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In-situ data scheduling optimization based on rainbow DQN for IIoT

Future Generation computer Systems 2026年 174卷

作者： Peiying Zhang Siyi Li Lizhuang Tan Neeraj Kumar Jian Wang Kai Liu Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao 266580 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala 147004 India Department of Computer Science University of Economics and Human Sciences Warszawa 01-043 Poland Department of Networks and Communications College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia College of Science China University of Petroleum (East China) Qingdao 266580 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing 100084 China State Key Laboratory of Space Network and Communications Tsinghua University Beijing 100084 China

In industrial systems, in-situ computing refers to a mode of local or proximate data processing within the cloud–edge-terminal architecture. In the Industrial Internet of Things (IIoT) environment, servers are typically deployed near terminal devices to coordinate and manage the scheduling of in-situ data, forming a comprehensive in-situ server system. With the rapid growth of IIoT terminal devices, challenges such as high processing delays and severe demand congestion arise in the in-situ data scheduling process. To address this challenge, this paper proposes a scheduling optimization scheme based on Rainbow DQN. The scheme designs a scheduling buffer and classification strategy to distinguish in-situ data based on storage demand characteristics, thereby optimizing the scheduling process. At the same time, a multi-level storage structure is introduced to store in-situ data classified according to the scheduling strategy. Additionally, the Rainbow DQN algorithm is utilized to assist in the scheduling decision-making of in-situ data retrieval, effectively alleviating system congestion while ensuring certain throughput performance. Extensive simulation experiments show that the Rainbow DQN algorithm achieves a reduction of 60.82% in average waiting delay, 62.94% in average unprocessed demand, and a 58.27% reduction in average completion delay.

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Loss Functions in Deep Learning: A Comprehensive Review

arXiv

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arXiv 2025年

作者： Elharrouss, Omar Mahmood, Yasir Bechqito, Yassine Serhani, Mohamed Adel Badidi, Elarbi Riffi, Jamal Tairi, Hamid Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Informatics Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah University Fez Morocco

Loss functions are at the heart of deep learning, shaping how models learn and perform across diverse tasks. They are used to quantify the difference between predicted outputs and ground truth labels, guiding the optimization process to minimize errors. Selecting the right loss function is critical, as it directly impacts model convergence, generalization, and overall performance across various applications, from computer vision to time series forecasting. This paper presents a comprehensive review of loss functions, covering fundamental metrics like Mean Squared Error and Cross-Entropy to advanced functions such as Adversarial and Diffusion losses. We explore their mathematical foundations, impact on model training, and strategic selection for various applications, including computer vision (Discriminative and generative), tabular data prediction, and time series forecasting. For each of these categories, we discuss the most used loss functions in the recent advancements of deep learning techniques. Also, this review explore the historical evolution, computational efficiency, and ongoing challenges in loss function design, underlining the need for more adaptive and robust solutions. Emphasis is placed on complex scenarios involving multi-modal data, class imbalances, and real-world constraints. Finally, we identify key future directions, advocating for loss functions that enhance interpretability, scalability, and generalization, leading to more effective and resilient deep learning models. © 2025, CC BY.

关键词： Deep learning

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Data Anonymous Authentication for BIoMT with Proxy Group Signature

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Journal of Cyber Security 2021年第4期3卷 207-216页

作者： Chaoyang Li Yalan Wang Gang Xu Xiubo Chen Xiangjun Xin Jian Li College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450001China Department of Computer Science University of SurreyGuildfordSurreyGU27XHUK School of Computing Science and Technology North China University of TechnologyBeijing100144China Information Security Centre State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China

Along with the increase of wearable medical device,the privacy leakage problem in the process of transmission between these edge medical *** blockchain-enabled Internet of Medical Things(BIoMT)has been developed to reform traditional centralized medical system in recent *** paper first introduces a data anonymous authentication model to protect user privacy and medical data in ***,a proxy group signature(PGS)scheme has been proposed based on lattice *** scheme can well satisfy the anonymous authentication demand for the proposed model,and provide anti-quantum attack security for BIoMT in the future general quantum computer ***,the security analysis shows this PGS scheme is secure against the dynamical-almost-full anonymous and *** efficiency comparison shows the proposed model and PGS scheme is more efficient and practical.

