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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Umran, Samir M. Lu, SongFeng Abduljabbar, Zaid Ameen Lu, Zhi Feng, Bingyan Zheng, Lu Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Iraqi Cement State Company Ministry of Industry and Minerals Baghdad10011 Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China University of Basrah College of Education for Pure Sciences Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Industrial Internet Research Institute Wuhan Huazhong Numerical Control Co. Ltd Wuhan430074 China South-Central University for Nationalities College of Computer Science Wuhan430074 China

ISBN: (纸本)9798350346558

The industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network;provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality. © 2022 IEEE.

关键词： Digital storage

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A Survey of Model Predictive control Methods for Traffic Signal control

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IEEE/CAA Journal of Automatica Sinica 2019年第3期6卷 623-640页

作者： Bao-Lin Ye Weimin Wu keyu Ruan Lingxi Li Tehuan Chen Huimin Gao Yaobin Chen IEEE the School of Mechanical and Electrical Engineering Jiaxing University the State Key Laboratory of Industrial Control Instituteof Cyber-Systems and Control Technology Zhejiang University the Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University Indianapolis Mechanical Engineering and Mechanics Ningbo University

Enhancing traffic efficiency and alleviating(even circumventing) traffic congestion with advanced traffic signal control(TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control(MPC) has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations,and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.

关键词： Autonomous vehicles coordination control mixed integer programming model predictive control system decomposition traffic flow models traffic signal control

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Lightweight Trust Model with Machine Learning scheme for secure privacy in VANET

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Procedia computer Science 2021年 194卷 45-59页

作者： Muhammad Haleem Junejo Ab Al-Hadi Ab Rahman Riaz Ahmed Shaikh Kamaludin Mohamad Yusof Dileep Kumar Imran Memon Faculty of Electrical Engineering Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia State Key Laboratory of Industrial Control Technology College of Control Science & Engineering Zhejiang University Hangzhou china Department of computer science bahria university Karachi campus sindh Pakistan

A vehicular ad hoc network (VANETs) is transforming public transport into a safer wireless network, increasing its safety and efficiency. The VANET consists of several nodes which include RSU (Roadside Units), vehicles, traffic signals, and other wireless communication devices that are communicating sensitive information in a network. Nevertheless, security threats are increasing day by day because of dependency on network infrastructure, dynamic nature, and control technologies used in VANET. The security threats could be addressed widely by using machine learning and artificial intelligence on the road transport nodes. In this paper, a comparison of trust and cryptography was presented based on applications and security requirements of VANET.

关键词： VANET Trust Model Machine Learning

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Mandatory Lane Change Strategy in VANET Based on Coordinated Stackelberg Game Model 32

Mandatory Lane Change Strategy in VANET Based on Coordinated...

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32nd Chinese control and Decision Conference, CCDC 2020

作者： Nan, DIng Maria, Akira Xianghua, Meng Nasibullah Wu, Di Dalian University of Technology Department of Computer Science and Technology Dalian116024 China Dalian University of Technology Department of Control Theory and Control Engineering Dalian116024 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Dalian University of Technology Dalian Liaoning116024 China

ISBN: (纸本)9781728158549

Despite the unavailability of intersections and turns, expressway has several lane closures and ramps. Mandatory lane change takes place when the geographical condition requires the vehicles to shift lane, such as, exits, on and off-ramps. Due to a high-speed regulation on the expressway, it is more difficult and riskier to shift lane, therefore better strategies for semi-automation vehicles have to be implemented for the users' safety. This paper studies the efficiency of the Stackelberg based algorithm for safer mandatory lane maneuver, yet still with appropriate speed. The paper also utilizes model predictive controller (MPC) to predict the motions of surrounding vehicles a few time steps in the future to model the probabilities of subject vehicles to shift. Then we consider the costs, MPC and the game to output the best time for subject vehicle to shift. Finally, we simulate the algorithm to determine if it is efficient by comparing it to other existing algorithms and breaking the simulation down to various indicators. © 2020 IEEE.

