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UAV Swarm Path Planning for Sensor Data Collection via Double Pre-Partitioned Deep Q-network

IEEE Sensors Journal 2025年

作者： Zhang, Heng Gao, Yuan Wang, Xin He, Yunpeng Yang, Ming Wu, Xiaoming Yang, Chaoqun Cao, Xianghui Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250014 China Jiangsu Ocean University School of Computer Engineering Lianyungang222000 China Southeast University School of Automation Nanjing210096 China

Collision-free path planning is a significant challenge in deploying unmanned aerial vehicle (UAV) swarms for sensor data collection tasks, particularly in uncertain environments. This paper addresses the problem of designing flight strategies for energy-constrained UAV swarms using deep reinforcement learning. We propose a locally-aware double pre-partitioned deep Q-network (DPPDQN) for flight planning of multiple UAVs in large-scale uncertain industrial environments. This algorithm aims to collect data from ground sensors with communication constraints while avoiding mutual collisions. In the proposed algorithm, we first introduce a node pre-partitioning scheme using an improved clustering algorithm, which efficiently reduces the number of access points for UAVs through effective data aggregation. Subsequently, we employ an area pre-partitioning algorithm that segments the detection area into several subregions based on the number of UAVs available for operation, thereby preventing collisions among the UAV swarm during task execution. Finally, we propose a deep Q-network algorithm incorporating a convolutional neural network, which utilizes the local perceptions of UAVs as state inputs to address the 'curse of dimensionality"occurring in large-scale environments. Systematic simulation experiments validate the effectiveness and advantages of the proposed method. © 2025 IEEE.

关键词： Convolutional neural networks

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The Distributed Intelligent Collaboration to UAV-Assisted VEC: Joint Position Optimization and Task Scheduling

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IEEE Internet of Things Journal 2025年

作者： Yi, Meng Lee, Vincent C.S. Yang, Peng Li, Peisong Zhang, Yifan Wei, Wei Gao, Honghao Southeast University School of Computer Science and Engineering Nanjing211189 China Monash University Faculty of IT Department of DS and AI ClaytonVIC3800 Australia Southeast University School of Computer Science and Engineering the Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing211189 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China Southeast University-Monash University Joint Graduate School Suzhou215123 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Shanghai University School of Computer Engineering and Science Shanghai200444 China

Deploying Unmanned Aerial Vehicles (UAVs) as aerial base stations enhances the coverage and performance of communication networks in Vehicular Edge Computing (VEC) scenarios. However, due to the limited communication range and energy capacity of UAVs, they cannot continuously cover entire areas or sustain long flights. Therefore, achieving full communication coverage of a target area with a minimal number of UAVs and efficient task offloading remains a significant challenge. To address this problem, the UAV-assisted Two-stage Intelligent Collaboration (UTIC) method is proposed in this paper to tackle the joint position optimization and task scheduling issue. Firstly, a UAV-assisted Two-stage Task Scheduling (UTTS) system model is designed to optimize the allocation process. Secondly, an Enhanced Particle Swarm Optimization (E-PSO) algorithm is designed to determine the optimal positions of UAVs, ensuring complete coverage of all mobile vehicles (MVs) with the minimum number of UAVs. Thirdly, Deep Deterministic Policy Gradient (DDPG) method is employed to find the optimal scheduling decisions for MVs, considering energy consumption, delay, and task priorities. Simulation results demonstrate that the proposed UTIC method can achieve nearly 20% reduction in UAV deployment and outperform three other classical reinforcement learning (RL) algorithms in terms of reducing system cost. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Research on Lightweight Sensing Technology Based on Single-Antenna Multicarrier

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IEEE Internet of Things Journal 2025年

作者： Jiang, Yu Zhu, Di Wang, Jiadong Hu, Aiqun Southeast University School of Cyber Scienc and Engineering Purple Mountain Laboratories Nanjing210096 China Key Laboratory of Computer Network Technology of Jiangsu Province Nanjing210096 China Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Southeast University School of Cyber Science and Engineering Nanjing210096 China Southeast University School of Cyber Science and Engineering School of Information Science and Engineering Nanjing210096 China Southeast University State Key Laboratory of Mobile Communication Nanjing210096 China