关键词： Blockchain-enabled Internet of Medical Things anonymous authentication proxy group signature medical data

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6G Internet-of-Things assisted smart homes and buildings: Enabling technologies, opportunities and challenges

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Internet of Things 2025年 32卷

作者： Arif Ullah Fawad Aamir Nadeem Muhammad Arif Muhammad Mehran Bashir Wooyeol Choi School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom Gwangju Institute of Science and Technology Gwangju Republic of Korea Chonnam National University Gwangju Republic of Korea Institute of Networked and Embedded Systems University of Klagenfurt 9020 Klagenfurt Austria Ubiquitous Sensing Systems Lab University of Klagenfurt-Silicon Austria Labs 9020 Klagenfurt Austria Department of Electrical Engineering Muhammad Nawaz Sharif University of Engineering and Technology Multan Pakistan School of Computer Science and Engineering College of Software Chung-Ang University Seoul Republic of Korea

Smart homes (SHs) and smart buildings (SBs) play a crucial role in addressing global urbanization and are key elements of smart cities (SCs). They utilize integrated systems to process large volumes of data, enabling intelligent responses and improved quality of life. However, the growing number of connected devices and increasing data demands have exceeded the capabilities of existing network technologies, reducing service effectiveness. This article provides a comprehensive survey of emerging technologies, especially those underexplored in SH and SB contexts. It highlights the potential of sixth-generation (6G) wireless networks and the Internet of Things (IoT) to transform smart indoor environments through greater automation, intelligence, and adaptability. The authors first analyze the limitations of current IoT-based systems and then examine advanced 6G technologies, including integrated sensing and communication (ISAC), machine learning (ML), visible light communication (VLC), reconfigurable intelligent surfaces (RIS), and blockchain (BC). These technologies aim to resolve issues related to scalability, connectivity, and interoperability that challenge current IoT/5G models. Unlike previous studies focusing on narrow areas such as energy or security, this survey takes a holistic approach. It aligns 6G capabilities with SH/SB needs like human-device interaction, energy efficiency, and robust security, and introduces new applications such as AI-driven digital twins and edge intelligence. The article also identifies research gaps, including resource allocation in diverse networks and ethical AI deployment. It offers guidance for future research and urges collaboration, standardization, and human-centric design to enable next-generation intelligent living environments.

关键词： 6G AI Blockchain Integrated sensing and communication IoT Smart buildings Smart homes VLC

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A novel object detection algorithm based on enhanced R-FCN and SVM

A novel object detection algorithm based on enhanced R-FCN a...

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Telecommunications, Optics and computer science (TOCS), IEEE Conference on

作者： Cong Xu Jiahao Fan Lin Liu College of Software Engineering Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Space Technology (Jilin) Limited Company Jilin China

ISBN: (数字)9781728181172

ISBN: (纸本)9781728181189

Object detection is an extremely important part of computer vision. However, the object detection result of R-FCN is not good enough in terms of speed and accuracy. In this paper, a novel architecture called Enhanced R-FCN (ER-FCN) is proposed for object detection. Two improvements are presented in ER-FCN. Firstly, novel anchor boxes, 3 scales with box areas of 512 2 , 256 2 and 128 2 pixels, and 3 aspect ratios of 0.618:1, 1:1 and 1:0.618, are designed to suit the different scales object detection in RPN. Hence, the performance of object localization and detection speed are increased. Secondly, since the softmax classifier is not optimal to deal with the binary classification problem, a Whale Optimization Algorithm based on support vector machine, termed WOA-SVM, is introduced to improve the accuracy of classification. Extensive experimental results on PASCAL VOC 2007 and PASCAL VOC 2012 datasets show that the mean average precision of ER-FCN is improved by 3.9% compared with that of R-FCN.

