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Backdoor-Empowered Regulable Privilege Authorization for Edge-Level Graph Learning in 6G Vehicular Networks

IEEE Transactions on Consumer Electronics 2025年

作者： Yang, Xiao Li, Gaolei Wu, Jun Zhou, Kai Li, Jianhua Yang, Wu Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai China The Hong Kong Polytechnic University Department of Computing Hong Kong Harbin Engineering University College of Computer Science and Technology Harbin China

Edge-Level Graph Learning System (EGLS) exhibits diverse applicability in flow prediction, route planning, and accident forecasting. Existing EGLS studies extremely stress absolute fairness and impartiality for all users, resultantly depriving its flexibility and humanity in addressing special circumstances and rendering the privilege authorization for exceptional responses challenging to achieve. To address this limitation, we present BackPriv, the first privilege authorization methodology enabling regulable privilege assignment or revocation in EGLS. Specifically, it is achieved through a creative edge backdoor mechanism, wherein the edge training samples are poisoned via user-specific privilege tokens to induce privilege outputs from the well-trained EGLS for authorized users. Moreover, related EGLS regulating strategies were proposed to dynamically realize the addition and revocation of user privileges by system retraining to guarantee privilege control in EGLS. Additionally, a Graph Mutual Information-based adaptive privilege token generation method is introduced to reduce the impact of privilege authorization on normal inputs and augment authorization functionality. Based on experiments with multiple real-world datasets, BackPriv demonstrates high success rates of privilege authorization under various scenarios ( 100%) and exerts minimal impact on normal task performance for regular inputs (mean accuracy drop 0.32%). © 1975-2011 IEEE.

关键词： Adversarial machine learning

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A Privacy Preserving and Byzantine Robust Collaborative Federated Learning Method Design

A Privacy Preserving and Byzantine Robust Collaborative Fede...

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IEEE International Conference on Communications (ICC)

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Changchuan Yin Christopher G. Brinton Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Collaborative federated learning (CFL) enables device cooperation in training shared machine learning models without reliance on a parameter server. However, the absence of a parameter server also impacts vulnerabilities associated with adversarial attacks, including privacy inference and Byzantine attacks. In this context, this paper introduces a novel CFL framework that enables each device to individually determine the subset of devices to transmit FL parameters to over the wireless network, based on its neighboring devices' location, current loss, and connection information, to achieve privacy protection and robust aggregation. This is formulated as an optimization problem whose goal is to minimize CFL training loss while satisfying the privacy preservation, robust aggregation, and transmission delay requirements. To solve this problem, a proximal policy optimization (PPO)-based reinforcement learning (RL) algorithm integrated with a graph neural network (GNN) is proposed. Compared to traditional algorithms that use global information with high computational complexity, the proposed GNN-RL method can be deployed on devices based on neighboring information with lower computational overhead. Simulation results show that the proposed algorithm can protect data privacy and increase identification accuracy by 15% compared to an algorithm in which devices are partially clustered for model aggregation.

关键词： Training Privacy Federated learning Simulation Collaboration Clustering algorithms Propagation losses

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Home Energy Management systems: Challenges, Heterogeneity & Integration Architecture Towards A Smart City Ecosystem

arXiv

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arXiv 2024年

作者： Kormpakis, Georgios Lekidis, Alexios Sarmas, Elissaios Papias, Giannis Serepas, Filippos Stravodimos, George Marinakis, Vangelis Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece HOLISTIC SA Athens Greece University of Thessaly Gaiopolis Campus Larissa41500 Greece

The contemporary era is marked by rapid urban growth and increasing population. A significant, and constantly growing, portion of the global population now resides in major cities, leading to escalating energy demands in urban centers. As urban population is expected to keep on expanding in the near future, the same is also expected to happen with the associated energy requirements. The situation with the continuously increasing energy demand, along with the emergence of smart grids and the capabilities that are already -or can be- offered by Home Energy Management System (HEMS), has created a lot of opportunities towards a more sustainable future, with optimized energy consumption and demand response, which leads to economic and environmental benefits, based on the actual needs of the consumers. In this paper, we begin by providing an analytical exploration of the challenges faced at both the development and deployment levels. We proceed with a thorough analysis and comparison between the abundance of devices, smart home technologies, and protocols currently used by various products. Following, aiming to blunt the currently existing challenges, we propose a reliable, flexible, and extendable architectural schema. Finally, we analyze a number of potential ways in which the data deriving from such implementations can be analyzed and leveraged, in order to produce services that offer useful insights and smart solutions towards enhanced energy efficiency. © 2024, CC BY.

