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Integrating Gen3 Blockchain into a Transactive Energy Market for DERs Orchestration

IEEE Transactions on Industry Applications 2025年第3期61卷 5103-5115页

作者： Ali, L. Azim, M. Imran Peters, J. Ojha, Nabin B. Simoes, M.G. Muyeen, S.M. Curtin University Department of Electrical Engineering Bentley Australia School of Engineering RMIT University Melbourne Australia aculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Berlin Melbourne Germany Department of Energy Environment and Climate Change Asian Institute of Technology Pathum Thani Thailand Department of Electrical Engineering University of Vaasa Vaasa Finland Department of Electrical Engineering Qatar University Doha Qatar

This paper presents the integration of third generation (Gen3) blockchain technology into a transactive energy market (TEM) for enabling secure and efficient peer-to-peer (P2P) energy trading among diverse participants, including prosumers with photovoltaic (PV) systems and battery energy storage systems (BESS), as well as electric vehicle (EV) owners. The primary motivation behind this work is to streamline transactive energy markets by reducing reliance on third-party intermediaries and enhancing energy self-sufficiency. The TEM utilizes blockchain's decentralized ledger for transparent transactions, with an advanced trading engine that matches participants' energy and price bids based on forecasted profiles and optimizes local energy use while minimizing costs. The major contributions of this study include: (1) the development of a scalable and efficient P2P trading framework leveraging Proof of Stake (POS) and Proof of History (POH) consensus mechanisms based on Gen3 blockchain, and (2) a comprehensive performance analysis resulting in considerable transaction time, throughput and cost reductions compared to traditional business-as-usual models. Numerical simulations reveal a 24% and 23% reduction in peak grid imports and exports, translating into financial and environmental benefits for all stakeholders, including prosumers, network operators, and retailers. © 2025 IEEE.

关键词： Cost reduction

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Interaction-aware Trust Management Scheme for IoT Systems With Machine Learning-Based Attack Detection

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IEEE Internet of Things Journal 2025年第11期12卷 17169-17182页

作者： Farhat, Ali Eldosouky, Abdelrahman Ibnkahla, Mohamed Matrawy, Ashraf Carleton University Department of Systems and Computer Engineering OttawaON Canada Carleton University School of Information Technology OttawaON Canada

The recent Internet of Things (IoT) adoption has revolutionized various applications while introducing significant security and privacy challenges. Traditional security solutions are unsuitable for IoT systems due to their dynamicity, heterogeneity, and resource constraints. Trust-based solutions are emerging as promising alternatives due to their ability to track the dynamic behavior in IoT systems. However, existing trust management schemes are implemented at the device level, raising several challenges, including device modification that compromises certification and scalability, increased network overhead, and higher device resource utilization. To address these challenges, this paper proposes a novel trust management scheme that shifts its implementation to a higher layer in the IoT system, specifically to the IoT access layer (e.g., gateway). The proposed scheme establishes trust based on typical device interactions with the gateway without requiring additional information from the device. It relies on objective attributes spanning communication, security, and advanced dimensions to compute the trust value of an IoT device. Additionally, an Artificial Neural Network (ANN) is integrated to determine if the device acts maliciously or behaves normally. Simulation results demonstrate a notable improvement in the detection rate, primarily due to incorporating the proposed ANN, compared to the threshold-based approaches in the literature. Overall, the improvements highlight the significant advantage of the proposed scheme's robustness. © 2025 IEEE.

关键词： Gateways (computer networks)

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Why Smart Contracts Reported as Vulnerable Were Not Exploited?

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2579-2596页

作者： Hu, Tianyuan Li, Jingyue Li, Bixin Storhaug, André Southeast University School of Computer Science and Engineering Nanjing211189 China Norwegian University of Science and Technology Department of Computer Science Trondheim7034 Norway

Smart contract security is crucial for blockchain applications. While studies suggest that only a small fraction of reported vulnerabilities are exploited, no follow-up research has investigated the reasons behind this. Our goal is to understand the factors contributing to the low exploitation rate to improve vulnerability detection and defense mechanisms. We collected 136,969 real-world smart contracts and analyzed them using seven vulnerability detectors. We applied Strauss’ grounded theory to gain insights into exploitability and analyzed transaction logs to trace the historic exploitations. Among the 4,364 smart contracts flagged as vulnerable, a significant 75.25% were found to be unexploitable, meaning they were either false positives or posed no security risk. We identified ten reasons for reporting unexploitable vulnerabilities. Furthermore, we found that only 66 out of 1,080 (6%) exploitable contracts had been exploited. We compared the characteristics of exploited versus non-exploited vulnerabilities and identified five factors that may reduce the likelihood of exploitation. Our findings highlight the importance of not treating smart contracts as conventional object-oriented (OO) applications. Researchers must account for the unique features of Solidity, smart contract design principles, and execution environments. Based on these insights, we propose six recommendations to improve smart contract vulnerability detection, prioritization, and mitigation. © 2004-2012 IEEE.

