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Personalized Privacy-Preserving Routing Mechanism Design in Payment Channel Network

Journal of Computer Science & Technology 2024年第6期39卷 1380-1400页

作者： Peng-Cheng Zhao Li-Jie Xu Jia Xu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China

Payment Channel Network(PCN)provides the off-chain settlement of *** is one of the most promising solutions to solve the scalability issue of the *** routing techniques in PCN have been ***,both incentive attack and privacy protection have not been considered in existing *** this paper,we present an auction-based system model for PCN routing using the Laplace differential privacy *** formulate the cost optimization problem to minimize the path cost under the constraints of the Hashed Time-Lock Contract(HTLC)tolerance and the channel *** propose an approximation algorithm to find the top K shortest paths constrained by the HTLC tolerance and the channel capacity,i.e.,top K-restricted shortest ***,we design the probability comparison function to find the path with the largest probability of having the lowest path cost among the top K-restricted shortest paths as the final ***,we apply the binary search to calculate the transaction fee of each *** both theoretical analysis and extensive simulations,we demonstrate that the proposed routing mechanism can guarantee the truthfulness and individual rationality with the probabilities of 1/2 and 1/4,*** can also ensure the differential privacy of the *** experiments on the real-world datasets demonstrate that the privacy leakage of the proposed mechanism is 73.21%lower than that of the unified privacy protection mechanism with only 13.2%more path cost compared with the algorithm without privacy protection on average.

关键词： blockchain payment channel network routing mechanism differential privacy personalized privacy-preserving

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Unsupervised social network embedding via adaptive specific mappings

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Frontiers of Computer Science 2024年第3期18卷 61-71页

作者： Youming GE Cong HUANG Yubao LIU Sen ZHANG Weiyang KONG School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510275China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006China

In this paper,we address the problem of unsuperised social network embedding,which aims to embed network nodes,including node attributes,into a latent low dimensional *** recent methods,the fusion mechanism of node attributes and network structure has been proposed for the problem and achieved impressive prediction ***,the non-linear property of node attributes and network structure is not efficiently fused in existing methods,which is potentially helpful in learning a better network *** this end,in this paper,we propose a novel model called ASM(Adaptive Specific Mapping)based on encoder-decoder *** encoder,we use the kernel mapping to capture the non-linear property of both node attributes and network *** particular,we adopt two feature mapping functions,namely an untrainable function for node attributes and a trainable function for network *** the mapping functions,we obtain the low dimensional feature vectors for node attributes and network structure,***,we design an attention layer to combine the learning of both feature vectors and adaptively learn the node *** encoder,we adopt the component of reconstruction for the training process of learning node attributes and network *** conducted a set of experiments on seven real-world social network *** experimental results verify the effectiveness and efficiency of our method in comparison with state-of-the-art baselines.

关键词： network embedding specific kernel mapping attention mechanism

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Efficient Modeling of Three Types Photovoltaic Panels Characteristics with Experimental Validation under Variable Weather Conditions

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Applied Solar Energy (English translation of Geliotekhnika) 2023年第6期59卷 903-918页

作者： Hali, A. Khlifi, Y. Laboratory of Renewable Energy Embedded Systems and Data Processing National School of Applied Sciences Oujda Morocco