关键词： Game theory

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An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments

arXiv

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arXiv 2022年

作者： Han, Zhichao Wu, Yuwei Li, Tong Zhang, Lu Pei, Liuao Xu, Long Li, Chengyang Ma, Changjia Xu, Chao Shen, Shaojie Gao, Fei State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

As a core part of autonomous driving systems, motion planning has received extensive attention from academia and industry. However, real-time trajectory planning capable of spatial-temporal joint optimization is challenged by nonholonomic dynamics, particularly in the presence of unstructured environments and dynamic obstacles. To bridge the gap, we propose a real-time trajectory optimization method that can generate a high-quality whole-body trajectory under arbitrary environmental constraints. By leveraging the differential flatness property of car-like robots, we simplify the trajectory representation and analytically formulate the planning problem while maintaining the feasibility of the nonholonomic dynamics. Moreover, we achieve efficient obstacle avoidance with a safe driving corridor for unmodelled obstacles and signed distance approximations for dynamic moving objects. We present comprehensive benchmarks with State-of-the-Art methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes for the research community.1 Copyright © 2022, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Distributed control of Multiple-Bus Microgrid With Paralleled Distributed Generators

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IEEE/CAA Journal of Automatica Sinica 2019年第3期6卷 676-684页

作者： Bo Fan Jiangkai Peng Jiajun Duan Qinmin Yang Wenxin Liu IEEE the State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University the Smart Microgrid and Renewable Technology (SMRT) Research Laboratory Department of Electrical and Computer Engineering Lehigh University the Global Energy Interconnection Research Institute North America (GEIRINA)

A microgrid is hard to control due to its reduced inertia and increased uncertainties. To overcome the challenges of microgrid control, advanced controllers need to be *** this paper, a distributed, two-level, communication-economic control scheme is presented for multiple-bus microgrids with each bus having multiple distributed generators(DGs) connected in parallel. The control objective of the upper level is to calculate the voltage references for one-bus subsystems. The objectives of the lower control level are to make the subsystems' bus voltages track the voltage references and to enhance load current sharing accuracy among the local DGs. Firstly, a distributed consensusbased power sharing algorithm is introduced to determine the power generations of the subsystems. Secondly, a discrete-time droop equation is used to adjust subsystem frequencies for voltage reference calculations. Finally, a Lyapunov-based decentralized control algorithm is designed for bus voltage regulation and proportional load current sharing. Extensive simulation studies with microgrid models of different levels of detail are performed to demonstrate the merits of the proposed control scheme.

关键词： Coordinate control decentralized control multiple-bus microgrid paralleled distributed generations power sharing algorithm

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Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach

arXiv

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arXiv 2023年

作者： Shi, Zhaoyuan Lu, Huabing Xie, Xianzhong Yang, Helin Huang, Chongwen Cai, Jun Ding, Zhiguo The Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology Dalian116024 China Chongqing Key Lab of Computer networks and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China The Department of Information and Communication Engineering School of Informatics Xiamen University Xiamen361005 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated, where non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH). The problem of joint control of the RIS’s amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic available energy of RIS. To tackle this non-convex problem, a cascaded deep learning algorithm namely long short-term memory-deep deterministic policy gradient (LSTM-DDPG) is designed. First, an advanced LSTM based algorithm is developed to predict users’ dynamic communication state. Then, based on the prediction results, a DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix of the RIS. Finally, simulation results verify the accuracy of the prediction of the proposed LSTM algorithm, and demonstrate that the LSTM-DDPG algorithm has a significant advantage over other benchmark algorithms in terms of communication success ratio performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy harvesting

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S2R-CMI: A Robust Deep Reinforcement Learning method based on counterfactual estimation and state importance evaluation under additive noise disturbance

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Neurocomputing 2025年 648卷

作者： Zhenyuan Chen Zhi Zheng Wenjun Huang Xiaomin Lin College of Computer and Cyber Security Fujian Normal University FuZhou 350117 China College of Control Science and Engineering Zhejiang University Hangzhou 310027 China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou 310027 China