关键词： Smart homes

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A Method to Control Distributed Energy Resources in Distribution Networks Using Smart Meter Data

SSRN

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SSRN 2023年

作者： Zabihinia Gerdroodbari, Yasin Pengwah, Abu Bakr Razzaghi, Reza Heidari, Rahmat Andrew, Lachlan L.H. Department of Electrical and Computer Systems Engineering Monash University ClaytonVIC Australia 10 Murray Dwyer Circuit Mayfield West NewcastleNSW Australia School of Computing and Information Systems University of Melbourne CarltonVIC Australia

Voltage regulation has been one of the major challenges for distribution network operators (DNOs) due to the integration of distributed energy resources in the recent years. Control approaches mitigating over-voltage (OV) generally require full network model, which for low-voltage distribution networks (LVDNs), can be inaccessible to DNOs. This paper presents a novel datadriven model-free control framework for improving voltage regulation in 4wire 3-phase LVDNs. In the proposed approach, first, using smart meter data, the aggregated resistance and reactance matrices of the network are estimated. Then, these matrices serve as an input for the proposed optimal power flow (OPF) method to control the DERs’ active and reactive power outputs. This method also lets the DNO adjust the fairness in prosumers’ generation curtailment. The performance of the proposed approach is evaluated in various case studies conducted on a real-world low-voltage test feeder. The simulation results exhibit significant effectiveness in solving the OV problem associated with DERs. © 2023, The Authors. All rights reserved.

关键词： Matrix algebra

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Learning Goal-based Movement via Motivational-based Models in Cognitive Mobile Robots

arXiv

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arXiv 2023年

作者： Berto, Letícia Costa, Paula Simões, Alexandre Gudwin, Ricardo Colombini, Esther Laboratory of Robotics and Cognitive Systems Institute of Computing University of Campinas Brazil Dept. of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Brazil Dept. of Control and Automation Engineering Institute of Science and Technology São Paulo State University Sorocaba Brazil University of Campinas Brazil

Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action’s perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We added hedonic dimensions to see how preferences affect decision-making, and we employed reinforcement learning to train our motivated-based agents. We run three agents with energy decay rates representing different metabolisms in two different environments to see the impact on their strategy, movement, and behavior. The results show that the agent learned better strategies in the environment that enables choices more adequate according to its metabolism. The use of pleasure in the motivational mechanism significantly impacted behavior learning, mainly for slow metabolism agents. When survival is at risk, the agent ignores pleasure and equilibrium, hinting at how to behave in harsh scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Metabolism

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Designing of a Solar MPPT Controller with the Technique of Differentiation Between Uniform Irradiance and Partial Shading Conditions 9

Designing of a Solar MPPT Controller with the Technique of D...

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9th IEEE International Conference on Power and Energy, PECon 2022

作者： Ahmed, Sajib Mekhilef, Saad Mubin, Marizan Tey, Kok Soon Rawa, Muhyaddin University of Malaya Power Electronics and Renewable Energy Research Laboratory Faculty of Engineering Department of Electrical Engineering Kuala Lumpur50603 Malaysia School of Science Computing and Engineering Technologies Swinburne University of Technology HawthornVIC3122 Australia University of Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia King Abdulaziz University Center of Research Excellence in Renewable Energy and Power Systems Jeddah21589 Saudi Arabia

ISBN: (数字)9781665409902

ISBN: (纸本)9781665409902

Solar energy is considered one of the best essential energy sources to solve the present power crisis. The solar photovoltaic system has a low conversion efficiency that inflicts maximum power extractions. The non-linear relationship between the voltage and current insists on using a maximum power point tracking (MPPT) controller. Perturb and observe (P&O) MPPT controller is popular because of its simple design and cost-effectiveness features;nonetheless, it has drawbacks while operating under partial shading conditions (PSC). The maximum power trapezium (MPT) method modified the P&O algorithm by incorporating an appropriate scanning mechanism for PSC. Despite having good PSC performance, this method's tracking time is much longer than the conventional algorithm in both uniform irradiance conditions (UIC) and PSC. The algorithm proposed in this paper modifies the MPT method with an extra strategy to distinguish between the UIC and PSC. If the pattern is recognized as UIC, the proposed method utilizes the conventional P&O algorithm to track the MPP;otherwise, scanning techniques are being used. The proposed algorithm's operation was tested on simulation using distinct UIC and PSC patterns and worked effectively. © 2022 IEEE.

