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IEEE International Conference on Robotics and Automation (ICRA)

作者： Hongchao Zhang Luyao Niu Andrew Clark Radha Poovendran Electrical and Systems Engineering Department McKelvey School of Engineering Washington University in St. Louis St. Louis MO Department of Electrical and Computer Engineering Network Security Lab University of Washington Seattle WA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Safety is a fundamental requirement of many robotic systems. Control barrier function (CBF)-based approaches have been proposed to guarantee the safety of robotic systems. However, the effectiveness of these approaches highly relies on the choice of CBFs. Inspired by the universal approximation power of neural networks, there is a growing trend toward representing CBFs using neural networks, leading to the notion of neural CBFs (NCBFs). Current NCBFs, however, are trained and deployed in benign environments, making them ineffective for scenarios where robotic systems experience sensor faults and attacks. In this paper, we study safety-critical control synthesis for robotic systems under sensor faults and attacks. Our main contribution is the development and synthesis of a new class of CBFs that we term fault tolerant neural control barrier function (FT-NCBF). We derive the necessary and sufficient conditions for FT-NCBFs to guarantee safety, and develop a data-driven method to learn FT-NCBFs by minimizing a loss function constructed using the derived conditions. Using the learned FT-NCBF, we synthesize a control input and formally prove the safety guarantee provided by our approach. We demonstrate our proposed approach using two case studies: obstacle avoidance problem for an autonomous mobile robot and spacecraft rendezvous problem, with code available via https://***/HongchaoZhang-HZ/FTNCBF.

关键词： Space vehicles Sufficient conditions Fault tolerance Neural networks Fault tolerant systems Robot sensing systems Safety

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Deep Reinforcement Learning-Based Full-Duplex Communication UAV Base Station Trajectory Optimization in Disaster Environments

Deep Reinforcement Learning-Based Full-Duplex Communication ...

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2023 VTS Asia Pacific Wireless Communications Symposium, APWCS 2023

作者： Kim, Tae-Yoon Lee, Jaeyeol Kim, Jae-Hyun Ajou University Department of Artificial Intelligence Convergence Network Suwon16499 Korea Republic of Ajou University Department of Space Survey Information Technology Suwon16499 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

ISBN: (纸本)9798350316803

Unmanned aerial vehicles (UAVs) are widely used in various fields due to their fast mobility and flexible deployment capabilities. While previous research focused on using UAVs as relays, recent studies have explored the use of UAVs as base stations (UAV-BS) to overcome the limitations of ground-base stations. Particularly, in disaster environments where traditional ground-base stations are unavailable, the utilization of UAV-BS has gained significant attention for overcoming critical communication challenges. Moreover, research on full-duplex (FD) communication, which enhances frequency efficiency to serve a larger number of nodes in disaster environments, has been actively pursued. However, the availability of FD communication between node pairs can vary depending on the position of the UAV-BS when it is used. In this paper, we propose a deep reinforcement learning (DRL)-based approach to optimize the trajectory of UAV-BS for efficient FD communication in disaster environments. Our experimental results demonstrate that the proposed algorithm outperforms other existing algorithms, achieving more efficient FD communication within the given time constraints. © 2023 IEEE.

关键词： Base stations

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Advancements in Object Detection for Accurate and Efficient Visual Recognition Using Machine Learning

Advancements in Object Detection for Accurate and Efficient ...

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International Conference on Advances in Information Technology (ICAIT)

作者： F. Mary Harin Fernandez A. Ganesh Ramachandran S. K. Saravanan M. Bhanumathi M. Sangeetha Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computers Science and Engineering BigData and Network Security Saveetha School of Engineering SIMATS Chennai Tamil Nadu Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Computer Engineering Department University of Technology and Applied Sciences Nizwa Sultanate of Oman

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Object detection in images and videos is a vital task in computer vision where its application goes beyond the automobile driving and surveillance, to healthcare. The paper introduces an object detection model which is specific and powerful, that is able to recognize visually with machine learning. The framework is constructed on the basis of the recent innovations in deep learning and attentive mechanisms, which not only lead to the best performance in object detection today but also guarantee the widest applicability. Using an extensive experiment which reveals the described model superiority over the current state-of-the-art algorithms by evaluating mAP indices, whereas the latter has a normal complexity. The ablation study uncovers the impact of core parameters including attention mechanisms and semi-supervised techniques, underlining their importance in the growth of detection precision. Moreover, the illustrative comparison of outcomes from detecting objects on the framework calls for attention to the capacity of the networks to objectify in different categories. Our outcome also suggest that although the proposed framework has a human-level performance in task of object detection there is still a scope to enhance their performance above the human level. Summarizing, this work concludes the conversation and can be considered in the progress of research developments of the object detection systems aimed at practical object meeting situations.

