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arXiv 2023年

作者： Hu, Guojie Wu, Qingqing Xu, Kui Si, Jiangbo Al-Dhahir, Naofal The College of Communication Engineering Rocket Force University of Engineering Xi’an710025 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The College of Communications Engineering The Army of Engineering University Nanjing210007 China The Integrated Service Networks Lab of Xidian University Xi’an710100 China The Department of Electrical and Computer Engineering The University of Texas at Dallas RichardsonTX75080 United States

Movable antenna (MA) array is a novel technology recently developed where positions of transmit/receive antennas can be flexibly adjusted in the specified region to reconfigure the wireless channel and achieve a higher capacity. In this letter, we, for the first time, investigate the MA array-assisted physical-layer security where the confidential information is transmitted from a MA array-enabled Alice to a single-antenna Bob, in the presence of multiple single-antenna and colluding eavesdroppers. We aim to maximize the achievable secrecy rate by jointly designing the transmit beamforming and positions of all antennas at Alice subject to the transmit power budget and specified regions for positions of all transmit antennas. The resulting problem is highly non-convex, for which the projected gradient ascent (PGA) and the alternating optimization methods are utilized to obtain a high-quality suboptimal solution. Simulation results demonstrate that since the additional spatial degree of freedom (DoF) can be fully exploited, the MA array significantly enhances the secrecy rate compared to the conventional fixed-position antenna (FPA) array. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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Investigation of Shared-Bike Demand Using Data Analytics 8

Investigation of Shared-Bike Demand Using Data Analytics

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8th IEEE International Smart Cities Conference, ISC2 2022

作者： Bencekri, Madiha Founoun, Adnane Haqiq, Abdelkrim Hayar, Aawatif Hassan First University of Settat Faculty of Sciences and Techniques Computer Networks Mobility and Modeling Laboratory: IR2M Settat26000 Morocco University of Seoul Dept. Transportation Engineering and Smart City Seoul Korea Republic of Hassan Ii University Ritm Lab Ced ENSEM/TICDev R&d Centre Casablanca Morocco

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

Sustainable development commitments are of concern to the city's decision-makers as well as significantly impacting the existing transportation systems. The concept of the smart city and precisely the component of smart mobility centered on the user and soft transport comes to support this approach of transformation which aims at the low carbon city. Similarly, reducing carbon emissions is one of the main objectives of a smart city, thereby comes the focus on enhancing eco-friendly and active transportation means, for instance, the shared-bike system. The mode benefits from the technology implemented within the smart city concept. Seoul Government has implemented a shared-bike program 'Ttareungyi' in 2015, within the big vision of 'low carbon green growth'. However, the program struggles to achieve the targeted demand. Therefore, this study is using data analytics to help enlighten decision-makers about the shared-bike system and provide insights for future development. The research was conducted to investigate the influence of the built environment, including slope, land use mix, and centrality parameters, the influence of transport infrastructure, including bike and transit infrastructure, and the influence of the socio-economic characteristics, including population, retail number, car ownership, and job offers on bike demand. And to predict bike demand based on the mentioned variables using the ridge regression method. Results revealed that dock number, population density, and car ownership have a significant positive impact on biking demand, while slope has a significant negative impact. In contradiction to the research hypothesis, land use mix revealed a weak impact on biking demand using random forest, and a negative influence using ridge regression. © 2022 IEEE.

