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A Novel Lightweight Dynamic Trust Evaluation Model for Edge Computing

IEEE Transactions on Network and Service Management 2025年

作者： Yang, Hao Guo, Liangmin Wang, Taochun Lv, Chengmei Anhui Technical College of Mechanical and Electrical Engineering School of Internet and Communication Wuhu241003 China Anhui Normal University School of Computer and Information Wuhu241003 China Anhui Provincial Key Laboratory of Network and Information Security Wuhu241003 China

The temporal decay of trust data in dynamic edge computing environments leads to inaccurate evaluation, and the recommended trust values from heterogeneous nodes are affected by subjective bias and are vulnerable to malicious attacks. To address these issues, this paper proposes a novel lightweight dynamic trust evaluation model. First, a time decay function is derived based on Newton’s law of cooling to effectively reflect the impact of trust timeliness on trust evaluation. On this basis, the real value is iteratively calculated and used as the recommended trust value through the truth discovery algorithm, enhancing the accuracy of trust evaluation. Then, the weight of the recommended trust value in the comprehensive trust value is determined based on the standard deviation of the weight of perceived data from each recommending node, balancing the influence of subjective and objective factors on trust evaluation results. Lastly, the comprehensive trust value is dynamically weighted, and an incentive mechanism is employed to update trust data based on feedback, reflecting the dynamic nature of trust. Theoretical analysis demonstrates that the model presented in this paper exhibits low time and space complexities, meeting the lightweight requirements of the edge computing environment. Experimental results indicate high recommendation trust accuracy and interaction success rates, as well as effective resistance against malicious attacks. © 2004-2012 IEEE.

关键词： Trusted computing

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Combining Scene Coordinate Regression and Absolute Pose Regression for Visual Relocalization

Combining Scene Coordinate Regression and Absolute Pose Regr...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jiahao Ruan Li He Yisheng Guan Hong Zhang School of Electromechanical Engineering Guangdong University of Technology Guangzhou China Department of Electrical and Electronic Engineering Shenzhen Key - Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China

Visual relocalization is a fundamental problem in computer vision and robotics. Recently, regression-based methods become popular and they can be categorized into two classes: absolute pose regression and scene coordinate regression. In this work, we present a combined regression network that jointly learns scene coordinate regression and absolute pose regression for single-image visual relocalization. The proposed network composes of a feature encoder and two regression branches with uncertainty modeling. In particular, we design a deep feature conditioning module, aiming at propagating the coarse pose information in absolute pose regression to inform the predictions in scene coordinate regression. The proposed network is trained in an end-to-end fashion to learn both regression tasks. Moreover, we propose an uncertainty-driven RANSAC algorithm that incorporates the predicted scene coordinates and their uncertainties to solve the camera pose during inference. To the best of our knowledge, this work is the first to combine scene coordinate regression and pose regression in a hierarchical framework for visual relocalization. Experiments on indoor and outdoor benchmarks demonstrate the effectiveness and the superiority of the proposed method over the state-of-the-art methods.

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On the Outage of Far-Reach Communications via RIS-Empowered Satellites

On the Outage of Far-Reach Communications via RIS-Empowered ...

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IEEE Pacific Rim Conference on Communications, computers and Signal Processing

作者： Fan Wang Ruihong Jiang Yuan Liu Tingnan Bao Mugen Peng State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Zhejiang Laboratory Research Center for Space Computing System Hangzhou China School of Electrical Engineering and Computer Science University of Ottawa Ottawa ON Canada

ISBN: (数字)9798350362312

ISBN: (纸本)9798350362329

The rising demand for reliable, high-bandwidth satellite communications in far-reach areas drives the integration of low earth orbit (LEO) satellites into future 6G networks. In this paper, we investigate the deployment of reconfigurable intelligent surface (RIS) technology and rate splitting multiple access (RSMA) within LEO satellite systems to improve communication quality and system capacity. We derive closed-form expressions for outage probability (OP) and reliable throughput that validate the theoretical advantages of these technologies under various signal-to-noise ratio conditions. Numerical results demonstrate that, compared with traditional space division multiple access (SDMA) and non-orthogonal multiple access (NOMA) approaches, our RSMA and RIS-integrated framework significantly can reduce OP and enhance reliable throughput with an appropriate power-splitting factor, showcasing its superiority in RIS-enabled satellite communication scenarios.

