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356 results on '"Sheng, Zhengguo"'

1. Robust Neural Control for Distributed Formation of UAVs Under Uncertain Disturbances

Author

Li, Chenwei, Xie, Ailing, Zhou, Jianshan, Tian, Daxin, Duan, Xuting, Sheng, Zhengguo, Zhao, Dezong, Lu, Caixia, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Qu, Yi, editor, Gu, Mancang, editor, Niu, Yifeng, editor, and Fu, Wenxing, editor

Published
2024
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3. A Blockchain based Federated Learning for Message Dissemination in Vehicular Networks

Author

Ayaz, Ferheen, Sheng, Zhengguo, Tian, Daxin, and Guan, Yong Liang

Subjects
Computer Science - Cryptography and Security
Abstract

Message exchange among vehicles plays an important role in ensuring road safety. Emergency message dissemination is usually carried out by broadcasting. However, high vehicle density and mobility usually lead to challenges in message dissemination such as broadcasting storm and low probability of packet reception. This paper proposes a federated learning based blockchain-assisted message dissemination solution. Similar to the incentive-based Proof-of-Work consensus in blockchain, vehicles compete to become a relay node (miner) by processing the proposed Proof-of-Federated-Learning (PoFL) consensus which is embedded in the smart contract of blockchain. Both theoretical and practical analysis of the proposed solution are provided. Specifically, the proposed blockchain based federated learning results in more number of vehicles uploading their models in a given time, which can potentially lead to a more accurate model in less time as compared to the same solution without using blockchain. It also outperforms the other blockchain approaches for message dissemination by reducing 65.2% of time delay in consensus, improving at least 8.2% message delivery rate and preserving privacy of neighbor vehicle more efficiently. The economic model to incentivize vehicles participating in federated learning and message dissemination is further analyzed using Stackelberg game model., Comment: Submitted to IEEE Transactions on Vehicular Technology

Published
2021

4. UAV Integrated Internet-of-Vehicle (IoV) System for Flooding Monitoring and Rescue

Author

Liu, Bo, Ayaz, Ferheen, Anh, Hồ Diệp, Edwards, Ollie, Zeng, Yong, Sheng, Zhengguo, Duan, Xuting, Tian, Daxin, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Fu, Wenxing, editor, Gu, Mancang, editor, and Niu, Yifeng, editor

Published
2023
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5. Vehicle-Edge-Cloud Integrated Communication and Computing Testbed for Intelligent Transportation Systems

Author

Fu, Yanjin, Zhou, Jianshan, Duan, Xuting, Sheng, Zhengguo, Tian, Daxin, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Fu, Wenxing, editor, Gu, Mancang, editor, and Niu, Yifeng, editor

Published
2023
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6. Cooperative Adaptive Cruise Control and Decision Making for Fuel-Efficient Vehicle Platooning

Author

Jia, Xiaomeng, Zhou, Jianshan, Duan, Xuting, Sheng, Zhengguo, Tian, Daxin, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Fu, Wenxing, editor, Gu, Mancang, editor, and Niu, Yifeng, editor

Published
2023
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7. Dynamic Computation Offloading for Platooning Vehicles: A Stochastic Game-Theoretic Approach

Author

Liu, Shiyi, Zhou, Jianshan, Tian, Daxin, Ren, Chenghao, Duan, Xuting, Sheng, Zhengguo, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Fu, Wenxing, editor, Gu, Mancang, editor, and Niu, Yifeng, editor

Published
2023
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15. Secure Data Sharing for Consortium Blockchain-Enabled Vehicular Social Networks

Author

Cui, Mingming, Han, Dezhi, Liu, Han, Li, Kuan-Ching, Tang, Mingdong, Chang, Chin-Chen, Ayaz, Ferheen, Sheng, Zhengguo, and Guan, Yong Liang

Abstract

Data sharing in Vehicular Social Networks (VSNs) is an essential road service that assists vehicle driving and promotes intelligent transportation applications. In VSNs, vehicles regularly collect and upload valuable data to share with other vehicles. Data encryption can be employed during data uploading and sharing to prevent malicious tampering and privacy disclosure. However, existing data-sharing schemes lack security, have high overhead in obtaining decrypted data, and show low trust in the central authority controlling the entire network. To facilitate data sharing in VSNs, this paper proposes a new scheme using consortium blockchain to realize secure data sharing. Nodes in the blockchain invoke smart contracts and implement the location-based Speculative Byzantine Fault Tolerance (LSBFT) to accomplish data-sharing transactions among vehicles. The scheme not only ensures the security of vehicle information but also protects the privacy of the shared data. Security analysis demonstrates that the proposed scheme can resist attacks and has shown transaction fairness, data confidentiality, non-repudiation, and traceability. Simulation results show that the scheme has higher sharing efficiency and less time to reach a consensus in the data storage process.