关键词： Support vector machines Location awareness Object detection Optics Classification algorithms Telecommunications Optimization

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Machine Learning-based Energy-Aware Offloading in Edge-Cloud Vehicular Networks

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Procedia computer science 2021年 191卷 328-336页

作者： Leila Ismail Huned Materwala Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates

A vehicular network underpinned by the 3-tier vehicle-edge-cloud infrastructure enables an efficient and safer travel experience. The compute-intensive vehicular applications are often offloaded to the edge and/or cloud servers to enhance the applications’ Quality of Services (QoS). The underlying edge-cloud servers consume a high among of energy. Consequently, it becomes crucial to optimizing energy consumption in the offloading process. Current energy-efficient offloading strategies in 2-tier vehicle-edge infrastructure, do not account for cloud computing energy consumption. In this paper, we address this void by proposing a machine learning-based energy-aware offloading algorithm, which optimizes the energy of the edge-cloud computing platform. The offloading strategy is enabled by the Support Vector Machine (SVM) regression model machine learning algorithm used for the edge-cloud power prediction. The experimental results show that the proposed algorithm is a promising approach in energy savings.

关键词： Cloud computing Computation offloading Edge computing Energy-efficiency Machine learning Queuing theory Support Vector Machine (SVM) Vehicular network

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Neuron segmentation using 3D wavelet integrated encoder-decoder network

arXiv

引用

arXiv 2021年

作者： Li, Qiufu Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shen zhen University Shenzhen518060 China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen518060 China

Motivation: 3D neuron segmentation is a key step for the neuron digital reconstruction, which is essential for exploring brain circuits and understanding brain functions. However, the fine line-shaped nerve fibers of neuron could spread in a large region, which brings great computational cost to the neuron segmentation. Meanwhile, the strong noises and disconnected nerve fibers bring great challenges to the task. Results: In this paper, we propose a 3D wavelet and deep learning based 3D neuron segmentation method. The neuronal image is first partitioned into neuronal cubes to simplify the segmentation task. Then, we design 3D WaveUNet, the first 3D wavelet integrated encoder-decoder network, to segment the nerve fibers in the cubes;the wavelets could assist the deep networks in suppressing data noises and connecting the broken fibers. We also produce a Neuronal Cube Dataset (NeuCuDa) using the biggest available annotated neuronal image dataset, BigNeuron, to train 3D WaveUNet. Finally, the nerve fibers segmented in cubes are assembled to generate the complete neuron, which is digitally reconstructed using an available automatic tracing algorithm. The experimental results show that our neuron segmentation method could completely extract the target neuron in noisy neuronal images. The integrated 3D wavelets can efficiently improve the performance of 3D neuron segmentation and reconstruction. © 2021, CC BY.

关键词： Neurons

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Improving Transformers with Probabilistic Attention Keys

arXiv

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arXiv 2021年

作者： Nguyen, Tam Nguyen, Tan M. Le, Dung D. Nguyen, Duy Khuong Tran, Viet-Anh Baraniuk, Richard G. Ho, Nhat Osher, Stanley J. FPT Software AI Center Hanoi Viet Nam Department of Mathematics University of California Los AngelesCA United States College of Engineering and Computer Science VinUniversity Hanoi Viet Nam Deezer Research France Department of Electrical and Computer Engineering Rice University HoustonTX United States Department of Statistics and Data Sciences The University of Texas AustinTX United States

Multi-head attention is a driving force behind state-of-the-art transformers, which achieve remarkable performance across a variety of natural language processing (NLP) and computer vision tasks. It has been observed that for many applications, those attention heads learn redundant embedding, and most of them can be removed without degrading the performance of the model. Inspired by this observation, we propose Transformer with a Mixture of Gaussian Keys (Transformer-MGK), a novel transformer architecture that replaces redundant heads in transformers with a mixture of keys at each head. These mixtures of keys follow a Gaussian mixture model and allow each attention head to focus on different parts of the input sequence efficiently. Compared to its conventional transformer counterpart, Transformer-MGK accelerates training and inference, has fewer parameters, and requires fewer FLOPs to compute while achieving comparable or better accuracy across tasks. Transformer-MGK can also be easily extended to use with linear attention. We empirically demonstrate the advantage of Transformer-MGK in a range of practical applications, including language modeling and tasks that involve very long sequences. On the Wikitext-103 and Long Range Arena benchmark, Transformer-MGKs with 4 heads attain comparable or better performance to the baseline transformers with 8 heads. © 2021, CC BY.