关键词： NOMA Satellites Closed-form solutions computer network reliability Low earth orbit satellites Reconfigurable intelligent surfaces Reliability theory Throughput Multiaccess communication Satellite communications

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Multi-Connectivity Mobility Management in Downlink FD-RAN: A Learning Based Approach

Multi-Connectivity Mobility Management in Downlink FD-RAN: A...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Tianqi Zhang Jianzhe Xue Jiwei Zhao Jiacheng Chen Haibo Zhou Xuemin Sherman Shen School of Electronic Science and Engineering Nanjing University Nanjing China Department of Mathematics and Theories Peng Cheng Laboratory Shenzhen China Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

We consider a fully-decoupled radio access network (FD-RAN), where base stations (BSs) are physically decoupled into control BSs, uplink BSs and downlink BSs, and multi-connectivity becomes the default user equipment (UE) association mode. Specifically, we study the inter-frequency multi-connectivity in downlink of FD-RAN and present a deep reinforcement learning based online multi-connectivity mobility management scheme. We formulate a UE dynamic multiple access problem and transform it into a handover decision problem, then apply the double deep Q-network (DDQN) algorithm to make real time mobility management decisions. Simulation results show that the proposed scheme outperforms benchmarks in terms of handover frequency and quality of service, while ensuring real-time performance.

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Development of Programmed Autonomous Electric Heavy Vehicle: An Application of IoT

Development of Programmed Autonomous Electric Heavy Vehicle:...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： P. Nagasekhar Reddy P. Umaeswari L. Natrayan Ankur Choudhary Department of Electrical and Electronics Engineering Mahatma Gandhi Institute of Technology Hyderabad India Department of Computer Science and Business Systems R.M.K Engineering College Kavaraipettai India Department of Mechanical Engineering Saveetha School of Engineering Simats India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

The influence of automation in the agriculture and construction industry plays a vital role in the development of the economic backbone of any country. The factors such as power, torque and speed are efficiently controlled by automation devices using Internet of things (IoT). The heavy vehicles are the multi-faced application carrier which is been used in construction, agriculture, mining and other heavy-duty fields. This research focuses on developing a IoT integrated sensor-based obstacle detection system for programmed autonomous heavy vehicles which will improve safety in both on-road and off-road construction. The automation process consists of two main categories: Observing the environment and recognizing the activities. The environment in our case is both highways and rural roads. An intelligent- camera is utilised to capture the activities from the captured video and process the data. A LiDAR sensor performs the obstacle detection process using laser reflection technology and an ultrasonic sensor is used to process the vibration and sound produced by the obstacle or upcoming vehicles. These three sensors are integrated with a controller-based device to be powered by the power distribution system in the programmed autonomous heavy vehicle.

关键词： Vibrations Laser radar Automation Torque Cameras Acoustics Agriculture

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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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Adaptive Distributed Learning with Byzantine Robustness: A Gradient-Projection-Based Method

Adaptive Distributed Learning with Byzantine Robustness: A G...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xinghan Wang Cheng Huang Jiahong Ning Tingting Yang Xuemin Shen School of Cyber Science and Engineering Southeast University Nanjing China Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada School of Dalian Maritime University Dalian China Peng Cheng Laboratory Shenzhen China

In this paper, we propose an adaptive distributed learning algorithm that not only resists three types of Byzantine attacks (i.e., gradient negative direction attacks, gradient partial dimension zeroing attacks, gradient scaling attacks) but also ensures high model accuracy. The proposed algorithm is built on a fully distributed model: clients share their local model updates with a group of dynamic committee clients, who cooperatively and iteratively train a global model. Specifically, to counter gradient negative direction attacks, we design a method based on gradient projection that maps clients' local gradients into small subspaces. The design allows committee clients to efficiently and precisely filter out adversarial clients by comparing angles between these subspaces. Moreover, considering that data heterogeneity among clients may cause misdetections of gradient partial dimension zeroing and scaling attacks, thereby reducing model accuracy, we introduce an adaptive multi-dimensional scoring method, which is applied after the gradient-projection-based filtering. The method assists committee clients in scoring and selecting most suitable clients for model aggregation using three hyperparameters, and thus achieves a balance between model accuracy and security. Finally, we conduct extensive experiments on real-world datasets to show the proposed algorithm's effectiveness: it can achieve Byzantine robustness and simultaneously maintain high model accuracy.