Published
2024
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19. Cooperative wireless networks

Author

Sheng, Zhengguo and Leung, Kin

Subjects
621.382
Abstract

In the last few years, there have been a lot of interests in wireless ad-hoc networks as they have remarkable commercial and military applications. Such wireless networks have the benefit of avoiding a wired infrastructure. However, signal fading is a severe problem for wireless communications particularly for the multi-hop transmissions in the ad-hoc networks. Cooperative communication has been proposed as an effective way to improve the quality of wireless links. The key idea is to have multiple wireless devices at different locations cooperatively share their antenna resources and aid each other’s transmission. In this thesis, we develop effective algorithms for cooperative wireless ad-hoc networks, and the performance of cooperative communication is measured based on various criteria, such as cooperative region, power ratio and end-to-end performance. For example, the proposed interference subtraction and supplementary cooperation algorithms can significantly improve network throughput of a multi-hop routing. Comprehensive simulations are carried out for all the proposed algorithms and performance analysis, providing quantitative evidence and comparison over other schemes. In our view, the new cooperative communication algorithms proposed in this research enable wireless ad-hoc networks to improve radio unreliability and meet future application requirements of high-speed and high-quality services with high energy efficiency. The acquired new insights on the network performance of the proposed algorithms can also provide precise guidelines for efficient designs of practical and reliable communications systems. Hence these results will potentially have a broad impact across a range of related areas, including wireless communications, network protocols, radio transceiver design and information theory.

Published
2011
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26. Efficient Robust Control of Mixed Platoon for Improving Fuel Economy and Ride Comfort

Author

Zhang, Peiyu, Tian, Daxin, Zhou, Jianshan, Duan, Xuting, Sheng, Zhengguo, Zhao, Dezong, and Cao, Dongpu

Abstract

The emergence of connected and automated vehicle technology has improved the operational efficiency of mixed traffic systems. This paper studies a two-tier trajectory optimization problem for mixed platooning to improve fuel efficiency, ride comfort, and operational safety during vehicle operations. The proposed model follows a two-tier control logic to plan the trajectory of platooning vehicles with three objectives, including minimizing fuel consumption, maximizing ride comfort, and enhancing the anti-disturbance performance of the platoon. The first is the planning tier, which aims to design the optimal trajectory for Connected and Automated Vehicles (CAVs) based on the optimal fuel consumption and comfort and obtain the expected acceleration curve of CAV. The second is the control tier, which aims to ensure the safe operation of platooning vehicles in the presence of uncertain disturbances in real time. Specifically, we propose a robust tube MPC control method, which dynamically adjusts the CAV acceleration according to the reference trajectory obtained by the planning tier to resist the effects of uncertain disturbances, and the tracking behaviour of Human-Driven Vehicles (HDVs) is offline solved by the robust optimal velocity model. Finally, we design simulation experiments to verify the effectiveness of the proposed two-tier optimization framework. The experimental results show the effectiveness and advantages of the two-tier framework in terms of fuel economy, ride comfort, and robustness against different noise disturbances.

Published
2024
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28. A Survey on Mobile Sensing Based Mood-Fatigue Detection for Drivers

Author

Tu, Wei, Wei, Lei, Hu, Wenyan, Sheng, Zhengguo, Nicanfar, Hasen, Hu, Xiping, Ngai, Edith C.-H., Leung, Victor C. M., Akan, Ozgur, Series editor, Cao, Jiannong, Series editor, Coulson, Geoffrey, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin (Sherman), Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert, Series editor, Bellavista, Paolo, Series editor, Leon-Garcia, Alberto, editor, Lenort, Radim, editor, Holman, David, editor, Staš, David, editor, Krutilova, Veronika, editor, Wicher, Pavel, editor, Cagáňová, Dagmar, editor, Špirková, Daniela, editor, Golej, Julius, editor, and Nguyen, Kim, editor

Published
2016
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29. Unicast Routing Protocol Based on Attractor Selection Model for Vehicular Ad-Hoc Networks

Author

Tian, Daxin, Zheng, Kunxian, Zhou, Jianshan, Sheng, Zhengguo, Ni, Qiang, Wang, Yunpeng, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Hsu, Ching-Hsien, editor, Wang, Shangguang, editor, Zhou, Ao, editor, and Shawkat, Ali, editor

