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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

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Energy-efficient OECT Sensor data Analysis on Constrained Edge Devices 11

Energy-efficient OECT Sensor Data Analysis on Constrained Ed...

引用

11th IEEE international conference on Cloud Engineering (IC2E)

作者： Saccani, Francesco Bettelli, Manuele Gentile, Francesco Amoretti, Michele Univ Parma Dept Engn & Architecture Parma Italy Inst Mat Elect & Magnetism IMEM CNR Parma Italy Magna Graecia Univ Catanzaro Dept Expt & Clin Med Catanzaro Italy

ISBN: (纸本)9798350343946

A new frontier in Smart Agriculture is merging nanobiotechnology with edge computing, for on-field raw data collection and processing. Smart plant sensors communicate plant chemical signals to on-field agricultural and phenotyping equipment. Particularly promising are the Organic Electrochemical Transistors (OECTs), i.e., devices that can measure the ionic content of liquid samples and biological systems. In this work, we present and evaluate several algorithms for solving a mathematical model that describes the behavior of OECT devices, in order to translate raw values like electrical currents, to meaningful information about the monitored plant stem, e.g., the concentration of ions and water saturation. Our Rust-based algorithm implementations are energy-efficient and suitable for real-time execution on constrained edge devices, as we demonstrate providing several experimental results that concern the quality of model solution, memory footprint, execution time, and the energy cost. The experiments were carried out using two different Arm Cortex-M processors, an ultra low power one and a high performance one.

关键词： Edge computing Energy Efficiency OECT Devices Neural Networks

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Whose data are Reliable: Sensor Declared data Reliability 19

Whose Data are Reliable: Sensor Declared Data Reliability

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19th international conference on Wireless and Mobile computing, Networking and Communications (WiMob)

作者： Shafin, Sakib Shahriar Karmakar, Gour Mareels, Iven Balasubramanian, Venki Kolluri, Ramachandra Rao Federat Univ Australia Ctr Smart Analyt CSA Ballarat Vic 3350 Australia Federat Univ Australia Inst Innovat Sci & Sustainabil Ballarat Vic 3350 Australia IBM Australia Pty Ltd IBM Consulting St Leonards Australia

ISBN: (纸本)9798350336672

Sensor data is susceptible to faults, noise, and malicious attacks, posing a significant operational and security threat. Therefore, ensuring reliability of sensor data is critical for real-time monitoring systems. Prior research on sensor data reliability relies on edge or upper-layer devices for data fusion from multiple sensors, employing architectures with major overheads and latency due to transmission and storage demands. An alternative approach is to have the sensor estimate and declare its own reliability. While some methods involve sensors computing data confidence and including it in payloads, limitations arise in the absence of neighboring sensor data, and communication overheads are incurred. To address this problem, this paper proposes an innovative approach to enhance the reliability of sensor data using an intelligent self-declaration process. Proposed reliability estimation is evaluate with three lightweight estimation algorithms, namely, Kalman Filter, Holt-Winters Method, and Mahalanobis Distance using sensor's historical data. The reliability level is then added to the three reserved bits of a TCP packet header which results in zero additional overhead. Experiments conducted using real-world sensor data (from water quality monitoring systems) obtained from our IoT lab demonstrate the effectiveness of our proposal and the potential for application in real-world sensor-based applications.

关键词： Reliability

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A Survey on Mechanisms for Fast Network Packet processing 23

A Survey on Mechanisms for Fast Network Packet Processing

引用

4th international conference on computing, Networks and Internet of Things, CNIOT 2023

作者： Du, Yimin Chang, Keyang Shi, Jinglin Zhou, Yiqing Liu, Min Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400700705

As the Internet of Things becoming widely used, the requirement for high performance network is growing rapidly. Network performance is affected by data transmission based on physical link and packet processing handled by operating system. With the development of network hardware technology and wireless communication technology, network data transmission rate has been further improved, but the packet processing performance based on operating system cannot match it. There are several performance bottlenecks in the traditional packet processing, correspondingly there are various optimization mechanisms to solve these problems and further improve the performance. This paper analyzes some mainstream optimization mechanisms of packet processing based on software. Firstly, we analyze the architecture design of these mechanisms and the workflow when receiving packets. Then we compare the technical methods adopted by each mechanism and problems solved by each mechanism. Finally, we compare the application scenarios and the shortcomings of each mechanism. © 2023 ACM.

关键词： data transfer

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Research on A Coal Mine Safety Knowledge Model Based on Spatial Knowledge Map Service 8

Research on A Coal Mine Safety Knowledge Model Based on Spat...

