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Successive Interference Cancellation-Enabled Timely Status Update in Linear Multi-Hop Wireless Networks

IEEE Transactions on Mobile Computing 2025年第6期24卷 5298-5311页

作者： Han, Xinhui Pan, Haoyuan Wang, Zhaorui Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Future Network of Intelligence Institute School of Science and Engineering Shenzhen518172 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology College of Computer Science and Software Engineering Shenzhen518060 China

We investigate the timely status update in linear multi-hop wireless networks, where a source tries to deliver status update packets to a destination through a sequence of half-duplex relays. Timeliness is measured by the age of information (AoI) metric. Maintaining a low AoI at the destination typically necessitates frequent transmission of update packets from the source. However, high packet transmission frequency in multi-hop scenarios can result in mutual wireless interference at intermediate relays. Specifically, when an intermediate relay receives wireless signals of a new packet from its previous node, simultaneous transmission of an old packet by its subsequent node to the next hop may cause wireless signals to interfere at the intermediate relay, conventionally leading to packet collision. A key motivation to solve this issue is that the intermediate relay has previously received the old packet (which can thus be forwarded to the subsequent node for further relaying). Hence, successive interference cancellation (SIC) can be employed to mitigate interference of the old packet and recover the new packet. This paper designs an SIC-enabled packet relaying scheme tailored to low AoI. Initially focusing on a three-hop network, we subsequently extend our approach to general multi-hop networks. We model the multi-hop relaying scheme using a Markov chain to derive the theoretical average AoI. Theoretical and simulation results indicate that the SIC-enabled packet relaying scheme significantly reduces the average AoI compared to the non-SIC approaches, owing to an increased packet transmission frequency at the source and the effectiveness of SIC techniques at the relays. © 2002-2012 IEEE.

关键词： Markov chains

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DSCR: A Dynamic Secure Clustering Routing Scheme for UANETs Based on Reputation Mechanism

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IEEE Internet of Things Journal 2025年第10期12卷 14109-14123页

作者： Gong, Yinyan Chen, Yuxiang Li, Kuanching Liang, Wei Li, Xiong Wang, Jin Xiang, Yang Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Sanya Research Institute Hunan University of Science and Technology Sanya572024 China School of Computer Science and Engineering Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Swinburne University of Technology School of Software and Electrical Engineering HawthornVIC3122 Australia

In UAV ad hoc networks (UANETs), rapid movement of nodes leads to frequent changes in network topology, increasing the risk of packet loss and affecting data transmission. Furthermore, current research on drone clustering lacks security considerations, which reduces the reliability of data transmission. Due to this, improving the stability and reliability of network data transmission in dynamically changing UANETs remains a challenge. In this work, we propose the reputation mechanism dynamically secure cluster routing scheme (DSCR) for UANETs, a design for cluster head election in UANETs using the evaluated value and reputation value of drones and forms clusters through this reputation mechanism to avoid malicious nodes from interfering with the clusters to improve the security of UANETs. We apply a residual link survival time-based intra- and inter-cluster algorithm based on residual link survival time for data forwarding of nodes, optimizing the dynamic routing strategy and reducing the packet loss rate of nodes. In addition, reinforcement learning is used in UANETs to achieve cluster decisions based on the current network state and cluster dynamics, effectively improving the stability of clusters. Experiments were conducted on the proposed method to verify its efficiency and stability. Compared to the ICRA, RICR, and DCA algorithms, the stability of the cluster is improved by 11.92%, 28.12%, and 75.2%, respectively, and the packet loss rate reduced by 41.48%, 44.17%, and 55.74%, demonstrating that DSCR is a compelling dynamic routing solution applicable to UANETs. © 2014 IEEE.

关键词： Drones

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OptiMobileX: Optimizing Deep Transfer Learning Model for Accurate Human Posture Recognition Using a Deep Feature Fusion Technique

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IEEE Sensors Journal 2025年第6期25卷 9759-9766页

作者： Ogundokun, Roseline Oluwaseun Damasevicius, Robertas Maskeliunas, Rytis Kaunas University of Technology Department of Software Engineering Kaunas44249 Lithuania Kaunas University of Technology Centre of Real Time Computer Systems Kaunas44249 Lithuania