关键词： Smart contract

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Power System Reliability Evaluation Based on Sequential Monte Carlo Simulation Considering Multiple Failure Modes of Components

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Journal of Modern Power Systems and Clean Energy 2025年第1期13卷 202-214页

作者： Wei Huang Bo Hu Changzheng Shao Wei Li Xiaozhe Wang Kaigui Xie C.Y.Chung State Key Laboratory of Power Transmission Equipment Technology School of Electrical EngineeringChongqing UniversityChongqingChina China Southern Power Grid GuangzhouChina Department of Electrical and Computer Engineering McGill UniversityMontrealCanada Department of Electrical and Electronic Engineering Hong Kong Polytechnic UniversityHong KongChina

The component aging has become a significant concern worldwide,and the frequent failures pose a serious threat to the reliability of modern power *** light of this issue,this paper presents a power system reliability evaluation method based on sequential Monte Carlo simulation(SMCS)to quantify system reliability considering multiple failure modes of ***,a three-state component reliability model is established to explicitly describe the state transition process of the component subject to both aging failure and random failure *** this model,the impact of each failure mode is decoupled and characterized as the combination of two state duration variables,which are separately modeled using specific probability ***,SMCS is used to integrate the three-state component reliability model for state transition sequence generation and system reliability ***,various reliability metrics,including the probability of load curtailment(PLC),expected frequency of load curtailment(EFLC),and expected energy not supplied(EENS),can be *** ensure the applicability of the proposed method,Hash table grouping and the maximum feasible load level judgment techniques are jointly adopted to enhance its computational *** studies are conducted on different aging scenarios to illustrate and validate the effectiveness and practicality of the proposed method.

关键词： Power system reliability evaluation aging failure sequential Monte Carlo simulation

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A voting-based trustworthy distributed IoT attack detection model

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Personal and Ubiquitous Computing 2025年第1期29卷 103-118页

作者： Sharma, Priya Sharma, Sanjay Kumar Dani, Diksha University School of Information & Communication Technology Gautam Buddha University Greater Noida India Department of Computer Engineering SVKM’s NMIMS Mukesh Patel School of Technology Management & Engineering Mumbai India

Besides the enhancement of the Internet of Things (IoT) distributed environment, anomalous activities are also escalating rapidly. Therefore, improving the trustworthiness of distributed networks is required for the extensive adoption of IoT infrastructure. Establishing a security mechanism in IoT networks is a challenging task as communication links are lossy and connected devices are resource-dependent. Conventional security techniques such as intrusion detection systems (IDS) are insufficient to shelter the IoT-distributed environment due to less computational capacity, restricted upgraded devices, and mismatched protocols. This paper proposes a novel machine learning-based trustworthy model for IoT attack detection. The proposed system combines the capability of Ada-boost and Gradient-boost to classify anomalous activities with low computational capacity proficiently and within a minimum time frame. Experiments were conducted on Distributed Smart Space Orchestration System (DS2OS) IoT dataset to assess the significance of the novel attack detection model. The demonstration shows that the proposed model obtains 98.28% training accuracy, 98.26% testing accuracy, and 99.25% AUC score. A confusion matrix has also been calculated to check the authenticity of the results—a comparison of the proposed model with individual learners and existing models proves that the proposed model outperformed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Network intrusion

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A Fully LTPS-TFT-Based Bi-Directional Biomedical Interface Circuit for Large-Area Healthcare Applications

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年第12期3卷 526-532页

作者： Zhang, Hanbo Lou, Yuqing Zhang, Zhihang Li, Yongfu Rokhan, Fakhrul Wang, Guoxing Zhao, Jian Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication System Engineering Selangor Serdang43400 Malaysia

This paper proposed a fully LTPS-TFT-based bi-directional biomedical pixel interface with integrated a high linearity stimulator and a high-performance bio-potential sensing front-end circuit to amplify and digitize biological signals for high robustness data transmission. All circuits are designed and manufactured under a 3-μm LTPS process. We demonstrated the experiment in saline using the stimulator circuit to simulate stimulation inside the organism, achieving stimulation with different average currents under different duty cycles. The proposed front-end circuit has a gain of 40 dB. The referred input voltage noise of the entire signal chain is 86.2 μV/√Hz, and the ENOB is 9.13 bits at an input of 0.5 mVPP. Simultaneously conducting statistical testing on 20 chips has achieved a 1.05 dB SNDR standard deviation. Finally, we use a function signal generator to simulate the input signals of ECoG, ECG, and EOG, then use front-end circuits to read and reconstruct them to demonstrate that the proposed pixel interface can correctly read common biomedical signals for healthcare applications. © 2022 IEEE.