引用

8th international conference on Intelligent computing and Signal processing, ICSP 2023

作者： Zhang, Xinju Jiang, Haiyan Zhai, Lulu Big Data Center of Ministry of Emergency Management Dongcheng District Hepingli North Street No.Jia 4 Beijing China

ISBN: (纸本)9798350302455

Research on conceptual models for coal mine safety management rely on data mining. Text mining of big data is used to extract the laws and rules that are implied in safety models, making these laws and rules more transparent by stating them explicitly and providing visualization. Currently, the ability to process information on coal mine safety management is insufficient for resolving the problems of inefficiency and a lack of knowledge. In this paper, based on a knowledge map service, the analysis of coal mine safety maps for management purposes and the acquisition of situational factors in a multidimensional space for innovation is divided into the current situation and the expectations hidden in the data. Multidimensional situations and the role of each component's situation require specific analysis and discussion of the issues of human-computer interactions and situations. Five levels of users' spatial knowledge demand are proposed and analyzed by combining the coal mine safety demand, multidimensional situational factors and spatial knowledge map service system. The service process model of user space knowledge acquisition and the organizational structure of the service system are constructed. Then, the coal mine safety text is classified, and a self-adaptive weight deep learning classification algorithm is used to classify the coal mine safety knowledge data to verify the difference in the classification effect of two classification algorithms. Finally, based on the correct classification of coal mine safety knowledge data, the mining of hazard association rules is performed. From the perspectives of knowledge workers, knowledge bases, and the combination of knowledge workers and knowledge bases, the mode of knowledge analysis of coal mine safety maps and its specific applications in intelligent searches, knowledge sharing and knowledge optimization are discussed. © 2023 IEEE.

关键词： Coal mines

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Predictive Queue-based Low Latency Congestion Detection in data Center Networks 21

Predictive Queue-based Low Latency Congestion Detection in D...

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21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Dong, Pingping Lu, Xiaojuan Huang, Tairan Chen, Liying Yang, Yang Zhang, Lianming Hunan Normal University College of Information Science and Engineering Changsha China

ISBN: (纸本)9798350329223

End-to-end congestion control in lossless data center networks (DCN) depends on congestion detection. However, many current congestion control mechanisms ignore the mutually reinforcing relationship with congestion detection and flow identification, resulting in wasted link resources. For accurate congestion detection, the currently popular ternary congestion detection (TCD) technique sets three separate port states. However, the flow needs to go through multiple hops before deciding the flow category, which leads to serious delays. In order to achieve accurate congestion detection and low delay flow identification, we propose a predictive queue-based low latency congestion detection, called hop-by-hop flow identification (HFI) based on congestion detection. HFI predicts the dynamic queueing threshold based on the data transmission rate to provide timely and proactive congestion detection, then combined it with the congestion information notification from the next hop to identify the congested flow hop by hop. Experimental results show that DCQCN+HFI obtains shorter flow completion time, and improves link utilization by 55%, 7.5% compared to DCQCN, DCQCN+TCD. © 2023 IEEE.

关键词： Queueing theory

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Leveraging Neural Networks to Enhance Large-Scale data processing Performance 3

Leveraging Neural Networks to Enhance Large-Scale Data Proce...

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3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Pandey, Megha Vinay Kumar, S.B. Khurana, Jatin Kalpana, B. Pathak, Kishor R. Kumar, Mukesh Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India 1 Electronics and Communication Engineering Faculty of Engineering and Technology Karnataka Bangalore India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India

ISBN: (纸本)9798350319125

Recent advances in neural networks have enabled them to become powerful tools in data processing for tasks such as pattern recognition and classification. This has enabled neural networks to be applied to large-scale data processing problems, where they have been shown to perform very well. However, due to the large amount of data involved in such tasks, it is essential that the processing performance is also improved. This paper presents several methods to leverage available resources such as distributed computing and improved algorithmic techniques to improve the performance of large-scale data processing tasks using neural networks. Examples of such techniques are given for both supervised and unsupervised learning tasks, which can significantly enhance the performance of large-scale data processing applications. The results of several experiments are presented, demonstrating the effectiveness of the proposed techniques. The authors also provide insights for future work that could further enhance the capabilities of neural networks when applied to large-scale data processing applications. © 2023 IEEE.

关键词： Applications data Networks presented processing

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Detection and Classification of DDoS Attacks in Cloud data Using Hybrid LSTM and RNN for Feature Selection

Detection and Classification of DDoS Attacks in Cloud Data U...