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Optimal scheduling of ESS in distribution system with multiple DG 13

Optimal scheduling of ESS in distribution system with multip...

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13th International Conference on electrical and Electronics engineering, ELECO 2021

作者： Tunçel, Suleyman Seker, Ayse Aybike Oskouei, Morteza Zare Gozel, Tuba Mohammadi-Ivatloo, Behnam Abapour, Mehdi Gebze Technical University Department of Electronics Engineering Kocaeli41400 Turkey Department of Electrical and Electronics Engineering Iskenderun Technical University Ratay 31200 Turkey Smart Energy Systems Laboratory University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9786050114379

Increasing tendency to highly penetrate distribution networks with renewables due to environmental concerns, has lead distribution network operators to establish energy storage systems (ESS) for obtaining required flexibility. In this paper, the role of a widely used ESS, namely, compressed-air energy storage (CAES) is evaluated considering hosting distributed generation capacity of network. A deterministic scheduling strategy is proposed to minimize cost of operation and maintenance of network in presence of PV and wind generation as renewable sources. By utilization of CAES, renewable energy curtailment has been reduced while total electricity cost injected from the grid is minimized. Optimal charge and discharge scheduling of CAES has allowed excess renewable energy to be stored and then injected to the network on peak hours in order to reduce cost by decreasing power grid dependency on peak hours. © 2021 Chamber of Turkish electrical Engineers.

关键词： Compressed air

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Early Prediction of Student Performance with LSTM-Based Deep Neural Network

Early Prediction of Student Performance with LSTM-Based Deep...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Han Wan Mengying Li Zihao Zhong Xiaoyan Luo State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Image Processing Center School of Astronautics Beihang University Beijing China

The shift to hybrid teaching during the COVID-19 pandemic brought about a real challenge to predict student performance and conduct timely interventions on at-risk students. This study proposed a deep neural network supporting the early prediction of student performance. Bidirectional LSTM, Global Average Pooling, and TIME MASK structure were utilized in the improved GritNet model. Subsequently, this study optimized the hyperparameters with the aid of the hill-climbing algorithm. Finally, on-campus data sets were used in experiments to evaluate the model's performance. Data were collected from a course that carried out multiple iterations from Fall 2021 to Fall 2022. In Fall 2022, the proposed model achieved a ROC-AUC value of 95.47% in the 8th week, while the baseline model only achieved 91.44% in the same week. Besides, the proposed model achieved a ROC-AUC value of 89.67% in the 4th week, which meant it had acceptable prediction performance in the early stage. The experimental findings demonstrated that the model was capable of predicting the academic performance of students in hybrid courses early on.

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Design and Validation of a Very Low-Power Phasor Measurement Unit

arXiv

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arXiv 2024年

作者： Lythgoe, Zachary J. Long, Thomas F. Buchholz, Michael J. Livernois, Anthony R. Kanuteh, Kebba Allee, David R. Pal, Anamitra Graham, Ian R. Drummond, Zachary D. School of Electrical Computer and Energy Engineering Arizona State University Tempe United States Department of Physics and Astronomy University of Pennsylvania Philadelphia United States DEVCOM Army Research Laboratory Adelphi United States

Phasor measurement units (PMUs) provide a high-resolution view of the power system at the locations where they are placed. As such, it is desirable to place them in bulk in low voltage distribution circuits. However, the power consumption of a PMU/micro-PMU is in the order of Watts (W) that results in them requiring an external power supply, which in turn increases the overall cost. This work details the hardware design of a PMU capable of measuring and reporting voltage and current phasors for a single-phase system at an average power consumption of only 30.8 mW – one to two orders of magnitude lower than existing academic and commercial PMUs. This enables the proposed PMU to run for two weeks using an 11-Wh battery or indefinitely if paired with an inexpensive solar panel. A testbench developed in accordance with the 2018 IEC/IEEE 60255-118-1 PMU Standard confirms the accuracy of this PMU. Given its low power consumption, the proposed design is expected to accelerate adoption of PMUs in modern distribution grids. © 2024, CC BY-NC-ND.

关键词： Field programmable gate arrays (FPGA)

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