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Safe Reinforcement Learning aided Lightweight Cooperation for Multi-UAV Data Collection in Random WSN

IEEE Internet of Things Journal 2025年

作者： Bai, Zixuan Shi, Jia Li, Zan Zhang, Peichang Zheng, Tong-Xing Xidian University State Key Laboratory of Integrated Service Networks School of Telecommunications Engineering Xi'an710071 China Shenzhen University College of Electronics and Information Engineering Shenzhen China Xi'an Jiaotong University School of Information and Communications Engineering Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi'an710049 China

Unmanned aerial vehicle (UAV) data collection problems in wireless sensor networks (WSNs) under random and uncertain environments are critical challenging, due to massive burden of real-time communication among UAVs for aligning with observation and state information, ect. For this sake, this work investigates the UAV cooperation problem of WSN data collection by jointly maximizing collected data amount while minimizing cooperation cost. We formulate the problem as a constrained partially observable Markov decision process (CPO-MDP), which stimulates the design of a novel safe reinforcement learning aided lightweight cooperation (SRL-LC) framework for multi-UAV data collection. Speficially, the self-conscious cooperative communication scheme is developed to assist the optimization of the UAV trajectory decision making. Additionally, a safety module embedded in the decision network integrates a relaxed artificial potential field (APF) algorithm, enabling UAVs to maintain safety distance constraints during training. Simulation results demonstrate that the proposed SRL-LC framework achieves data collection performance comparable to the full-cooperation scheme for various settings of prior information, while reducing communication cost by approximately 85%. Moreover, the SRL-LC framework ensures zero violations of safety constraints throughout the training process. © 2014 IEEE.

关键词： Drones

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Multi-routine-data driven spatio-temporal short-term predictions for surface ozone in China

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Air Quality, Atmosphere and Health 2025年 1-10页

作者： Zheng, Canju Shen, Hengqing Sun, Jianan Liu, Guangliang Cao, Haowei Zhang, Jie Gong, Xiang Xu, Da Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Environment Research Institute Shandong University Shandong Qingdao 266237 China Ningbo Yonghuanyuan Environmental Engineering and Technology Co. LTD Zhejiang Ningbo 315012 China School of Mathematics and Physics Qingdao University of Science and Technology Qingdao 266061 China Qingdao Innovation Center of Artificial Intelligence Ocean Technology Qingdao 266061 China

Ozone (O3) is a major atmospheric pollutant, and accurate prediction of its concentrations remains challenging due to its complex nonlinear relationships with precursor compounds. Existing machine learning methods have mainly focused on single-site or spatial predictions, lacking research on spatio-temporal short-term predictions based on simple factors. To address this gap, the MRD-O3former, a deep learning model driven by multi-routine data, was developed to predict short-term hourly spatio–temporal surface ozone concentrations over China. The model exhibits strong spatio–temporal consistency, achieving a high correlation coefficient (r2=0.85∼0.90) and low normalized mean biases (NMBs) between -15% and 15% at the national scale compared to reanalysis ozone data. Both ablation experiments and permutation importance analysis reveal that historical ozone levels play a primary role in next-day ozone prediction, while meteorological factors such as wind speed and temperature also make significant contributions. Regional validation confirms the model’s effectiveness in the Beijing-Tianjin-Hebei region. Moreover, the study investigates the differential impact of crucial factors in urban and rural areas, revealing that historical ozone levels and meteorological factors significantly influence rural areas. However, the influence of historical ozone levels on urban ozone prediction is relatively small, especially during the summer. This suggests that urban ozone undergoes rapid formation and removal processes. These findings highlight the promising potential of deep learning techniques in accurately predicting spatiotemporal short-term ozone concentrations and interpreting the mechanism and source for ozone pollution. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Data-driven Multi-factor Ozone concentration Spatio-temporal prediction

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Robust Transmission Capacity Margin Evaluation Based on All-Scenario-Feasible Optimization 36

Robust Transmission Capacity Margin Evaluation Based on All-...

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第36届中国控制会议

作者： Weisheng Liu Xiaohong Guan Jiang Wu Feilong Song Dong Xie Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University

ISBN: (纸本)9781538629185

A robust and effective evaluation method for the safety of optimal generation scheduling has great potential on optimizing operation of electrical power and energy *** on the concept of transmission capacity margin,a robust model of transmission capacity margin evaluation（TCME） is established to assess the overload risk of transmission lines when loads *** TCME method can provide the system operators with useful information such as possibly overloaded lines and time periods when lines are potentially overloaded under the uncertainties of nodal ***,TCME formulation has a multi-level（min-max） structure and the solution methods are usually *** order to overcome the computational complexity of original problem,an All-Scenario-Feasible optimization is *** is established based on very limited scenarios while the solution is feasible to all *** proposed formulation is single-level rather than *** no iterative algorithms are needed and commercial solver like CPLEX could be directly *** order to use the proposed formulation for large-scale power grid,considering that the transmission capacity constraint is the most important one in all problem constraints,we further reduce the number of *** testing is also performed and the results show that the proposed formulation is effective.