引用

2023 international conference on Circuit Power and computing Technologies, ICCPCT 2023

作者： Karthika, R.A. Sriramya, P. Rohini, A. SRMIST Dept. of Computational Intelligence Chennai India Saveetha School of Engineering SIMATS Dept. of Artificial Intelligence and Data Science Chennai India Presidency University Dept. of CSE Bengaluru India

ISBN: (纸本)9798350333244

Cyber threats are the main source of concern in the sector it is deployed. DDoS attacks are at the very top of the list. The rapid growth of cloud migration also expands the attack surface. Different forms of DDoS attacks exist, such as DoS and DDoS etc., There are several existing machine learning algorithms to detect DDoS attacks. There are also many hybrids of machine learning algorithms which are introduced for detecting DDoS attacks to increase the accurateness of detection. Here we use hybrid Long Short-Term Memory Network (LSTM) and Recurrent Neural Network (RNN) for feature selection and deliver an improved detection system. The proposed exploratory model of Multi-layer Perceptron (MLP) classifiers uses deep neural network algorithms. The final outcome shows higher accuracy and precision by dealing with a huge dataset that was gathered. The accuracy and precision of 98.85% and 92 % respectively for grouping in structure. © 2023 IEEE.

关键词： Denial-of-service attack

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Multi-Server Based Task Offloading Strategy in Mobile Edge computing

Multi-Server Based Task Offloading Strategy in Mobile Edge C...

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2023 international conference on algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Wei, Zeju Gao, Jingnan Ruan, Peiguang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China

ISBN: (纸本)9781510671881

Edge computing, as a complement to cloud computing, has many unique advantages. In mobile edge computing (MEC) scenarios, tasks on terminal devices can be offloaded to edge servers for computing purposes, reducing the shortage of computing resources on terminal devices. Moreover, the resulting delay is much shorter than uploading the task to the cloud center directly. However, many scenarios in reality show that there are dependencies between tasks, and a task cannot be calculated until all its predecessor tasks have been calculated. In this paper, we summarize a variety of task dependencies. At the same time, we build an efficient task offloading model based on two levels of edge servers with different functions. In particular, the second-level edge server has the routing decision function, which provides us with diversified offloading methods. Finally, we used improved NSGA-Ⅱ for task offloading. Compared with other offloading methods, our algorithm performs better in latency and energy consumption. © 2023 SPIE. All rights reserved.

关键词： Energy utilization

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Application Research of Computer Virtual Reality Technology in Big data Information Face Recognition System 5

Application Research of Computer Virtual Reality Technology ...

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2023 5th international conference on Artificial Intelligence and Computer Applications, ICAICA 2023

作者： Yu, Xin Li, Yuan Modern Finance Industry School Shandong Institute of Commerce and Technology Shandong Jinan China

ISBN: (纸本)9798350323313

This paper studies the edge-cloud collaborative face recognition technology. This project intends to integrate cloud computing technology with boundary computing technology, breaking through the limitations of traditional algorithms on network environment and expanding the scope of application of algorithms. This improves the efficiency of the user. The image feature extraction algorithm based on Resent is studied, and the ResNet34 network is perfected. By introducing the face loss function of ArcFace to guide the training of the network, the network can better understand the corner features of the face. In the boundary aspect, this project intends to study the lightweight Resent structural model based on depth-separable volume to solve the problem of limited computation and memory. The SResNet model based on the lightweight Resent structure is studied. The effectiveness and real-time performance of this method are verified by the facial recognition experiments on edge cloud collaboration. © 2023 IEEE.

关键词： Face recognition

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A Federated Learning Network Intrusion Detection System for Multiple Imbalances

A Federated Learning Network Intrusion Detection System for ...

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2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zeng, Yifan Zheng Li, Jian School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Jinan China

ISBN: (纸本)9798350368741

Centralized training of deep learning-based network intrusion detection systems (DLNIDS) raises privacy concerns and incurs huge overhead. Federated learning (FL), while preserving privacy, confronts challenges including size imbalance, heterogeneous and global imbalance. To address these issues, this paper proposes a Self-Balancing Federated learning Network Intrusion Detection System (SBFedNIDS) for accurate and efficient network intrusion detection while preserving privacy and tackling negative impact caused by multiple imbalances resulting from FL. SBFedNIDS can efficiently mitigate model bias cause by multiple imbalances and addresses imbalances at data-level via federated cost sensitive learning (FCSL) and client synthetic minority over-sampling technique (CSMOTE), without requiring additional training resources, unlike generative models. Extensive experiments on a benchmark dataset show that SBFedNIDS outperforms baselines in multiple unbalanced scenarios and even surpasses centralized training in several settings. © 2025 IEEE.

关键词： data Imbalance Deep Learning Federated Learning Network Intrusion Detection

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