关键词： Image segmentation

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RoPe-Door: Toward Robust and Persistent Backdoor Data Poisoning Attacks in Federated Learning

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IEEE Network 2025年第3期39卷 302-310页

作者： Liu, Ye Chang, Shan Li, Denghui Shi, Shaohuai Li, Bo Jiangxi Normal University School of Computer and Information Engineering Nanchang330022 China Donghua University School of Computer Science and Technology Shanghai201620 China Harbin Institute of Technology School of Computer Science and Technology Shenzhen518057 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Sai Kung Hong Kong

Federated Learning (FL) enables privacy-preserving collaborative training and builds a federation through exchanges of immutable data such as model parameters or gradient updates. FL remains vulnerable to a variety of attacks during critical processes like local model training and parameter transmission, in which the backdoor attack is particularly evident. In this paper, we propose a novel backdoor data poisoning attack method, RoPe-Door, using a trigger generation algorithm to improve the robustness and persistence of attacks even under Byzantine aggregation methods. We conduct extensive experiments on four image classification tasks to evaluate the effectiveness of RoPe-Door. The experimental results demonstrate that, compared to backdoor attacks using random triggers, RoPe-Door exhibits significant advantages in robustness, persistence, and attack effectiveness under both IID and Non-IID data settings. © 1986-2012 IEEE.

关键词： Federated learning

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Data-driven output regulation control for constrained linear systems

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Science China(Information Sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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Orbit-Orbit Interaction in Spatiotemporal Optical Vortex

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engineering 2025年第2期45卷 44-51页

作者： Jian Chen Jie Zhao Xi Shen Dewei Mo Cheng-Wei Qiu Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

While spin-orbit interaction has been extensively studied,few investigations have reported on the interaction between orbital angular momenta(OAMs).In this work,we study a new type of orbit-orbit coupling between the longitudinal OAM and the transverse OAM carried by a three-dimensional(3D)spatiotemporal optical vortex(STOV)in the process of tight *** 3D STOV possesses orthogonal OAMs in the x-y,t-x,and y-t planes,and is preconditioned to overcome the spatiotemporal astigmatism effect.x,y,and t are the axes in the spatiotemporal *** corresponding focused wavepacket is calculated by employing the Debye diffraction theory,showing that a phase singularity ring is generated by the interactions among the transverse and longitudinal vortices in the highly confined *** Fourier-transform decomposition of the Debye integral is employed to analyze the mechanism of the orbit-orbit *** is the first revelation of coupling between the longitudinal OAM and the transverse OAM,paving the way for potential applications in optical trapping,laser machining,nonlinear light-matter interactions,and more.

关键词： Spatiotemporal optical vortex Orbital angular momentum Momentum interaction Highly confined wavepacket Diffraction

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Ensuring safety of vehicular cyber physical systems using machine learning and MQTT

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International Journal of Vehicle Information and Communication Systems 2025年第2期10卷 165-183页

作者： Bagga, Neha Kalra, Sheetal Kaur, Parminder Department of Computer Science Guru Nanak Dev University Punjab Amritsar India School of Computer Science Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering G.N.D.U. Regional Campus Punjab Jalandhar India

One of the pressing concerns for emerging nations is maintenance of roads, including identification and repair of pavement distress. Previous research has focused on pothole detection and lane identification, with the distress details being shared with drivers via database or an Android application. However, this approach is battery-intensive for sensors in smart vehicles and requires a regular internet connection. To address these issues, we have proposed a model trained using Python and TensorFlow to identify road distress and steep curves with an accuracy of 85.2% and 83.1%, respectively. The simulation uses Geocoder to capture the geographical coordinates of the distress, and the collected data is transferred to other CPS devices in cars using MQTT which outperforms databases and Android applications in terms of efficiency, sensor load, and internet connectivity. Drivers receive alerts within 10 seconds, allowing them to make informed decisions which helps prevent accidents and fatalities on the road. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Highway accidents

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