关键词： robust transmission capacity margin All-Scenario-Feasible

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A Wind Power Time Series Modeling Method Based on Stochastic Burgers Equation 36

A Wind Power Time Series Modeling Method Based on Stochastic...

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第36届中国控制会议

作者： Xie Dong Wu Jiang Song Feilong Liu Weisheng Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University

ISBN: (纸本)9781538629185

With more wind power integrating into power grid,a lot of issues are caused by its uncertainty and *** order to analysis wind characters,this paper proposes a method to model wind *** method bases on stochastic Burgers *** turns wind power model from three dimension to one dimension in order to simplify the physical model and generate more wind speed data to improve *** uses Gaussian distribution to fit the *** uses the Park model to calculate the wake *** it turns the wind speed data to wind power ***,the scheme is applied to a 30-turbines case.

关键词： wind power stochastic Burgers equation Park model

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AOD estimation in WSN localization system with synthetic aperture technique

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Science China(Information Sciences) 2012年第10期55卷 2216-2225页

作者： WANG WenJie,YAO BoBin & YIN QinYe MOE key Lab for intelligent networks and network security,School of Electronic and Information Engineering,Xi’an Jiaotong University,Xi’an 710049,China MOE Key Lab for Intelligent Networks and Network Security School of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China

Angel of departure(AOD) based distributed localization scheme in wireless sensor networks allows the single antenna sensor node to estimate its own AOD information by an equivalent antenna array for further position ***,the condition that the number of antennas at the multi-antenna agent/anchor node should be larger than the number of multipath signal components(MPCs) constrains its actual *** this paper,we provide a more practical approach to achieve AOD information estimation under multipath *** that the sensor node can not identify the line of sight(LOS) signal component due to the insufficient degree of freedom,which is determined by the above limiting condition,we introduce a synthetic aperture technique with the aid of a fast moving multi-antenna agent/anchor *** operation greatly enlarges the degree of freedom of the equivalent antenna array constructed at sensor node end but without requiring other extra physical antennas equipped at agent/anchor node or sensor node end,which means that it relaxes the limiting ***,the AOD information of LOS signal component can be estimated and extracted successfully in complex multipath *** analysis proves the effectiveness of our proposed synthetic aperture technique and numerical simulations show great performance improvement.

关键词： angle of departure degree of freedom synthetic aperture wireless sensor networks

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MRLATO: An Adaptive Task Offloading Mechanism Based on Meta Reinforcement Learning in Edge Computing Environment

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Peiying Liu, Jiamin Guizani, Maher Wang, Jian Kumar, Neeraj Tan, Lizhuang Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao266580 China Ministry of Education Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China University of Texas Arlington Computer Science and Engineering Department TX United States College of Science Qingdao266580 China Thapar University Department of Computer Science and Engineering Patiala147004 India Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China

Traditional cloud computing models struggle to meet the requirements of latency-sensitive applications when processing large amounts of data. As a solution, Multi-access Edge Computing (MEC) extends computing resources to the edge of the network to reduce processing delays and improve user experience. However, in dynamically changing edge computing environments, effective decision making on whether to offload tasks to edge servers remains a core challenge. For this purpose, we propose MRLATO, an adaptive task offloading mechanism based on Meta Reinforcement Learning (MRL), which exploits a large amount of a priori knowledge of different tasks to achieve fast adaptation. The task offloading process is modelled as multiple Markov Decision Processes (MDPs) and solved using a Sequence to Sequence (Seq2Seq) neural network integrating multi-head attention and recursive task sequencing. It is shown by the experimental results that the proposed method has the lowest latency in all experimental settings and the convergence efficiency is improved by 14.06% compared to the traditional Deep Reinforcement Learning (DRL) algorithm. This research fully demonstrates the significant benefits of the deep integration of MRL with the edge computing domain, providing new optimisation ideas for task offloading decisions. © 1967-2012 IEEE.

关键词： Computation offloading

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On cross-correlation indicators of an S-box

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Frontiers of Computer Science 2011年第4期5卷 448-453页

作者： Sheng GAO Wenping MA Zepeng ZHUO Fenghe WANG Key Laboratory of Computer Network and Information Security Ministry of Education Xidian University Xi＇an 710071 China State Key Laboratory of Integrated Service Networks Xidian University Xi＇an 710071 China

Substitution boxes （S-boxes） are often used as the most important nonlinear components in many symmetric encryption algorithms. The cryptographic properties of an S-box directly affect the security of the whole cipher system. Recently, generalized global avalanche characteristics （GGAC） were introduced to measure the correlation between two arbitrary Boolean functions. In this paper, to better evaluate the security of an S-box, we present two cross-correlation indicators for it. In addition, by studying the related properties of the cross-correlation between two balanced Boolean func- tions, we propose the lower bounds on the sum-of-squares indicator related to GGAC for two balanced functions and also for an S-box.