关键词： Cost effectiveness

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DisCOV: Distributed COVID-19 Detection on X-Ray Images with Edge-Cloud Collaboration

DisCOV: Distributed COVID-19 Detection on X-Ray Images with ...

引用

2022 IEEE World Congress on Services, SERVICES 2022

作者： Xu, Xiaolong Tian, Hao Zhang, Xuyun Qi, Lianyong He, Qiang Dou, Wanchun Nanjing University of Information Science and Technology School of Computer and Software Nanjing China Macquarie University Department of Computing Sydney Australia Qufu Normal University School of Information Science and Engineering Rizhao China Swinburne University of Technology School of Software and Electrical Engineering Melbourne Australia Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services computing DOI 10.1109/TSC.2022.3142265]

ISBN: (数字)9781665481311

ISBN: (纸本)9781665481311

[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services computing DOI 10.1109/TSC.2022.3142265]

关键词： COVID-19 Image edge detection Collaboration X-ray imaging

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Enhancing Wireless Networks with Attention Mechanisms: Insights from Mobile Crowdsensing

arXiv

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arXiv 2024年

作者： Yang, Yaoqi Du, Hongyang Xiong, Zehui Niyato, Dusit Jamalipour, Abbas Han, Zhu College of Communications Engineering Army Engineering University of PLA Nanjing210000 China College of Computing and Data Science NTU Singapore Singapore Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Singapore School of Electrical and Computer Engineering University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States

The increasing demand for sensing, collecting, transmitting, and processing vast amounts of data poses significant challenges for resource-constrained mobile users, thereby impacting the performance of wireless networks. In this regard, from a case of mobile crowdsensing (MCS), we aim at leveraging attention mechanisms in machine learning approaches to provide solutions for building an effective, timely, and secure MCS. Specifically, we first evaluate potential combinations of attention mechanisms and MCS by introducing their preliminaries. Then, we present several emerging scenarios about how to integrate attention into MCS, including task allocation, incentive design, terminal recruitment, privacy preservation, data collection, and data transmission. Subsequently, we propose an attention-based framework to solve network optimization problems with multiple performance indicators in large-scale MCS. The designed case study have evaluated the effectiveness of the proposed framework. Finally, we outline important research directions for advancing attention-enabled MCS. © 2024, CC BY.

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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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2023 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2023

作者： Naceur, Ferdaws Ben Toumi, Sana Salah, Chokri Ben Mahjoub, Mohamed Ali National School of Engineers of Sousse University of Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse4011 Tunisia Department of Electrical Engineering Monastir Tunisia Brest29238 France Issat Sousse Sousse Tunisia

ISBN: (纸本)9798350325539

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)). © 2023 IEEE.

关键词： Decision making

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A User-Friendly Electric Vehicle Reallocation Solution for Uniformly Utilized Charging Stations

SSRN

引用

SSRN 2023年

作者： Skaloumpakas, Panagiotis Spiliotis, Evangelos Stoian, Daniela Stravodimos, George Makarouni, Ioanna Marinakis, Vangelis Psarras, Ioannis Psarras, John HOLISTIC S.A. Athens Greece Forecasting and Strategy Unit School of Electrical & Computer Engineering National Technical University of Athens Greece Decision Support Systems Laboratory School of Electrical & Computer Engineering National Technical University of Athens Greece National Technical University of Athens Greece

Electric vehicle (EV) users often struggle to identify charging stations (EVCSs) that are both available at the time of interest and placed in convenient locations. At the same time, EVCSs in nearby locations may systematically be underutilized. This variance in the utilization of the EVCSs can have negative implications, both for the EV users (e.g. higher anxiety, waiting time, and discomfort) and the owners/operators of the EVCSs (e.g. lower income and longer payback period of investment). The present work aims to mitigate said implications by introducing a solution that efficiently allocates EV users to stations based on their expected utilization and their proximity to the charging locations of preference. To do so, a forecasting model is first used to predict the arrivals at the EVCSs. Then, the utilization rates of the EVCSs are computed and a heuristic algorithm is employed to dynamically reallocate the EV users from overutilized stations to nearby, underutilized stations. The proposed approach, which has been implemented in a web-based application, is evaluated through simulation experiments and compared with the traditional ``first-come, first-served'' approach. The results indicate that our approach can achieve more uniformly distributed utilization rates with a minimal deterioration on the convenience of the EV users, thus offering a practical solution to the examined problem. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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