink communication overhead compared to federated SGD (FedSGD) algorithms, which arises from the necessity to transmit both local model updates and first and second moment estimates from distributed devices to the centralized server for aggregation. Driven by this issue, we propose a novel sparse FedAdam algorithm called FedAdam-SSM, wherein distributed devices sparsify the updates of local model parameters and moment estimates and subsequently upload the sparse representations to the centralized server. To further reduce the communication overhead, the updates of local model parameters and moment estimates incorporate a shared sparse mask (SSM) into the sparsification process, eliminating the need for three separate sparse masks. Theoretically, we develop an upper bound on the divergence between the local model trained by FedAdam-SSM and the desired model trained by centralized Adam, which is related to sparsification error and imbalanced data distribution. By minimizing the divergence bound between the model trained by FedAdam-SSM and centralized Adam, we optimize the SSM to mitigate the learning performance degradation caused by sparsification error. Additionally, we provide convergence bounds for FedAdam-SSM in both convex and non-convex objective function settings, and investigate the impact of local epoch, learning rate and sparsification ratio on the convergence rate of FedAdam-SSM. Experimental results show that FedAdam-SSM outperforms baselines in terms of convergence rate (over 1.1× faster than the sparse FedAdam baselines) and test accuracy (over 14.5% ahead of the quantized FedAdam baselines). Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Performance Analysis of OMA/NOMA-aided Satellite Communication Networks: A Stochastic Geometry Approach

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IEEE Transactions on Communications 2024年

作者： Li, Kecheng Wang, Jun Hou, Tianwei Li, Anna Yue, Xinwei Liu, Yuanwei Chen, Wei School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom KU Leuven Leuven3001 Belgium School of Computing and Communications Lancaster University LancasterLA1 4WA United Kingdom Key Laboratory of Information and Communication Systems Ministry of Information Industry China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

The increasing quality of service requirements and demand for satellite services necessitate higher data rates, spectral efficiency, and stability in satellite communication networks. Therefore, this paper investigates the non-orthogonal multiple access (NOMA) assisted satellite communication networks, where multiple users are uniformly distributed over a spherical hat according to the homogeneous Poisson point process (HPPP). Based on the characteristics of HPPP, we analyze the distance distributions of users. To evaluate the performance of the proposed networks, we first derive the closed-form expressions and approximated expressions of the outage probability (OP) for paired NOMA users. To obtain more insights into the proposed networks, the ergodic rate and diversity orders for paired NOMA users are also derived. Spectral efficiency is derived for NOMA and orthogonal multiple access (OMA) assisted satellite communication networks. Our analytical results demonstrate that the diversity order of the proposed networks is all one. Numerical results confirm that: 1) compared to OMA, the proposed NOMA-assisted satellite network demonstrates superior outage performance and spectral efficiency, especially in the higher power regimes;2) fading factors have negligible effects on OP and spectral efficiency;and 3) under certain target rates for both near and far users, the outage performance of far users in NOMA-assisted satellite communication networks is superior to that of near users. © 2024 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Satellite communication systems

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An Artificial Intelligence Solution for Energy Management in Smart Grid

An Artificial Intelligence Solution for Energy Management in...

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Artificial Intelligence & Green Energy (ICAIGE), IEEE International Conference on

作者： Ferdaws Ben Naceur Sana Toumi Chokri Ben Salah Mohamed Ali Mahjoub LATIS- Laboratory of Advanced Technology and Intelligent Systems National School of Engineers of Sousse University of Sousse Sousse Tunisia Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse Tunisia Department of Electrical Engineering Laboratory of Automation Electrical Systems and Environment (LASEE) National School of Engineers University of Monastir Monastir Tunisia Institut de Recherche Dupuy de Lôme (UMR CNRS 60 27 IRDL) Brest France Laboratory of Automation Electrical Systems and Environment (LASEE) ISSAT Sousse University of Sousse Sousse Tunisia

This paper deals with an intelligent decision-making strategy of energy management for a smart grid within an element storage in order to reduce the system cost and improve its reliability. Indeed, the integration of distributed energy storage devices allows the customers to store energy and reuse at a later time or delayed some appliances operation according to a time-varying price signal. The adopted strategy is based on an artificial intelligence control algorithm “fuzzy logic”. The main objective is to determine the appropriate source (grid or battery) used for meeting the consumer. The decision solution shows the system reliability where the switches are synchronized. Switches draw electricity from the grid for both load and battery when price is low and in contrary when electricity price is high, we find a variation's price correspondance with the switch drawing electricity from the battery. The obtained results are verified carrying out Matlab simulation using real data such as the electricity price (Price (t)) and the battery state of charge (SOC (t)).

