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2023 International Conference on Advances in Artificial Intelligence and Applications, AAIA 2023

作者： Su, Weibin Machica, Ivy Kim D. Dumdumaya, Cristina E. Gamboa, Randy S. Xu, Gang He, Zhengfang College of Information and Computing University of Southeastern Philippines Davao City8000 Philippines Edge Computing Network Engineering Research Center Yunnan Technology and Business University Kunming651701 China

ISBN: (纸本)9798400708268

A recent development in the field is the emergence of a novel Internet of Things (IoT) framework known as the Artificial Intelligence of Things (AIoT). Based on this framework, it can enable smart autonomous vehicles, unmanned aerial vehicles, and intelligent industrial production lines to effectively reduce costs and expand the scope of sensor perception by utilizing collaborative sensor networks and shared computing resources provided by edge computing. The sensor network formed by traditional internet has successfully achieved global unified wide area connectivity. However, for a certain control object, sensors that are far away from it do not need to participate in the control process, while local existing sensors fail due to the movement of the control object in a specific direction. So this requires a new type of network that can dynamically update the domain. To enhance the real-time responsiveness of regional communication involving controllers and controlled devices, it is essential for the network to establish dynamic local connections that adapt to the spatial location changes of cars, unmanned aerial vehicles, or production line logistics. As a response to this requirement, this paper introduces a novel edge computing distributed communication network and a dynamically updated domain identity architecture. Every endpoint node has the capability to autonomously form a local cluster based on its spatial location for signal transmission. By dynamically polling and accessing this cluster, it achieves close sharing of sensors and AI computing power, leading to a cost reduction in building a comprehensive intelligent control system compared to traditional single-machine AI systems. This holds significant potential for advancing the implementation of comprehensive AI projects. © 2023 Copyright held by the owner/author(s).

关键词： edge computing

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Design of ceiling light with air quality monitoring and heat dissipation structure based on duct technology 3

Design of ceiling light with air quality monitoring and heat...

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3rd International Conference on Electronic Information engineering and Data Processing, EIEDP 2024

作者： Yu, Liang Mo, Jiayu Lu, Jiancong Lai, Yangbing Tian, Liwei College of Computer Science Guangdong University of Science and Technology AIoT Edge Computing Engineering Technology Research Center Dongguan523000 China

ISBN: (纸本)9781510680531

Most air quality monitors and lighting fixtures are currently separate, with air quality monitors typically being stand-alone devices that require separate power supplies and mounting locations. This not only occupies valuable space but also complicates their use and management. The emergence of air quality monitoring ceiling lights in smart homes aims to address this issue. However, many existing ceiling light designs do not consider heat dissipation, which can lead to performance issues, reduced lifespan, and even fire hazards. To tackle these challenges, this study presents the design of an intelligent ceiling lamp for air quality monitoring utilizing air duct technology. Equipped with a seven-in-one air quality sensor, the ceiling lamp collects indoor air data and performs real-time intelligent control and anomaly identification through a cloud-side-end synergy approach. The incorporation of air ducts enhances the sensitivity and accuracy of air quality monitoring, facilitates thermal convection, and improves heat dissipation efficiency, ultimately enhancing performance and longevity. Physical product design and field tests conducted on the cloud-side-end platform demonstrate that the ceiling lamp's air quality monitoring and anomaly identification have an average inference time of less than 1 millisecond with an identification accuracy of over 98%. Furthermore, the air duct design improves thermal efficiency by approximately 28%. Overall, this intelligent ceiling lamp represents a practical, efficient, and intelligent multi-functional indoor lighting solution. © 2024 SPIE.

关键词： Efficiency

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Improved Particle Swarm optimization Task Scheduling Algorithm Based on Multiple Swarm Molecular Dynamics Theory 24

Improved Particle Swarm optimization Task Scheduling Algorit...