关键词： Benchmarking

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On the security of networked control systems in smart vehicle and its adaptive cruise control

arXiv

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arXiv 2020年

作者： Farivar, Faezeh Haghighi, Mohammad Sayad Jolfaei, Alireza Wen, Sheng Department of Mechatronics and Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Iran Department of Computing Macquarie University SydneyNSW2109 Australia Department of Computer Science and Software Engineering Swinburne University of Technology Australia

With the benefits of Internet of Vehicles (IoV) paradigm, come along unprecedented security challenges. Among many applications of inter-connected systems, vehicular networks and smart cars are examples that are already rolled out. Smart vehicles not only have networks connecting their internal components e.g. via Controller Area Network (CAN) bus, but also are connected to the outside world through road side units and other vehicles. In some cases, the internal and external network packets pass through the same hardware and are merely isolated by software defined rules. Any misconfiguration opens a window for the hackers to intrude into vehicles’ internal components e.g. central lock system, Engine Control Unit (ECU), Anti-lock Braking System (ABS) or Adaptive Cruise Control (ACC) system. Compromise of any of these can lead to disastrous outcomes. In this paper, we study the security of smart vehicles’ adaptive cruise control systems in the presence of covert attacks. We define two covert/stealth attacks in the context of cruise control and propose a novel intrusion detection and compensation method to disclose and respond to such attacks. More precisely, we focus on the covert cyber attacks that compromise the integrity of cruise controller and employ a neural network identifier in the IDS engine to estimate the system output dynamically and compare it against the ACC output. If any anomaly is detected, an embedded substitute controller kicks in and takes over the control. We conducted extensive experiments in MATLAB to evaluate the effectiveness of the proposed scheme in a simulated environment. Copyright © 2020, The Authors. All rights reserved.

关键词： software defined networking

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Enhancing data harvesting systems: Performance quantification of Cloud-Edge-sensor networks using queueing theory

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ICT Express 2025年

作者： Jose Wanderlei Rocha Eder Gomes Vandirleya Barbosa Arthur Sabino Luiz Nelson Lima Gustavo Callou Francisco Airton Silva Eunmi Choi Tuan Anh Nguyen Dugki Min Jae-Woo Lee Laboratory of Applied Research to Distributed Systems (PASID) Federal University of Piaui (UFPI) Picos Piaui R. Cicero Duarte No. 905 - Junco 64607-670 Brazil School of Software College of Computer Science Kookmin University Seoul 02707 South Korea Department of Software Engineering Faculty of Information Technology Ho Chi Minh City University of Industry and Trade (HUIT) Ho Chi Minh City 700000 Viet Nam Department of Artificial Intelligence Graduate School Konkuk University Seoul 05029 South Korea Department of Computer Science and Engineering College of Engineering Konkuk University Seoul 05029 South Korea Department of Aerospace Engineering Konkuk University Seoul 05029 South Korea

This study investigates a Cloud-Edge-sensors infrastructure using M/M/c/K queuing theory to analyze agricultural data systems’ performance. It focuses on optimizing data handling and evaluates the system configuration impacts on performance. The model significantly enhances efficiency and scalability, minimizing the need for extensive physical infrastructure. Analysis shows over 90% utilization in both layers, highlighting the model’s applicability to various IoT applications. The M/M/c/K queuing model addresses scalability and real-time data processing challenges in agricultural cloud–edge-sensor networks, improving over traditional methods lacking dynamic scalability. Designed for optimized resource use and reduced data handling delays, this model proves crucial in precision agriculture, where timely data is essential for decision-making. Its versatility extends to various agricultural applications requiring efficient real-time analysis and resource management.

关键词： Data harvesting Agricultural monitoring system Queuing model Sensors Performance

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