Abstract: This paper presents a validation of a proposal combined analytical and numerical approach applied to a single diode model of photovoltaic (PV) module for extracting its five PV parameters: shunt resistance, series resistance, diode ideality factor, photo-generated current and saturation current. This method is tested using data provided by manufacturer’s datasheets for three PV panels technologies: multicrystalline Kyocera (KC175GHT-2), monocrystalline Silicon Shell (SQ-150PC) and heterojunction with amorphous silicon "intrinsic thin-layer" "HIT-240HDE4" under variable environmental conditions. The simulation results in MATLAB environment show a good agreement between simulated and experimental power-voltage and current-voltage characteristics for different irradiation levels and temperature values. This accuracy of the proposed method has been confirmed by lowest root mean square error (RMSE) whatever the weather conditions compared to recent conventional approaches reported in the literature. Furthermore, this new approach is tested experimentally on three types of photovoltaic modules’ data provided by "NREL": The National Renewable Energy laboratory, USA. An accurate knowledge of photovoltaic panel parameters from measurement data is essential for solar panels quality control, design and estimating their performance. Indeed, the photovoltaic panel is prone to degrading over time owing to aging and weather exposure. Therefore, predicting these performance degradations is key to avoid their negative impacts on PV production. For this purpose, this work presents a fast, simple, and precise approach of PV parameters extraction to obtain an exact model which more accurately emulates the photovoltaic modules characteristics under a large interval of temperature and irradiation levels, and valid for different PV technologies. Also, from the performance comparison of these three PV panel technologies, we have concluded that the monocrystalline module shows the

关键词： Solar panels

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Fast and Efficient Way of PV Parameters Estimation Based on Combined Analytical and Numerical Approaches

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Applied Solar Energy (English translation of Geliotekhnika) 2023年第2期59卷 135-151页

作者： Hali, Aissa Khlifi, Yamina Laboratory of Renewable Energy Embedded Systems and Data Processing National School of Applied Sciences Oujda Morocco

Abstract—: In this work, a new simpler and more efficient method is proposed to estimate the unknown photovoltaic (PV) parameters of solar cells and PV modules. The proposed method is based on combined analytical and numerical (CAN) approaches. Since the theory and structure of this method are succinct, it can be applied easily. For this study, a single-diode model known as a five parameters model was chosen for modeling the solar cells and PV modules. The proposed technique of PV parameters determination aims to minimize the absolute error between experimental and calculated output current while increasing the speed of convergence to the optimum solution. The accuracy of the suggested approach is tested on a commercial monocrystalline silicon (R.T.C France) solar cell at 33°C and 1000 W/m2 and two PV modules: a commercial Photowatt-PWP 201 in which 36 polycrystalline silicon cells are connected in series with experimental current-voltage characteristic given at 45°C and 1000 W/m2, and an amorphous module referred to as Cocoa aSiMicro03036 with varying environmental conditions. Comprehensive results and statistical analysis indicate that the proposed CAN method is more accurate than most of the published techniques. This accuracy has been proven by lowest statistical errors for all treated data. This good agreement is identified also by the lowest root mean squared error obtained with a value of 7.920179 × 10−4 and 2.083999 × 10−3 for the case RTC France solar cell and Photowatt-PWP 201 PV module respectively, while being lower than 3.948 × 10−4 for the case of Cocoa aSiMicro03036 PV module. © 2023, Allerton Press, Inc.

关键词： Solar panels

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Practical frequency-hopping MIMO joint radar communications:Design and experiment

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Digital Communications and Networks 2024年第6期10卷 1904-1914页

作者： Jiangtao Liu Kai Wu Tao Su J.Andrew Zhang National Key Laboratory of Radar Signal Processing Xidian UniversityXi'anShanxi 710071China Global Big Data Technologies Centre University of Technology SydneyNSW 2007Australia

Joint Radar and Communications(JRC)can implement two Radio Frequency(RF)functions using a single of resources,providing significant hardware,power and spectrum savings for wireless systems requiring both ***-Hopping(FH)MIMO radar is a popular candidate for JRC because the achieved communication symbol rate can greatly exceed the radar pulse repetition ***,practical transceiver imperfections can cause many existing theoretical designs to *** this work,we reveal for the first time the non-trivial impact of hardware imperfections on FH-MIMO JRC and model the impact *** also design new waveforms and correspondingly develop a low-complexity algorithm to jointly estimate the hardware imperfections of unsynchronized *** addition,using low-cost software-defined radios and Commercial Off-The-Shelf(COTS)products,we build the first FH-MIMO JRC experimental platform with simultaneous over-the-air radar and communication *** by simulation and experimental results,the proposed designs achieve high performance for both radar and communications.