关键词： Land use

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CRS-Diff: Controllable Remote Sensing Image Generation with Diffusion Model

arXiv

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arXiv 2024年

作者： Tang, Datao Cao, Xiangyong Hou, Xingsong Jiang, Zhongyuan Liu, Junmin Meng, Deyu The School of Computer Science and Technology the Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China The School of Information and Communications Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The School of Cyber Engineering Xidian University Shaanxi Xi’an710049 China The School of Mathematics and Statistics the Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Guangdong Guangzhou510555 China

The emergence of generative models has revolutionized the field of remote sensing (RS) image generation. Despite generating high-quality images, existing methods are limited in relying mainly on text control conditions, and thus do not always generate images accurately and stably. In this paper, we propose CRS-Diff, a new RS generative framework specifically tailored for RS image generation, leveraging the inherent advantages of diffusion models while integrating more advanced control mechanisms. Specifically, CRS-Diff can simultaneously support text-condition, metadata-condition, and image-condition control inputs, thus enabling more precise control to refine the generation process. To effectively integrate multiple condition control information, we introduce a new conditional control mechanism to achieve multi-scale feature fusion, thus enhancing the guiding effect of control conditions. To our knowledge, CRS-Diff is the first multiple-condition controllable RS generative model. Experimental results in single-condition and multiple-condition cases have demonstrated the superior ability of our CRS-Diff to generate RS images both quantitatively and qualitatively compared with previous methods. Additionally, our CRS-Diff can serve as a data engine that generates high-quality training data for downstream tasks, e.g., road extraction. The code is available at https://***/Sonettoo/CRS-Diff. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Robust Reinforcement Learning for Stochastic Linear Quadratic Control with Multiplicative Noise ⁎

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IFAC-PapersOnLine 2021年第7期54卷 240-243页

作者： Bo Pang Zhong-Ping Jiang Control and Networks Lab Department of Electrical and Computer Engineering Tandon School of Engineering New York University Six Metrotech Center Brooklyn NY 11201 USA

This paper studies the robustness of reinforcement learning for discrete-time linear stochastic systems with multiplicative noise evolving in continuous state and action spaces. As one of the popular methods in reinforcement learning, the robustness of policy iteration is a longstanding open issue for the stochastic linear quadratic regulator (LQR) problem with multiplicative noise. A solution in the spirit of small-disturbance input-to-state stability is given, guaranteeing that the solutions of the policy iteration algorithm are bounded and enter a small neighborhood of the optimal solution, whenever the error in each iteration is bounded and small. In addition, a novel off-policy multiple-trajectory optimistic least-squares policy iteration algorithm is proposed, to learn a near-optimal solution of the stochastic LQR problem directly from online input/state data, without explicitly identifying the system matrices. The efficacy of the proposed algorithm is supported by rigorous convergence analysis and numerical results on a second-order example.

关键词： Reinforcement learning control Stochastic optimal control problems Data-based control Robustness analysis Input-to-state stability

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Joint Task Scheduling and Container Image Caching in Edge Computing

arXiv

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arXiv 2023年

作者： Mou, Fangyi Tang, Zhiqing Lou, Jiong Guo, Jianxiong Wang, Wenhua Wang, Tian Institute of Artificial Intelligence and Future Networks Beijing Normal University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College China

In Edge Computing (EC), containers have been increasingly used to deploy applications to provide mobile users services. Each container must run based on a container image file that exists locally. However, it has been conspicuously neglected by existing work that effective task scheduling combined with dynamic container image caching is a promising way to reduce the container image download time with the limited bandwidth resource of edge nodes. To fill in such gaps, in this paper, we propose novel joint Task Scheduling and Image Caching (TSIC) algorithms, specifically: 1) We consider the joint task scheduling and image caching problem and formulate it as a Markov Decision Process (MDP), taking the communication delay, waiting delay, and computation delay into consideration;2) To solve the MDP problem, a TSIC algorithm based on deep reinforcement learning is proposed with the customized state and action spaces and combined with an adaptive caching update algorithm. 3) A real container system is implemented to validate our algorithms. The experiments show that our strategy outperforms the existing baseline approaches by 23% and 35% on average in terms of total delay and waiting delay, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Edge computing

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Traffic-Aware Fast Reroute Mechanism Exploiting Disjoint Subpaths for Named Data networks

Traffic-Aware Fast Reroute Mechanism Exploiting Disjoint Sub...