关键词： cryptography Boolean functions substitu-tion boxes balancedness generalized global avalanchecharacteristics cross-correlation

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Hierarchical Task Planning for Power Line Flow Regulation

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 29-40页

作者： Chenxi Wang Youtian Du Yanhao Huang Yuanlin Chang Zihao Guo the Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong UniversityXi'an 713599China the State Key Laboratory of Power Grid Safety and Energy Conservation China Electric Power Research InstituteBeijing 100192China

The complexity and uncertainty in power systems cause great challenges to controlling power *** a popular data-driven technique,deep reinforcement learning(DRL)attracts attention in the control of power ***,DRL has some inherent drawbacks in terms of data efficiency and *** paper presents a novel hierarchical task planning(HTP)approach,bridging planning and DRL,to the task of power line flow ***,we introduce a threelevel task hierarchy to model the task and model the sequence of task units on each level as a task planning-Markov decision processes(TP-MDPs).Second,we model the task as a sequential decision-making problem and introduce a higher planner and a lower planner in HTP to handle different levels of task *** addition,we introduce a two-layer knowledge graph that can update dynamically during the planning procedure to assist *** results conducted on the IEEE 118-bus and IEEE 300-bus systems demonstrate our HTP approach outperforms proximal policy optimization,a state-of-the-art deep reinforcement learning(DRL)approach,improving efficiency by 26.16%and 6.86%on both systems.

关键词： Knowledge graph power line flow regulation reinforcement learning task planning

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Rate-Splitting Multiple Access Enabled Green Probabilistic Semantic Communication over Wireless networks

IEEE Transactions on Green Communications and Networking

引用

IEEE Transactions on Green Communications and networking 2025年

作者： Xu, Ruopeng Yang, Zhaohui Mao, Yijie Huang, Chongwen Yang, Qianqian Xu, Lexi Xu, Wei Zhang, Zhaoyang Zhejiang University College of Information Science and Electronic Engineering China Hangzhou310027 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Zhejiang Prov. Key Lab. of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China China United Network Communications Corporation Research Institute Beijing100048 China Southeast University National Mobile Communications Research Laboratory Nanjing211189 China

In this paper, we propose a multi-user green probabilistic semantic communication (PSC) system for semantic communication (SemCom) facilitated by probabilistic knowledge graphs (PKGs), which are knowledge graphs (KGs) integrated with probability to represent semantic information. In the considered model, semantic information of different users is represented by PKGs. On this basis, we design a semantic compression model for multi-user downlink task-oriented SemCom, utilizing the semantic compression ratio (SCR) as a parameter to connect the computation and communication processes of information transmission. Utilizing rate-splitting multiple access (RSMA) technology, the transmitted messages are splitted into shared and private ones. Considering the limited wireless resources and constraint in semantic communication, an optimization problem with the goal of minimizing system energy consumption comprehensively considering the computation and communication process is formulated. In order to address the optimization problem, we propose an alternating optimization algorithm that tackles sub-problems of power allocation and beamforming design with successive convex approximation (SCA) method, semantic compression ratio optimization with variable substitution method, and computation capacity allocation with obtaining closed-form optimal solution. Simulation results verify the effectiveness of the proposed algorithm. © 2017 IEEE.

关键词： Knowledge graph

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A new class of S-boxes with optimal Feistel boomerang uniformity

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Cryptography and Communications 2025年 1-13页

作者： Lu, Yuxuan Mesnager, Sihem Li, Nian Wang, Lisha Zeng, Xiangyong Ministry of Education the Hubei Provincial Engineering Research Center of Intelligent Connected Vehicle Network Security School of Cyber Science and Technology Hubei University Wuhan430062 China Department of Mathematics University of Paris VIII Saint-Denis93526 France University Sorbonne Paris Nord CNRS UMR 7539 VilletaneuseF-93430 France Télém Paris Palaiseau France Ministry of Education Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan430062 China

The Feistel Boomerang Connectivity Table (FBCT), which is the Feistel version of the Boomerang Connectivity Table (BCT), plays a role in analyzing block ciphers’ ability to withstand strong attacks, such as boomerang attacks. However, as of now, only four classes of power functions are known to have explicit values for all entries in their FBCT. In this paper, we focus on studying the FBCT of the power function F(x)=x2n-2-1 over F2n, where n is a positive integer. Through certain refined manipulations to solve specific equations over F2n and employing binary Kloosterman sums, we determine explicit values for all entries in the FBCT of F(x) and further analyze its Feistel boomerang spectrum. Finally, we demonstrate that this power function exhibits the lowest Feistel boomerang uniformity. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Digital arithmetic

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