关键词： Joint radar and communications Frequency-hopping MIMO radar Sampling timing offset Carrier frequency offset Front-end errors Software-defined radio Commercial off-the-shelf

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Introducing a high-throughput energy-efficient anti-collision (HT-EEAC) protocol for RFID systems

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International Journal of Intelligent Networks 2024年第1期5卷 1-9页

作者： El Mattar, Sara Baghdad, Abdennaceur Laboratory of Electronics Energy Automatics and Data Processing Department of Electrical Engineering Faculty of Sciences and Techniques of Mohammedia Hassan II University Casablanca Morocco

The utilization of radio frequency identification (RFID) technology has witnessed a substantial rise in recent years, and this upward trend is expected to continue in the coming years. It is often described as an industry-changing concept, which is why continuous improvements in the technique of object identification are still needed. These enhancements are related to security, speed, and reliability of communications. RFID is frequently used in areas where a large number of things must be identified. Therefore, some problems of tag collision when many tags are transmitting their data are presented by these systems. The research presented here thoroughly analyzes and evaluates existing tag reading approaches that offer low power consumption during identification. The outcome of this work is the proposal of energy-efficient anti-collision protocol, namely HT-EEAC. By assessing the energy consumed for HT-EEAC at checkpoints, we were able to increase throughput and reduce energy consumption in our protocol. The HT-EEAC approach for updating frame size aims to enhance the energy and throughput of the EPC C1 G2 UHF RFID standard. Performance comparisons demonstrate the advantages of the strategy we propose in terms of low energy consumption per identified tag, minimal collisions, and high throughput. As a result, this method can be effectively utilized in the field of RFID systems to significantly enhance their efficiency. © 2024

关键词： Energy efficiency

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Wireless Charging Scheduling for Long-term Utility Optimization

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ACM Transactions on Sensor Networks 2025年第1期21卷 1-31页

作者： Xu, Jia Chen, Wenbin Dai, Haipeng Xu, Lijie Xiao, Fu Liu, Linfeng Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China The State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Wireless power transmission has been widely used to replenish energy for wireless sensor networks, where the energy consumption rate of sensor nodes is usually time varying and indefinite. However, few works have investigated the problem of long-term charging scheduling with random variable. This article designs an optimization model for the long-term scheduling of chargers to maximize the time-averaged charging utility while ensuring its time-averaged constraints of budget and response rate. The Lyapunov optimization technique is adopted to transform the stochastic optimization problem into a deterministic optimization problem, which remains NP-hard. Thus, an approximation algorithm following greedy approach is proposed to solve the deterministic optimization problem. We further provide the theoretical analysis of feasibility and performance guarantee of the proposed scheduling algorithm. The simulation results show that our algorithm outperforms three comparison algorithms by 6.53%, 20.04%, and 19.97% in terms of time-averaged charging utility, as well as by 11.25%, 4.42%, and 3.73% in terms of time-averaged response rate on average. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Inductive power transmission

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Underwater target recognition based on adaptive multi-feature fusion network

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Multimedia Tools and Applications 2025年第10期84卷 7297-7317页

作者： Pan, Xiaoying Sun, Jia Feng, TianHao Lei, MingZhu Wang, Hao Zhang, WuXia Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an710121 China School of Computer Science and Technology Xi’an University of Post & Telecommunications Xi’an710121 China