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5th International Conference on Computer Information and Big Data Applications, CIBDA 2024

作者： Liu, Zilai Yu, Liang Li, Qiqian College of Computer Science Guangdong University of Science and Technology Guangdong AIoT Application Innovation Joint Laboratory AIoT Edge Computing Engineering Technology Research Center Dongguan523000 China

ISBN: (纸本)9798400718106

In light of the objective to optimize task scheduling in Cyber-physical systems (CPS) by establishing an efficient mapping between system resources and user tasks, a novel particle swarm optimization task scheduling algorithm is proposed. This algorithm incorporates the principles of multiple swarm molecular dynamics theory, thereby enhancing its performance. By integrating molecular dynamics theory and KMTOA into the particle swarm optimization algorithm, the proposed approach aims to achieve improved task time span and load balancing capabilities. To achieve this, the algorithm initially divides the particle population into distinct sub-populations. Subsequently, different gravitational rules are applied to guide the evolution of particles in each sub-population. This division and rule implementation greatly enhance the diversity of the population in the early stages and enhance the local search ability in the later stages. Simulation and comparative experiments have been conducted to evaluate the effectiveness of the improved task scheduling algorithm. The results demonstrate that the algorithm exhibits superior task time span and load balancing capabilities, effectively enhancing the efficiency of resource mapping. © 2024 ACM.

关键词： Cyber Physical System

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Smart Firefighting Early Warning and Evacuation System Based on edge computing with Led Dynamic Indicator Devices 6

Smart Firefighting Early Warning and Evacuation System Based...

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6th International Conference on Communications, Information System and Computer engineering, CISCE 2024

作者： Yu, Liang Lai, Yangbing Mo, Jiayu Zeng, Yuze Tan, Guoshen Xu, Haonan College of Computer Guangdong University of Science and Technology Dongguan China AIoT Edge Computing Engineering Technology Research Center of Dongguan City Guangdong AIoT Application Innovation Joint Laboratory Dongguan China

ISBN: (纸本)9798350353174

The current standard practice for building fire safety relies heavily on regular lighting, emergency lighting, and smoke sensors. The management and control centers for these systems are typically separate and independent, resulting in a bulky and expensive local control system that is difficult to use and manage. Additionally, the traditional fixed instructions in fire protection systems do not allow for adaptive planning of optimal escape routes based on the location of the fire, leading to delays in evacuation and ultimately causing significant casualties. This paper presents the design of a smart fire warning and evacuation system based on edge computing utilizing LED dynamic indication devices. The system integrates LED dot-matrix technology to create a smart indicator and emergency light with dynamic adaptive indication capabilities. Additionally, it includes temperature, humidity, and smoke sensors to monitor the building's environmental conditions. Through Mesh wireless networking, time-series sensing data is aggregated to the edge gateway, where a miniature machine learning model trained by the edge Impluse (EI) platform performs edge computing for predicting and reasoning about fire situations. The data is then uploaded to a cloud server and forwarded to mobile users via Cat.1 communication protocol for real-time monitoring. The system also incorporates an improved ant colony algorithm for automatic execution in the cloud and allows manual commands from mobile terminals to control adaptive and dynamic indication of indicator lights and emergency lights, guiding building personnel to the safest evacuation path. System testing demonstrates a high accuracy rate of 95% for fire prediction and inference, with an average inference time of less than 1 ms. This system provides a novel approach to adaptive building fire prediction and evacuation, enhancing safety measures for orderly and secure evacuations. © 2024 IEEE.

关键词： Ant colony optimization

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Dynamic Domain ID Based on DDS for AIoT 23

Dynamic Domain ID Based on DDS for AIoT

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Proceedings of the 2023 International Conference on Advances in Artificial Intelligence and Applications

作者： Weibin Su Ivy Kim D Machica Cristina E Dumdumaya Randy S Gamboa Gang Xu Zhengfang He College of Information and Computing University of Southeastern Philioppines Philioppine and Edge Computing Network Engineering Research Center Yunnan Technology and Business University China College of Information and Computing University of Southeastern Philioppines Philioppine

ISBN: (纸本)9798400708268

关键词： AIoT DDS Dynamic Domain ID edge computing

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A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving

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Neural computing and Applications 2025年第3期37卷 1651-1672页