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Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

arXiv

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arXiv 2023年

作者： Ferrag, Mohamed Amine Friha, Othmane Kantarci, Burak Tihanyi, Norbert Cordeiro, Lucas Debbah, Merouane Hamouda, Djallel Al-Hawawreh, Muna Choo, Kim-Kwang Raymond Technology Innovation Institute Masdar City Abu Dhabi9639 United Arab Emirates Badji Mokhtar-Annaba University B.P.12 Annaba23000 Algeria School of Electrical Engineering and Computer Science University of Ottawa OttawaON Canada The University of Manchester Manchester United Kingdom Khalifa University of Science and Technology P O Box 127788 Abu Dhabi United Arab Emirates Labstic Laboratory Department of Computer Science Guelma University B.P. 401 24000 Algeria School of Information Technology Deakin University Australia The Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX78249-0631 United States

The deployment of the fifth-generation (5G) wireless networks in the Internet of Everything (IoE) applications and future networks (e.g., sixth-generation (6G) networks) has raised several operational challenges and limitations, for example in terms of security and privacy. Edge learning is an emerging approach to training models across distributed clients while ensuring data privacy. Such an approach when integrated in future network infrastructures (e.g., 6G) can potentially solve challenging problems such as resource management and behavior prediction. However, edge learning (including distributed deep learning) is known to be susceptible to tampering and manipulation. This survey article provides a holistic review of the extant literature focusing on edge learning-related vulnerabilities and defenses for 6G-enabled Internet of Things (IoT) systems. Existing machine learning approaches for 6G-IoT security and machine learning-associated threats are broadly categorized based on learning modes, namely: centralized, federated, and distributed. Then, we provide an overview of enabling emerging technologies for 6G-IoT intelligence. We also provide a holistic survey of existing research on attacks against machine learning and classify threat models into eight categories, namely: backdoor attacks, adversarial examples, combined attacks, poisoning attacks, Sybil attacks, byzantine attacks, inference attacks, and dropping attacks. In addition, we provide a comprehensive and detailed taxonomy and a comparative summary of the state-of-the-art defense methods against edge learning-related vulnerabilities. Finally, as new attacks and defense technologies are realized, new research and future overall prospects for 6G-enabled IoT are discussed. © 2023, CC BY.

关键词： Internet of things

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Meta-RetinaNet for Few-shot Object Detection 31

Meta-RetinaNet for Few-shot Object Detection

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31st British Machine Vision Conference, BMVC 2020

作者： Li, Shaoqi Song, Wenfeng Li, Shuai Hao, Aimin Qin, Hong State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Peng Cheng Laboratory Shenzhen518055 China Department of Computer Science Stony Brook University Stony Brook United States

Few shot object detection (FSD) is gaining popularity, enhanced by the deep learning methods in recent years. Meanwhile, meta-learning has achieved great success in few-shot image classification benefitting from its adaptive capability corresponding to a suite of tasks. Yet, most object detection models are based on deep neural networks (DNNs), and they are prone to the overfitting problem due to limited samples available during training. To adapt the learned prior knowledge more effectively to new tasks, this paper proposes a novel Meta-RetinaNet for FSD, which avoids a biased meta-learner and improves its generalization ability. It employs a Meta Coefficient Learner (MCL) trained by the Balanced Loss (BL) to augment the DNNs. Specifically, the MCL adapts to tasks by the product of pre-trained convolution weights and coefficient vectors densely for all the convolutional layers, such that it could adequately transfer the learned knowledge to new tasks (while overcoming the overfitting problem) by training fewer parameters. In addition, the BL expedites the training of a Meta-RetinaNet by balancing the performance of a host of tasks, and it also retains stable performance for new tasks. Our experiments showcase the effectiveness of our method, which achieves the state-of-the-art performance on the multiple settings of Pascal VOC and COCO datasets. © 2020. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Object detection

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Machine Learning-based Energy-Aware Offloading in Edge-Cloud Vehicular Networks

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Procedia computer science 2021年 191卷 328-336页

作者： Leila Ismail Huned Materwala Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates

A vehicular network underpinned by the 3-tier vehicle-edge-cloud infrastructure enables an efficient and safer travel experience. The compute-intensive vehicular applications are often offloaded to the edge and/or cloud servers to enhance the applications’ Quality of Services (QoS). The underlying edge-cloud servers consume a high among of energy. Consequently, it becomes crucial to optimizing energy consumption in the offloading process. Current energy-efficient offloading strategies in 2-tier vehicle-edge infrastructure, do not account for cloud computing energy consumption. In this paper, we address this void by proposing a machine learning-based energy-aware offloading algorithm, which optimizes the energy of the edge-cloud computing platform. The offloading strategy is enabled by the Support Vector Machine (SVM) regression model machine learning algorithm used for the edge-cloud power prediction. The experimental results show that the proposed algorithm is a promising approach in energy savings.

关键词： Cloud computing Computation offloading Edge computing Energy-efficiency Machine learning Queuing theory Support Vector Machine (SVM) Vehicular network

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Adaptive Estimation for Environmental Monitoring Using an Autonomous Underwater Vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

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IEEE Conference on Decision and Control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

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