关键词： Visualization Technological innovation Attention mechanisms Accuracy Surveillance Transfer learning Object detection

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Mixture Gaussian Distribution-Based Collaborative Reinforcement Learning for 3D UAV Localization Optimization Against Jamming Attacks

Mixture Gaussian Distribution-Based Collaborative Reinforcem...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhu, Yujiao Chen, Mingzhe Wang, Sihua Liu, Yuchen Li, Gaolei Yin, Changchuan Quek, Tony Q.S. Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Network Beijing100876 China Singapore University of Technology and Design 487372 Singapore University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States North Carolina State University Department of Computer Science RaleighNC27695 United States Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China

ISBN: (纸本)9798350351255

In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly use two localization methods: 1) generative adversarial network (GAN)-based positioning method and 2) time difference of arrival (TDOA)-based positioning method. Since GAN-based positioning method cannot defense in a strong jamming signal while TDOA-based positioning method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to estimate the position of the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model-based collaborative reinforcement learning (RL) method which enables the active UAV to determine its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the movement of passive UAVs and the unknown jamming attack pattern of the jamming UAV. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method. © 2024 IEEE.

关键词： Gaussian distribution

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Receding-Horizon Trajectory Planning for Under-Actuated Autonomous Vehicles Based on Collaborative Neurodynamic Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1909-1923页

作者： Jiasen Wang Jun Wang Qing-Long Han IEEE the Future Network Research Center Purple Mountain LaboratoriesNanjing 211111China the Department of Computer Science the School of Data ScienceCity University of Hong KongHong KongChina the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia

This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization.A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal *** feasibility of the formulated optimization problem is guaranteed under derived *** optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/*** results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.

关键词： Collaborative neurodynamic optimization receding-horizon planning trajectory planning under-actuated vehicles

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Attribute-Based Data Sharing Scheme with Flexible Search Functionality for Cloud-Assisted Autonomous Transportation System

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IEEE Transactions on Industrial Informatics 2023年第11期19卷 10977-10986页

作者： Xiong, Hu Wang, Hanxiao Meng, Weizhi Yeh, Kuo-Hui University of Electronic Science and Technology of China School of Information and Software Engineering Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby2800 Denmark National Dong Hwa University Department of Information Management Hualien97401 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804201 Taiwan

The existing group public key encryption with equality test schemes could only support one-to-one data sharing and are not suitable for cloud-assisted autonomous transportation systems, which demand one-to-many data sharing. To tackle this problem efficiently, in this article, we put forward the group-attribute-based encryption with equality test (G-ABEET) scheme. The presented G-ABEET allows sensors equipped in vehicles to encrypt traffic data with an expressive access policy before sharing it. Only users with attributes required by the access policy ought to access the shared ciphertexts, thus achieving selective one-to-many data sharing. Meanwhile, the authorized cloud server could provide group users with equality tests over the ciphertexts, realizing ciphertext search ability. Furthermore, with the group mechanism, the G-ABEET scheme could resist offline message recovery attacks. Besides, in the standard model, we give rigorous security proof of the G-ABEET construction. The feasibility and efficiency of G-ABEET are demonstrated by experimental simulations. © 2005-2012 IEEE.

关键词： Cloud computing

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Integration of STAR-RIS With Index Modulation: Novel Attraction Solutions for IoT Applications

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IEEE Internet of Things Journal 2025年

作者： Yue, Ming Peng, Yuyang Ye, Runlong AL-Hazemi, Fawaz Mirza, Mohammad Meraj Lee, Juho Macau University of Science and Technology School of Computer Science and Engineering China University of Jeddah Department of Computer and Network Engineering Jeddah21959 Saudi Arabia Taif University College of Computers and Information Technology Department of Computer Science Taif21944 Saudi Arabia Samsung Electronics Samsung Research Seoul06765 Korea Republic of

The integration of the Sixth-Generation (6G) communication technology with the Internet of Things (IoT) is one of the directions in achieving a digitalized world, aiming to provide ultra-high-speed communication services that exhibit higher energy-efficiency, lower latency, and heightened reliability and availability. In this paper, we investigate the use of the Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS), one of the candidate technologies for 6G communication, along with the Index Modulation (IM) in multi-antennas Ambient Backscatter Communication System (ABCS) to form the STAR-RIS-assisted ABCS with Spatial Modulation (STAR-RIS-ABCS-SM). In particular, through the utilization of STAR-RIS and backscatter communication, we meet the demand for low-power wireless communication devices in the green communication, which is also a crucial characteristic of the IoT. Specifically, we introduce STAR-RIS into ABCS, which not only significantly boost the signal strength but also achieve full communication coverage. Meanwhile, we apply the SM technology in the multi-antennas Backscatter Tags (BTs), located on both sides of the STAR-RIS, and use joint detection at the reader to enhance the system’s Spectral Efficiency (SE) as well as its Bit Error Rate (BER) performance. Furthermore, we divide the STAR-RIS into various subsurfaces to form patterns that further enhance SE, which is termed STAR-RIS-assisted ABCS with Pattern Index and SM (STAR-RIS-ABCS-PISM) scheme. Finally, theoretical BER analytical expressions for both schemes are derived and presented. Simulation and theoretical results demonstrate the enhanced performance of the proposed schemes as well as the validity of the system model. © 2014 IEEE.