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IEEE International Conference on Communications (ICC)

作者： Boyang Zhou Chunming Wu Qiang Yang Zhen Zhu Yinghui Nie Wenjie Yu Zhejiang Lab Intelligent Networks Institute Hangzhou China College of Computer Science Zhejiang University Hangzhou China College of Electrical Engineering Zhejiang University Hangzhou China School of Intelligent Science and Technology Hangzhou Institute for Advanced Study UCAS Hangzhou China

The popularized named data network (NDN) needs a fast reroute (FRR) to improve resilience in dealing with the norm of link faults. The resilience of the existing disjoint arborescences and paths based approaches is restricted in reroute options, due to the limited number of disjoint end-to-end routes available. Such restrictions can be weakened by exploiting of the disjoint subpaths (DSP) between two intermediate nodes in the primary path (PP), as what DSP can link together is not only the two ends. In this paper, we propose a novel NDN based traffic-aware fast reroute (TA-FRR) mechanism that exploits a new resilient subgraph (RSG) route involving multiple DSPs. TA-FRR proactively constructs an RSG with scalable computational complexity to maximize the resilience and to avoid a too high reroute stretch. In the RSG, TA-FRR efficiently computes the shortest detour path for the disconnected components actively identified according to packet arrival timeouts. We evaluated the performance of TA-FRR in the ndnSIM simulator using synthsized Internet topologies with different degrees of link faults. The results show that TA-FRR can efficiently recover serious link faults in PP with a scalable RSG construction and outperforms the existing approaches with a significantly higher resilience and the maximum stretch similar to the arborescences approach.

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Hybrid Deep Learning based Attack Detection and Classification Model on IoT Environment

Hybrid Deep Learning based Attack Detection and Classificati...

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International Conference on Electronics, Communication and Computing Technologies (ICECCT)

作者： Jaya Dipti Lal Shahnawaz Ayoub Prashant D Hakim S. Prabagar Vijay Kumar Dwivedi Mohit Tiwari Department of Electronics and Communication Engineering Shri G S Institute of Technology and Science Indore Madhya Pradesh India Department of Computer Science Shri Venkateshwara University Gujrala Amroha Uttar Pradesh India Department of Information Technology Thakur College of Science and Commerce Mumbai Maharashtra India Department of Information Science Engineering Networks and IoT Lab School of Computer Science and Engineering & Information Science Presidency University Bengaluru Karnataka India Department of Mathematics Vishwavidyalaya Engineering College Ambikapur Chhattisgarh India Department of Computer Science and Engineering Bharati Vidyapeeth's College of Engineering Delhi India

The Internet of Things (IoT) is becoming an active research area because of its largescale challenges and implementation. But security is the major concern while seeing the dramatic expansion in its applications and size. It is challenging to independently put security mechanism in all the IoT devices and upgrade it according to newer threats. Furthermore, machine learning (ML) techniques could better apply the massive quantity of data produced by IoT devices. Thus, several Deep Learning (DL) based algorithms were introduced for detecting attacks in IoT. Therefore, this study develops a galactic swarm optimization with Deep Learning based Attack Detection and Classification (GSODL-ADC) Model in IoT Environment. The presented GSODL-ADC technique concentrates on the identification of attacks in the IoT environment. The presented GSODL-ADC technique utilizes deep autoencoder (DAE) as a classifier model which properly recognizes the attacks in the IoT environment. Followed by this, the GSO approach is utilized for the optimum hyperparameter adjustments of the DAE model, which leads to enhanced attack detection efficacy. The experimental evaluation of the GSODL-ADC algorithm is tested against benchmark dataset. The obtained experimental values signify the betterment of the GSODL-ADC technique for attack recognition purposes.