作者： Xu, Junjie Chen, Yuzhong Xiao, Lingsheng Liao, Hongmiao Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Math word problem (MWP) represents a critical research area within reading comprehension, where accurate comprehension of math problem text is crucial for generating math expressions. However, current approaches still grapple with unresolved challenges in grasping the sensitivity of math problem text and delineating distinct roles across various clause types, and enhancing numerical representation. To address these challenges, this paper proposes a Numerical Magnitude Aware Multi-Channel Hierarchical Encoding network (NMA-MHEA) for math expression generation. Firstly, NMA-MHEA implements a multi-channel hierarchical context encoding module to learn context representations at three different channels: intra-clause channel, inter-clause channel, and context-question interaction channel. NMA-MHEA constructs hierarchical constituent-dependency graphs for different levels of sentences and employs a Hierarchical Graph Attention Neural network (HGAT) to learn syntactic and semantic information within these graphs at the intra-clause and inter-clause channels. NMA-MHEA then refines context clauses using question information at the context-question interaction channel. Secondly, NMA-MHEA designs a number encoding module to enhance the relative magnitude information among numerical values and type information of numerical values. Experimental results on two public benchmark datasets demonstrate that NMA-MHEA outperforms other state-of-the-art models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantics

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Online Queue-Aware Service Migration and Resource Allocation in Mobile edge computing

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8063-8078页

作者： Du, An Jia, Jie Chen, Jian Wang, Xingwei Huang, Min Northeastern University School of Computer Science and Engineering The Engineering Research Center of Security Technology of Complex Network System Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

Mobile edge computing (MEC) integrated with network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual network Functions (VNFs) deployed at MEC networks. This emerging paradigm offers flexible edge resource management for Internet Service Providers (ISPs) and improves service satisfaction of diverse applications. However, a critical challenge lies in processing extremely unpredictable and bursty traffic flow, especially in event-trigger sensing tasks. In this paper, we study queue-aware service migration and resource allocation in an MEC network, where different devices with different arrival patterns request different network services. To this end, we first formulate a novel long-term operation cost minimization problem subject to the stability of multiple queues by adopting a simultaneous migration scheme and determining resource allocation dynamically. To address the problem efficiently, we propose an online control framework to make decisions without any prior system *** first adopt the Lyapunov optimization technique to decompose the long-term optimization problem into multiper-slot subproblems. Then, we adopt a decoupled optimization algorithm based on the coordinate descent method and augmented Lagrangian method to solve the offline mixed integer non-linear programming (MINLP) subproblems. Extensive results reveal that the proposed online optimization framework can efficiently balance queue stability and cost consumption. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Industrial Cyber Intelligent Control Operating System that Hybrid with IEC 61499 and Big Data on edge computing 5

Industrial Cyber Intelligent Control Operating System that H...

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5th IEEE International Conference on Artificial Intelligence and Big Data, ICAIBD 2022

作者： Su, Weibin MacHica, Ivy Kim Xu, Gang He, Zhengfang Kong, Yanchun University of Southeastern Philippines College of Information and Computing Davao Philippines Yunnan Technology and Business University Edge Computing Network Center Kunming China Faculty of Construction Engineering Kunming Metallurgy College Kunming China

ISBN: (纸本)9781665499132

IEC 61499 and Big Data systems, which are also distributed architecture, but they belong to two independent and unrelated platforms due to different network and control concepts. With the development of intelligent manufacturing factories, the big data generated by the process of industrial production is facing unprecedented challenges of storage and data processing. The current approach is to put industrial big data on cloud computing and the industrial automation control program on the side that is close to physical equipment. This architecture has the problems of low efficiency, insufficient security and poor real-time performance. The purpose of this paper is to design an architecture for intergration IEC61499 and Big Data. It can not only reduce the cost of building a smart factory, but also increase the security and data access velocity. More importantly, it can meet the real-time performance of industrial control. © 2022 IEEE.

关键词： edge computing

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IGED:Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

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Computers, Materials & Continua 2024年第8期80卷 1851-1866页

作者： Yanhua Liu Yuting Han HuiChen Baokang Zhao XiaofengWang Ximeng Liu College of Computer and Data Science Fuzhou UniversityFuzhou350108China Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou350108China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350108China College of Computer National University of Defense TechnologyChangsha410073China

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly *** propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data *** the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization *** results show that the proposed method can efficiently distinguish normal traffic from DDoS *** with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

关键词： DDoS real-time improved generalized entropy DNN

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

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10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

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