Human posture recognition (HPR) has garnered growing interest given the possibility of its use in various applications, including healthcare and sports fitness. Interestingly, achieving accurate pose recognition on mobile devices with very little computing power is still tricky. The precise identification of human posture on mobile devices with constrained computational resources is of utmost importance to bring about significant advancements in healthcare, fitness, and technology engagement. By facilitating real-time monitoring and providing personalized solutions, this technology improves accessibility and well-being, revolutionizing our interaction with digital applications. In this work, we used the deep feature concatenation (DFC) mechanism to combine features extracted from input images using two modern pretrained DTL models, MobileNetV2 and Xception, which led to our proposed OptiMobileX: A deep transfer learning (DTL) model based on a DFC of the MobileNetV2 and Xception models for the overall improvement of the identification capability of this posture. The classification accuracy of 96.42% is achieved by the OptiMobileX model, demonstrating the superiority of the proposed model over MobileNetV2, Xception, AlexNet, and InceptionV3, which only manage to obtain 88.17%, 92.47%, 90.32%, and 73.48% accuracy, respectively. The OptiMobileX model exhibits notable capabilities but faces significant limitations. Inadequate details on detecting inappropriate postures and a lack of information on required computing resources hinder the study's comprehensiveness and practical implementation. The absence of insufficient discussion on dataset biases and a lack of information on the model's generalization capacity create obstacles, raising concerns about its fairness, applicability, and real-world utility. © 2001-2012 IEEE.

关键词： Deep learning

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CausalConf: Datasize-Aware Configuration Auto-Tuning for Recurring Big Data Processing Jobs via Adaptive Causal Structure Learning

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IEEE Transactions on Parallel and Distributed Systems 2025年第7期36卷 1354-1371页

作者： Dou, Hui He, Mingjie Zhang, Lei Zhang, Yiwen Zheng, Zibin Anhui University School of Computer Science and Technology Hefei China Sun Yat-sen University School of Software Engineering Guangzhou China

To ensure high-performance processing capabilities across diverse application scenarios, Big Data frameworks such as Spark and Flink usually provide a number of performance-related parameters to configure. Considering the computation scale and the characteristic of repeated executions of typical recurring Big Data processing jobs, how to automatically tune parameters for performance optimization has emerged as a hot research topic in both academic and industry. With the advantages in interpretability and generalization ability, causal inference-based methods recently prove their advancement over conventional search-based and machine learning-based methods. However, the complexity of Big Data frameworks, the time-varying input dataset size of a recurring job and the limitation of a single causal structure learning algorithm together prevent these methods from practical application. Therefore, in this paper, we design and implement CausalConf, a datasize-aware configuration auto-tuning approach for recurring Big Data processing jobs via adaptive causal structure learning. Specifically, the offline training phase is responsible for training multiple datasize-aware causal structure models with different causal structure learning algorithms, while the online tuning phase is responsible for recommending the next promising configuration in an iterative manner via the Multi-Armed Bandit-based optimal intervention set selection as well as the novel datasize-aware causal Bayesian optimization. To evaluate the performance of CausalConf, a series of experiments are conducted on our local Spark cluster with 9 different previously unknown target applications from HiBench. Experimental results show that the performance speed ratio achieved by CausalConf compared to the four recent and representative baselines can respectively reach 1.45×, 1.31×, 1.26× and 1.54× on average and up to 2.53×, 1.55×, 1.57×, 2.18×. Besides, the average total online tuning cost of CausalConf is reduced b

关键词： Spatio-temporal data

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Reflective clothing detection based on YOLOv5s fused with SE attention mechanism

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International Journal of Reasoning-based Intelligent Systems 2025年第1期17卷 9-16页

作者： Wang, Yuanyuan Gu, Dingyun Zhu, Yemeng Zhang, Zhihan Cao, Jiahui Zhang, Haiyan School of Computer and Software Engineering Huaiyin Institute of Technology Huaian223001 China Engineering Laboratory of Mobile Interconnection Technology for Internet of Things Jiangsu Province Huai’an Huaian City223001 China

This paper proposes a YOLOv5s deep learning algorithm incorporating the SE attention mechanism to address the issue of workers failing to wear reflective clothing on duty, which has resulted in casualties from time to time. The YOLOv5s model is used to train the reflective clothing dataset obtained by collecting images of construction sites, and the SE attention mechanism module is added to the network structure of YOLOv5 for improved performance. The reflective clothing detection algorithm is then deployed to the web for real-time detection. The experimental results show that the algorithm achieves mAP value of 96.70% with the SE attention module, resulting in an accuracy improvement of 1.09% compared to the model without the SE module. This enables practical applications to reduce the incidence of construction accidents at construction sites. Coрyright © 2025 Inderscience Enterрrises Ltd.

关键词： attention mechanism flask reflective clothing detection vue YOLOv5

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Road Defect Detection Model Based on YOLOv8

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第4期52卷 986-994页

作者： Cong, Shengqiang Zhang, Chunna Yu, Yang Yue, Xiaoping Zhao, Jinchi Shen, Yuming School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China University of Science and Technology Liaoning Liaoning Anshan114051 China

The detection of road defects is crucial for ensuring vehicular safety and facilitating the prompt repair of roadway imperfections. Existing YOLOv8-based models face the following issues: extraction capabilities and insufficient feature representation in complex scenarios, slow bounding box regression speed. To address these challenges, we propose the YOLOv8-DSW model. Firstly, we incorporate the Dilation-wise Residual (DWR) module into the C2f module in the neck, improving the detection performance of multi-scale feature maps. Secondly, We incorporate the SENetv2 attention mechanism into the neck of the YOLOv8 model to augment feature expression capabilities. Finally, we introduce Wise-IoU (WIoU) to enhance the speed of bounding box regression. Experimental results indicate that YOLOv8-DSW enhances the mean Average Precision at 50% (mAP50) by 2.5% in comparison to the YOLOv8n, significantly improving detection accuracy and provide valuable method for the development of road defect detection. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Deep learning