Channel State information (CSI) serves as a critical indicator of wireless signal conditions and is widely regarded by researchers for its sensitivity in detecting changes within the channel. However, traditional sensing technologies often require substantial data and intricate learning algorithms, highlighting an urgent need for advancements in lightweight sensing technologies. These technologies should leverage simpler terminal devices, reduced data volumes, and more straightforward classification algorithms to achieve sensing capability that are comparable to those offered by more complex and established methods. This paper concentrates on the lightweight application of wireless sensing and encompasses the following key contributions: 1) The development of a lightweight sensing model utilizing a single-antenna multicarrier system, which introduces a CSI ratio model that adapts multi-antenna techniques for single-antenna settings;2) The enhancement of feature stability through the introduction of a complex-plane fitting method using artificial vector, alongside a dual receiver based method for cross-scene feature generation aimed at producing stable and high-quality auxiliary features. Experimental results show that the feature extraction capability of the single-antenna multicarrier CSI ratio model is close to that of traditional multi-antenna scheme. On the gait dataset, when the enhanced CSI ratio is used as a feature, the accuracy is nearly 100%, surpassing the 93% accuracy of the original amplitude feature. On the gesture dataset, the combination of the enhanced CSI ratio and PEI features achieves an accuracy of 96%, which is superior to using the original CSI amplitude feature alone. An analysis of resource consumption shows that the lightweight SVM model incurs very low computational overhead during decision-making, validating the potential of this scheme in terms of efficiency and practical application. © 2014 IEEE.

关键词： Communication channels (information theory)

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Long and Recent Preference Learning with Recent-k Items Distribution for Recommender System

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IEEE Transactions on Multimedia 2025年

作者： Gao, Yongbiao Niu, Sijie Lv, Guohua Ling, Miaogen Geng, Xin Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China Southeast University Ministry of Education Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing School of Information Science and Engineering China Nanjing University of Information Science and Technology School of Computer Science China Southeast University School of Computer Science and Engineering China

Reinforcement learning (RL) aims to formulate the recommendation task as a Markov decision process (MDP) and trains an agent to automatically learn the optimal recommendation policy from interaction trajectories through trial-and-error and reward mechanisms. However, most existing RL-based approaches overlook the correlation between items and the dynamics of user interests implied in temporally close interactions. Therefore, in this paper, we propose a reinforcement learning method that incorporates a "recent-k items"distribution to capture users' local preferences. Specifically, we model the output layer as two distinct branches. The "recent-k items"branch, formulated with a Kullback-Leibler divergence loss, learns the recent interests of users, whereas the other branch utilizes a one-step temporal difference error to capture long-term preferences. The proposed structure is integrated into deep Q-learning and actor-critics, resulting in two enhanced methods named R k Q and R k AC, respectively. Furthermore, a novel soft inter-reward is carefully designed to enhance the proposed method, and we theoretically prove the convergence of the proposed algorithm. We perform extensive experiments on two large real-world datasets and conduct further analysis of the influences of different action sequences, time intervals, and enhancement capabilities for state-of-the-art models. The experimental results demonstrate the efficacy of our proposed methods. © 1999-2012 IEEE.

关键词： Deep reinforcement learning

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Incremental Verification of Evolving BPEL-Based Web Composite Service

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Chinese Journal of Electronics 2016年第1期25卷 6-12页

作者： JI Shunhui LI Bixin QIU Dong School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education

Web composite services inevitably evolve to meet various requirements. However, most researchers only considered the verification of single versions. Simply reapplying the original verification techniques on subsequent versions of composite service as they evolve is costly,especially for large-scale services. In this paper, a new approach called Incremental verification(ICV) is proposed to incrementally verify the non-conflict property of evolved versions of Business process execution language(BPEL)based composite service. With a kind of e Xtensible control flow graph(XCFG) as the formal model to describe the BPEL process, ICV compares the new version with the old one to identify the process changes which bring threats to the non-conflict property. Then it concentrates on the changed elements to perform the verification, which reduces the cost by skipping the analysis of unchanged elements. Case study shows that the proposed approach is effective and more efficient than the original verification method.

关键词： Incremental verification Evolution Web composite service eXtensible control flow graph(XCFG) Non-conflict property

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HilAnchor:Location Privacy Protection in the Presence of Users' Preferences

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Journal of computer Science & Technology 2012年第2期27卷 413-427页

作者：倪巍伟郑锦旺崇志宏 School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration in Southeast University Ministry of Education

Location privacy receives considerable attentions in emerging location based *** current practices however either ignore users＇ preferences or incompletely fulfill privacy *** this paper,we propose a privacy protection solution to allow users＇ preferences in the fundamental query of k nearest neighbors （kNN）.Particularly,users are permitted to choose privacy preferences by specifying minimum inferred *** Hilbert curve based transformation,the additional workload from users＇ preferences is ***,this transformation reduces time-expensive region queries in 2-D space to range the ones in 1-D ***,the time efficiency,as well as communication efficiency,is greatly improved due to clustering properties of Hilbert ***,details of choosing anchor points are theoretically *** empirical studies demonstrate that our implementation delivers both flexibility for users＇ preferences and scalability for time and communication costs.