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Reinforcement Learning for Linear Continuous-time Systems: an Incremental Learning Approach

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IEEE/CAA Journal of Automatica Sinica 2019年第2期6卷 433-440页

作者： Tao Bian Zhong-Ping Jiang Bank of America Merrill Lynch IEEE the Control and Networks Lab Department of Electrical and Computer Engineering Tandon School of Engineering New York University

In this paper, we introduce a novel reinforcement learning(RL) scheme for linear continuous-time dynamical systems. Different from traditional batch learning algorithms,an incremental learning approach is developed, which provides a more efficient way to tackle the on-line learning problem in realworld applications. We provide concrete convergence and robust analysis on this incremental-learning algorithm. An extension to solving robust optimal control problems is also given. Two simulation examples are also given to illustrate the effectiveness of our theoretical result.

关键词： Adaptive optimal control robust dynamic programming value iteration(Ⅵ)

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Deep Reinforcement Learning for Energy-Efficient Fresh Data Collection in Rechargeable UAV-assisted IoT networks

Deep Reinforcement Learning for Energy-Efficient Fresh Data ...

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IEEE Conference on Wireless Communications and Networking

作者： Mengjie Yi Xijun Wang Juan Liu Yan Zhang Ronghui Hou School of Cyber Engineering Xidian University Xi’an China School of Electronics and Information Technology Sun Yat-sen University Guangzhou China School of Electrical Engineering and Computer Science Ningbo University Zhejiang China State Key Lab of Integrated Service Networks Information Science Institute Xidian University Xi’an China

The unmanned aerial vehicle (UAV) can act as the edge server in delay-sensitive monitoring for data collection and processing in the Internet of things (IoT) networks due to its flexibility and low operational cost. One of its major disadvantages is the limited battery level. This paper focuses on a problem with the rechargeable UAV-assisted energy-efficient and fresh data collection in the IoT networks. In particular, the UAV takes off from the initial position to collect data packets from sensor nodes (SNs) in the IoT networks and needs to reach the final position at a given time. Some charging stations (CSs) are in the IoT networks, which can recharge the UAV by the wireless power transfer technique to keep the UAV’s energy level from falling below the threshold energy. To minimize the weighted sum of the average age of information (AoI) and the average recharging price, we design a Markov Decision Process (MDP) to determine the UAV’s flight trajectory, the scheduling of SNs, and energy recharging. The MDP is then solved using a rechargeable UAV-assisted data collection algorithm based on dueling double deep Q-networks (D3QN). Numerous simulations show that the proposed D3QN algorithm can reduce the weighted sum of the average AoI and the average recharging price more effectively than the baseline algorithms.

关键词： Simulation Wireless power transfer Data collection Autonomous aerial vehicles Energy efficiency Scheduling Trajectory

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Discrete-Time Distributed Optimization for Linear Uncertain Multi-Agent Systems

Discrete-Time Distributed Optimization for Linear Uncertain ...

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IEEE Conference on Decision and Control

作者： Tong Liu Michelangelo Bin Ivano Notarnicola Thomas Parisini Zhong-Ping Jiang Department of Electrical and Computer Engineering Control and Networks Lab Tandon School of Engineering New York University Brooklyn NY USA Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy Department of Electrical and Electronic Engineering Imperial College London London UK Department of Engineering and Architecture University of Trieste Trieste Italy KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

The distributed optimization algorithm proposed by J. Wang and N. Elia in 2010 has been shown to achieve linear convergence for multi-agent systems with single-integrator dynamics. This paper extends their result, including the linear convergence rate, to a more complex scenario where the agents have heterogeneous multi-input multi-output linear dynamics and are subject to external disturbances and parametric uncertainties. Disturbances are dealt with via an internal-model-based control design, and the interaction among the tracking error dynamics, average dynamics, and dispersion dynamics is analyzed through a composite Lyapunov function and the cyclic small-gain theorem. The key is to ensure a small enough stepsize for the convergence of the proposed algorithm, which is similar to the condition for time-scale separation in singular perturbation theory.

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