The development of Deep Reinforcement Learning (DRL) overcomes the limitations of traditional reinforcement learning in discrete spaces, extending its applications to scenarios in continuous spaces. However, disturbance commonly encountered in real-world environments poses significant threats to the performance of DRL algorithms, potentially leading to erroneous decision-making by agents and severe consequences. To address this issue, this paper proposes a Robust Deep Reinforcement Learning (RDRL) method named S2R-CMI, which aims to mitigate the impact of additive noise in the state space on DRL performance without requiring prior knowledge of the noise disturbance. Specifically, a state-based and reward-based conditional mutual information mechanism is designed to dynamically capture state importance and estimate its contribution to rewards. To address the lack of counterfactual data during training, a counterfactual label estimation method is proposed to approximate the counterfactual reward distribution while avoiding local optima during network training. State importance is then evaluated to quantify the impact of disturbance on states. Finally, we validate the proposed method in five scenarios: Cartpole, LunarLander-Discrete, LunarLander-Continuous, Build-Marine, and Half-Cheetah, under state disturbance. Experimental results demonstrate that S2R-CMI significantly enhances the robustness of DRL algorithms. Furthermore, we conduct experiments in some scenarios without state disturbance, and the results indicate that the method also achieves strong performance, further verifying its superiority and generalization capabilities.

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Cache-Assisted Content Delivery for NOMA-Based Satellite-Edge-Terminal Cooperation Networks

Cache-Assisted Content Delivery for NOMA-Based Satellite-Edg...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Chao Fang Hang Xu Yingshan Li Haofei Xie Shu-Ming Tseng Zheng Yang Deze Zeng Faculty of Information Technology Beijing University of Technology Beijing China Purple Mountain Laboratories Nanjing China Key Laboratory of Industrial Internet of Things & Networked Control Chongqing University of Posts and Telecommunications Chongqing China Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Normal University Fuzhou China School of Computer Science China University of Geosciences Wuhan China

To satisfy differentiated service requirements of delay-sensitive and computing-intensive tasks in satellite communication networks, we propose a cache-assisted low-latency content distribution scheme in multi-cell multi-carrier nonorthogonal multiple access (MCMC-NOMA)-based satellite-edge-terminal cooperation environments. In the paper, a delay minimization problem is formulated to achieve optimal content distribution by jointly optimizing the allocation of subchannels and transmit power, which is decomposed and solved in each slot by using many-to-one matching and successive convex approximation, respectively. Simulation results show that the proposed model significantly improve network latency and content distribution in comparison to its existing counterparts in cloud-edge-terminal cooperation networks.

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Risk Identification Method for Smuggled Frozen Meat in Urban Public Safety Awareness and Early Warning

Risk Identification Method for Smuggled Frozen Meat in Urban...

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Intelligent Communication and Networking (ICN), International Conference on

作者： Xueru Li Yan Zhang Lingjie Liu Ye Tian Yue Zhang College of Computer Science and Technology Harbin Engineering University Harbin China China Industrial Control Systems Cyber Emergency Response Team Beijing China Environment and Food and Drug Crime Investigation Detachment of Hangzhou Public Security Bureau Hangzhou China Hangzhou Institute of Technology Xidian University Hangzhou China Key Laboratory of Peace-Building Big Data of Zhejiang Province Zhejiang China

With the continuous growth in demand for meat and frozen products in the cold chain market, illegally smuggled frozen goods have quickly entered the market due to their low prices, leading to a series of food safety issues. This article proposes a method for identifying the risks of smuggled frozen meat for urban public safety awareness using the YOLOv7 model. The YOLOv7 model is used to detect data captured at various traffic checkpoints, obtaining spatiotemporal information of frozen goods vehicles. This information is then combined with map data to generate vehicle trajectory information. The trajectory similarity between the frozen vehicle trajectories and the specialized database of frozen transportation routes is analyzed using the EDwP distance algorithm. The EDwP algorithm employs a threshold-free approach, dynamically interpolating using the concept of projection, and automatically adapting to non-uniform sampling rates to address the issue of inconsistent trajectory data sampling rates. The experimental results of the YOLOv7-EDwP algorithm were verified by verifying the alerts generated in the recent period through on-site verification by a third-party inspection agency, with a recognition accuracy of 90.77%. This holds significant significance in the detection and warning of smuggled frozen meat.

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