Published
2016
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48. Distributed Multi-Agent Reinforcement Learning for Collaborative Path Planning and Scheduling in Blockchain-Based Cognitive Internet of Vehicles

Author

Chang, Huigang, Liu, Yiming, and Sheng, Zhengguo

Abstract

The collaborative path planning and scheduling can overcome the limitations of single vehicle intelligence to obtain a globally optimal decision strategy in cognitive Internet of Vehicles (CIoVs). The collaboration of vehicles necessitates the exchange of environmental and decision information, generating massive collaborative computing tasks with strict latency requirements. Leveraging mobile edge computing (MEC) technology, computing tasks can be processed near the vehicles to reduce latency. However, traffic congestion and computational load imbalance seriously affect traffic efficiency and computational latency. In hybrid driving scenarios, it is challenging to fulfill the diverse service requirements of vehicles with different intelligence levels. Moreover, non-collaborative tend to result in traffic congestion due to vehicle aggregation effects, while centralized solutions lack flexibility and have high computational complexity. To address these concerns, a distributed multi-agent reinforcement learning (DMARL) algorithm is proposed for collaborative path planning and scheduling in a blockchain-based collaboration framework. In this framework, we model the communication, traffic situation and task processing of the system and formulate a joint optimization problem to minimize both travel time and computation latency. Last, we convert the scheduling problem for different types of vehicles into Markov decision processes (MDPs) and propose Q-learning-based DMARL algorithm to achieve proactive load balancing of both road infrastructures and MEC nodes (MECNs). Simulation results demonstrate that the proposed approach outperforms the comparison schemes in terms of load balance indexes of roads and MECNs, travel time, and computation latency.

Published
2024
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49. A Novel Hierarchical Tree-DCNN Structure for Unbalanced Data Diagnosis in Microelectronic Manufacturing Process

Author

Zeng, Yong, Mei, Yanfang, Hu, Yueming, and Sheng, Zhengguo

Abstract

The quality of flexible integrated circuit substrates (FICSs) is critical to the reliability of various electronic products, making intelligent defect measurement essential for efficient manufacturing and cost-saving. However, existing solutions for substrate defect diagnosis heavily rely on human visual interpretation, which leads to poor efficiency and a high error rate. A novel vision-based detection system consisting of a multiscale imaging module and a hierarchical structure based on the deep convolution neural network (DCNN) is proposed in this article. Rapid and accurate fault diagnosis can be enabled for high-density FICS, and various defects could be located and classified in a coarse-to-fine resolution. Specifically, a new mechanism of hierarchical decision based on DCNNs is built for FICS fault diagnosis, wherein the challenge of unbalanced data is addressed in the network learning process to reach a good trade-off between detection accuracy and speed. The substantial experiments and effectiveness comparison by using the typical methods on three categories of FICS and their corresponding eight-type faults reveal that the proposed system could facilitate the solution of substrate fault measurement and achieve high accuracy and efficiency, which could provide essential information of FICS to divide its industrial acceptance quality level.

Published
2024
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50. Energy-Efficiency Optimization With Model Convexification for Wireless Ad Hoc Networks With Multi-Packet Reception Capability

Author

Zhou, Jianshan, Tian, Daxin, Qu, Guixian, Sheng, Zhengguo, Duan, Xuting, and Leung, Victor C. M.

Abstract

Energy efficiency is a significant requirement of resource management and design optimization in information networks. In this article, we propose an iterative fractional programming framework embedded with a distributed primal-dual extra-gradient projection algorithm, which addresses a wide class of the energy-efficiency optimization problems in wireless ad hoc networks with full-duplex radios and multi-packet reception capability. Specifically, we propose a model convexification mechanism by joining an affine transformation and an exponential transformation into the nonlinear fractional programming, which enables us to deal with the challenge arising from the complexity and non-convex structure of the original problem. With the model convexification, we can map the non-convex power control space into a convex space and equivalently derive a sequence of convex subproblems, which relaxes the convexity assumption widely adopted in the existing literature. We further propose a distributed primal-dual algorithm based on extra-gradient projection to solve the convex subproblem at each iteration of the fractional programming. The convergence of the proposed iterative fractional programming and the distributed optimization method is theoretically proven. Numerical results also verify the proposed method and demonstrate its superior performance over other representative distributed and centralized schemes in terms of achieving global energy efficiency.

Published
2024
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