关键词： location privacy kNN query minimum inferred region users＇ privacy preferences

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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External Reliable information-enhanced Multimodal Contrastive Learning for Fake News Detection

arXiv

引用

arXiv 2025年

作者： Cao, Biwei Wu, Qihang Cao, Jiuxin Liu, Bo Gui, Jie School of Cyber Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Ministry of Education of China Nanjing211189 China Purple Mountain Laboratories Nanjing210000 China School of Computer Science and Engineering Southeast University Nanjing211189 China Engineering Research Center of Blockchain Application Supervision And Management Southeast University Ministry of Education China

With the rapid development of the Internet, the information dissemination paradigm has changed and the efficiency has been improved greatly. While this also brings the quick spread of fake news and leads to negative impacts on cyberspace. Currently, the information presentation formats have evolved gradually, with the news formats shifting from texts to multimodal contents. As a result, detecting multimodal fake news has become one of the research hotspots. However, multimodal fake news detection research field still faces two main challenges: the inability to fully and effectively utilize multimodal information for detection, and the low credibility or static nature of the introduced external information, which limits dynamic updates. To bridge the gaps, we propose ERIC-FND, an external reliable information-enhanced multimodal contrastive learning framework for fake news detection. ERIC-FND strengthens the representation of news contents by entity-enriched external information enhancement method. It also enriches the multimodal news information via multimodal semantic interaction method where the multimodal constrative learning is employed to make different modality representations learn from each other. Moreover, an adaptive fusion method is taken to integrate the news representations from different dimensions for the eventual classification. Experiments are done on two commonly used datasets in different languages, X (Twitter) and Weibo. Experiment results demonstrate that our proposed model ERIC-FND outperforms existing state-of-the-art fake news detection methods under the same settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Design and Implementation of Not So Cooperative Caching System

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China Communications 2015年第7期12卷 31-46页

作者： HU Xiaoyan GONG Jian CHENG Guang ZHANG Weiwei Ahmad Jakalan School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education

Not So Cooperative Caching(NSCC) considers a network comprised of selfish nodes; each is with caching capability and an objective of reducing its own access cost by fetching data from its local cache or from neighboring caches. These nodes would cooperate in caching and share cached content if and only if they each benefit. The challenges are to determine what objects to cache at each node and to implement the system in the context of information Centric networking(ICN). This work includes both a solution for the NSCC problem and a design and implementation of an NSCC system in Named Data networking(NDN), a large effort that exemplifies ICN. Our design applies NDN synchronization protocol to facilitate the information exchange among nodes, adopts group key encryption to control data access within the NSCC group, and offers an error checker to detect error events in the system. Our approach is validated by deploying the system we developed on Planet Lab.

关键词： named data networking content caching selfish caches synchronization group rekeying error checker game theory

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Cone-Beam Computed Tomography Limited-Angle Image Reconstruction Using Planning CT-Guided Deep Diffusion Model

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Wu, Zhan Yang, Yang Hu, Dianlin Lyu, Tianling Li, Yinsheng Tang, Hao Shu, Huazhong Xi, Yan Zhang, Yi Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing210096 China Zhejiang University of Technology Academy for Advanced Interdisciplinary Science and Technology Hangzhou China Chinese Academy of Sciences Key Laboratory of Biomedical Imaging Science and System China Capital Medical University Beijing Jishuitan Hospital China First-Imaging Tech Shanghai China Sichuan University School of Cyber Science and Engineering Chengdu China Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Laboratory of Image Science and Technology the School of Computer Science and Engineering Nanjing210096 China

For existing image-guided radiation therapy systems (IGRT), the limited angular range of the cone-beam computed tomography (CBCT) scanning improves the compactness of the system and compatibility between CBCT and linear accelerator configurations. However, the limited-angle CBCT reconstruction under incomplete projection data causes severe artifacts in the reconstructed images, further having a negative impact on the registration accuracy in IGRT. In this paper, we propose a planning CT-guided deep diffusion model, termed LARGE-DiffNet, to improve imaging quality and registration accuracy for limited-angle CBCT image reconstruction. In stark contrast to existing approaches leveraging hand-crafted regularization, such as local smoothness and/or sparsity of the image, LARGE-DiffNet learns the distribution-level prior knowledge to describe the features of ideal CBCT images via a planning CT-guided diffusion model. The proposed LARGE-DiffNet is evaluated on the clinical CBCT dataset, and extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods. The proposed LARGE-DiffNet technique has great potential in IGRT. © 1963-2012 IEEE.

关键词： cone-beam computed tomography deep learning diffusion model image registration image-guided radiation therapy limited-angle

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