关键词： Spectrum efficiency

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FAKEY: Fake Hashed Key Attack on Payment Channel networks

FAKEY: Fake Hashed Key Attack on Payment Channel Networks

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IEEE Conference on Communications and network Security (CNS)

作者： Alvi Ataur Khalil Mohammad Ashiqur Rahman Hisham A. Kholidy Department of Electrical and Computer Engineering Florida International University USA Department of Network and Computer Security SUNY Polytechnic Institute USA

Although blockchain’s immutability and decentralized capabilities make monetary transactions more secure than ever, its inherent scalability problem hinders its utilization in myriad applications. Payment channel networks (PCNs), one of the prominent solutions to blockchain’s scalability issue, reduce the load on the blockchain by performing the transactions offchain. However, malicious PCN participants can delay the block delivery by slightly misbehaving in the initialization phase. In this work, we introduce FAKEY, a fake hashed key-based attack on PCN that can block up a whole set of channels carrying a transaction for a certain period (via collateral lock), depending on the hashed timelock contract (HTLCs) in the attacked payment path. For the targeted attack setup, the effect of the FAKEY is more severe, as PCN throughput can be reduced significantly. Moreover, the attacker can gain direct monetary benefit by carefully choosing one or more victim nodes. We utilize one of the state-of-the-art PCN simulators, PCN sim, to perform multiple experiments with different attack strategies and calculate profit accumulation scenarios. To demonstrate the attack impacts in detail, we perform a couple of case studies using the simulator. Finally, to validate the attack effect and adversarial benefits in a real-world PCN, we utilize the Bitcoin Lightning network snapshots spanning over a period of 18 months and calculate the exact monetary impact in satoshi units.

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A Distillation-Based Attack Against Adversarial Training Defense for Smart Grid Federated Learning

A Distillation-Based Attack Against Adversarial Training Def...

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Consumer Communications and networking Conference, CCNC IEEE

作者： Atef H. Bondok Mohamed Mahmoud Mahmoud M. Badr Mostafa M. Fouda Maazen Alsabaan Department of Electrical and Computer Engineering Tennessee Tech. University Cookeville TN USA Department of Electrical Engineering College of Engineering Al-Azhar University Cairo Egypt Department of Network and Computer Security College of Engineering SUNY Polytechnic Institute Utica NY USA Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

In the advanced metering infrastructure (AMI) of the smart grid, smart meters (SMs) are deployed to collect fine-grained electricity consumption data, enabling billing, load monitoring, and efficient energy management. However, some consumers engage in fraudulent behavior by hacking their meters, leading to either traditional electricity theft or more sophisticated evasion attacks. Evasion attacks aim to illegally reduce electricity bills while deceiving theft detection mechanisms. The current methods for identifying such attacks raise privacy concerns due to the need for access to consumers' detailed consumption data to train detection mechanisms. To address privacy concerns, federated learning (FL) is proposed as a collaborative training approach across multiple consumers. Adversarial training (AT) has shown promise in countering evasion threats on machine learning models. This paper, first, investigates the susceptibility of traditional electricity theft classifiers trained by FL to evasion attacks for both independent and identically distributed (IID) and Non-IID consumption data. Then, it investigates the effectiveness of AT in securing the global electricity theft detector against evasion attacks, assuming no misbehavior from the participant consumers in the FL process. After that, we introduce a novel attack, called Distillation, which can be launched during the AT process to make the global model susceptible to evasion at inference time. Finally, extensive experiments are conducted to validate the severity of the proposed attack.

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Office Building Energy Community: A Case Study on Optimal Market Operation and Grid Impact 9

Office Building Energy Community: A Case Study on Optimal Ma...

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9th IEEE International Smart Cities Conference, ISC2 2023

作者： Lepisto, Juhani Forcan, Jovana Forcan, Miodrag Heine, Pirjo Helen Electricity Network Helsinki Finland University of East Sarajevo Faculty of Philosophy Department of Computer Science and Systems East Sarajevo Bosnia and Herzegovina University of East Sarajevo Faculty of Electrical Engineering Department of Electroenergetics East Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350397758

In this paper, we study an office building connected to an urban distribution network from an energy community perspective. The office building and its electrical equipment have a real-world counterpart from which we construct synthetic time series based on the historical measurement data. The office building involves three stakeholders and their assets. The central stakeholders are the owner and the tenant of the building. The third stakeholder is the operator of flexible assets. The smartness of the office building is related to the control capabilities of its assets. These controllable assets are the battery energy storage system (BESS) and the electric vehicles (EV) charging system (EVCS) that both can operate in a flexible manner in response to energy price fluctuations and participate in a reserve market. We proposed different cases where either the owner or the tenant is paying the EV charging, and where the flexibility of the EV charging is either active or not. We show that, when the owner pays for the energy usage of the EVCS and the tenant is using it, there is a beneficial case to form a coalition between them and share the benefits from the flexibility, as otherwise there is no incentive for the tenant to be flexible in the first place. We conducted operative cost-benefit calculations where we shared the community benefits according to the Shapley value and simulated the power flows at the building's connection point based on the market operations. We found that harnessing benefits from the two market operations had significant effects on the power flows at the connection point. This was true when operating under the variable energy prices and in the reserve market. © 2023 IEEE.

关键词： Office buildings

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