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Heterologous Image Matching Based on Salience Region

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第4期52卷 894-900页

作者： Zhai, Jinlong Xu, Yang Yan, Xiaohu Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic University Shenzhen518055 China

To address the matching problem caused by the significant differences in spatial features, spectrum and contrast between heterologous images, a heterologous image matching method based on salience region is proposed in this paper. Firstly, texture features and corner features are detected. Secondly, the epanechnikov kernel function is used to calculate the point with the maximum kernel function value among all feature points, which is then defined as E-point. Thirdly, the salience region is defined and extracted according to the position of the E-point. Finally, six groups of heterologous images are used as data sources, and compared with HAPCG, HOWP, SIFT, and PSO_SIFT algorithms respectively. The results show that the comprehensive matching performance of the proposed method is better than the original algorithm without salience region processing. The root mean square error and the matching time are less than the original method while ensuring a similar number of matching points, which greatly improves the accuracy and speed of heterologous image matching. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Image matching

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Auto-3D-house Design from Structured User Requirements

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Machine Intelligence Research 2025年第2期22卷 368-385页

作者： Minkui Tan Qi Chen Zixiong Huang Qi Wu Yuanqing Li Jiaqiu Zhou School of Software Engineering South China University of TechnologyGuangzhou 510006China Faculty of Engineering Computer and Mathematical ScienceUniversity of AdelaideAdelaide SA 5005Australia School of Automation Science and Engineering South China University of TechnologyGuangzhou 510006China

We study the task of automated house design,which aims to automatically generate 3D houses from user ***,in the automatic system,it is non-trivial due to the intrinsic complexity of house designing:1)the understanding of user requirements,where the users can hardly provide high-quality requirements without any professional knowledge;2)the design of house plan,which mainly focuses on how to capture the effective information from user *** address the above issues,we propose an automatic house design framework,called auto-3D-house design(A3HD).Unlike the previous works that consider the user requirements in an unstructured way(e.g.,natural language),we carefully design a structured list that divides the requirements into three parts(i.e.,layout,outline,and style),which focus on the attributes of rooms,the outline of the building,and the style of decoration,*** the processing of architects,we construct a bubble diagram(i.e.,graph)that covers the rooms′attributes and relations under the constraint of *** addition,we take each outline as a combination of points and orders,ensuring that it can represent the outlines with arbitrary ***,we propose a graph feature generation module(GFGM)to capture layout features from the bubble diagrams and an outline feature generation module(OFGM)for outline ***,we render 3D houses according to the given style requirements in a rule-based *** on two benchmark datasets(i.e.,RPLAN and T3HM)demonstrate the effectiveness of our A3HD in terms of both quantitative and qualitative evaluation metrics.

关键词： Automated house design user requirements understanding outline processing layout generation graph feature generation.

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YOLOCSP-PEST for Crops Pest Localization and Classification

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computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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Real-Time Cross-Domain Gesture and User Identification via COTS WiFi

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5124-5137页

作者： Cao, Chenhong Ding, Yue Dai, Miaoling Gong, Wei Zhao, Xibin University of Science and Technology of China School of Computer Science and Technology Langfang101127 China Shanghai University School of Computer Engineering and Science Shanghai200444 China Tsinghua University School of Software Beijing100190 China

WiFi-based gesture recognition has emerged as a promising alternative to computer vision, enabling seamless integration and enhanced interaction in human-computer interaction systems. Simultaneously identifying users during gesture recognition is vital for improving security and personalization. However, existing WiFi-based dual-task recognition approaches often rely on handcrafted features, which hinder precision and introduce delays in cross-domain scenarios. To address these challenges, we propose WiDual, a real-time system for cross-domain gesture recognition and user identification using WiFi signals. By integrating spatial and channel attention mechanisms, WiDual adaptively extracts crucial features for dual-task recognition. The system employs Channel State Information (CSI) visualization to convert WiFi signals into images, facilitating efficient feature extraction and minimizing information loss and latency. Furthermore, a collaborative module fuses gesture and user identity features, enhancing recognition performance. Experimental evaluations on a public dataset with six gestures and six users across diverse environments demonstrate WiDual's effectiveness. It achieves 96% accuracy in cross-domain gesture recognition and 91.27% in user identification. Compared to state-of-the-art methods, WiDual improves user identification accuracy by 26%, gesture recognition by 8%, and reduces processing time sixfold, showcasing its potential for real-time applications. © 2002-2012 IEEE.

关键词： Wireless local area networks (WLAN)

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