Due to the complexity of the underwater environment, underwater acoustic target recognition is more challenging than ordinary target recognition, and has become a hot topic in the field of underwater acoustics research. In recent years, deep learning has been widely used in underwater recognition due to its powerful feature learning capabilities. However, most deep learning-based underwater target recognition methods use only the time-frequency domain features of signals, ignoring the frequency domain image features of signals. Additionally, they only use simple summation or splicing strategies to fuse and discriminate the extracted features, which leads to limited improvement in the accuracy of underwater target recognition. To address these issues, this paper proposes an underwater target recognition method based on an adaptive multi-feature fusion network. The method consists of a data pre-processing module, a multi-dimensional feature extraction module, and an adaptive multi-feature fusion module. In the multi-dimensional feature extraction module, the long short time memory network (LSTM) and the one-dimensional convolutional neural network (1DCNN) are used to extract the time-frequency features of the underwater signal. Furthermore, the two-dimensional convolutional neural network (2DCNN) is used to extract the image features in the frequency domain. The adaptive multi-feature fusion module uses an attention mechanism for adaptive weighted fusion to make full use of the learned features. The effectiveness of the proposed method is validated on the ShipEar dataset, and the recognition accuracy reaches 94.92%, which is higher than other existing methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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L_(1)-Smooth SVM with Distributed Adaptive Proximal Stochastic Gradient Descent with Momentum for Fast Brain Tumor Detection

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Computers, Materials & Continua 2024年第5期79卷 1975-1994页

作者： Chuandong Qin Yu Cao Liqun Meng School of Mathematics and Information Science North Minzu UniversityYinchuan750021China Ningxia Key Laboratory of Intelligent Information and Big Data Processing Yinchuan750021China

Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous *** tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment *** learning models have become key players in automating brain tumor *** descent methods are the mainstream algorithms for solving machine learning *** this paper,we propose a novel distributed proximal stochastic gradient descent approach to solve the L_(1)-Smooth Support Vector Machine(SVM)classifier for brain tumor ***,the smooth hinge loss is introduced to be used as the loss function of *** avoids the issue of nondifferentiability at the zero point encountered by the traditional hinge loss function during gradient descent ***,the L_(1) regularization method is employed to sparsify features and enhance the robustness of the ***,adaptive proximal stochastic gradient descent(PGD)with momentum,and distributed adaptive PGDwithmomentum(DPGD)are proposed and applied to the L_(1)-Smooth *** computing is crucial in large-scale data analysis,with its value manifested in extending algorithms to distributed clusters,thus enabling more efficient processing ofmassive amounts of *** DPGD algorithm leverages Spark,enabling full utilization of the computer’s multi-core *** to its sparsity induced by L_(1) regularization on parameters,it exhibits significantly accelerated convergence *** the perspective of loss reduction,DPGD converges faster than *** experimental results show that adaptive PGD withmomentumand its variants have achieved cutting-edge accuracy and efficiency in brain tumor ***-trained models,both the PGD andDPGD outperform other models,boasting an accuracy of 95.21%.

关键词： Support vector machine proximal stochastic gradient descent brain tumor detection distributed computing

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Low Complexity Joint Spectrum Resource and Power Allocation for Ultra Dense Networks

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China Communications 2023年第5期20卷 104-118页

作者： Qiang Wang Yanhu Huang Qingxiu Ma Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal UniversityYulin 537000China

In this paper,we propose a low complexity spectrum resource allocation scheme cross the access points(APs)for the ultra dense networks(UDNs),in which all the APs are divided into several AP groups(APGs)and the total bandwidth is divided into several narrow band spectrum resources and each spectrum resource is allocated to APGs independently to decrease the interference among the ***,we investigate the joint spectrum and power allocation problem in UDNs to maximize the overall *** problem is formulated as a mixed-integer nonconvex optimization(MINCP)problem which is difficult to solve in *** joint optimization problem is decomposed into two subproblems in terms of the spectrum allocation and power allocation *** the spectrum allocation,we model it as a auction problem and a combinatorial auction approach is proposed to tackle *** addition,the DC programming method is adopted to optimize the power allocation *** decrease the signaling and computational overhead,we propose a distributed algorithm based on the Lagrangian dual *** results illustrate that the proposed algorithm can effectively improve the system throughput.

关键词： ultra dense networks resource allocation combinatorial auction optimization algorithm

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