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5th International Conference on Information Technology, Applied Mathematics and Statistics, ICITAMS 2023

作者： Tariq, Suphian Mohammed Al-Mejibli, Intisar Shadeed Al-Iraqia University Computer Engineering Informatics Institute For Postgraduate Iraqi Commission For Computers & Informatics College Of Engineering Baghdad Iraq Biomedical Informatics College University Of Information Technology And Communications Baghdad Iraq

ISBN: (纸本)9798350335118

Specifying the location of wireless sensor devices is vital in many applications such as target tracking, remote environmental monitoring, and more. Time of arrival (TOA), the received signal strength indicator (RSSI) and range information are applied to locate the wireless sensor network (WSN) node's location. Still, they suffer inaccuracy because of environmental factors in the 2D region. A ranging method has been suggested, merging the TOA information domain with RSSI to accurately determine the distance between the unknown node and the anchor node. This approach relies on utilizing RSSI and TOA ranging techniques in the near and far endings. We combine received signal strength indication and arrival time in parallel style as major elements. The findings of the two methods are then combined with the error percentage to determine the final location using adaptive weight errors. The proposed range method is promoted in this work to enhance the accuracy of the positioning. The proposal has been implemented by MATLAB simulation and the results show improving positioning accuracy compared to the conventional TOA and RSSI ranging method. The mean square error in the proposed method is 0.5044, whereas it was 0.7032 and 0.8102 in RSSI, respectively. This indicates that the suggested approach is more efficient and performs better for localization than the other two methods. © 2023 IEEE.

关键词： Sensor nodes

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Optical Intra- and Inter-Rack Switching Architecture for Scalable, Low-Latency Data Center Networks 28

Optical Intra- and Inter-Rack Switching Architecture for Sca...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Drainakis, Georgios Baziana, Peristera Bogris, Adonis School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Thessaly Department of Informatics and Telecommunications Lamia Greece University of West Attica Department of Informatics and Computer Engineering Egaleo Greece

ISBN: (纸本)9798350300482

In this paper we propose a DC network (DCN) architecture that interconnects servers in the intra-rack and inter-rack domain, utilizing optical switching at each domain. The proposed interconnection techniques are studied as an intermediate step before migrating the entire DCN to all-optical schemes. Unlike other studies, we study the server-to-server communication across the whole DCN. For the performance evaluation we produce numerical results for throughput and end-to-end delay for three traffic classes co-existing in DCN s. The numerical analysis reveals that bandwidth utilization reaches 90% and 100% in the intra- and inter- domain respectively. Meanwhile, the maximum end-to-end delay for the highest priority packets under congested load is lower than 0.56 and 0.41 μs for the two examined intra-rack capacity scenarios of 400 and 600 Gbps respectively. A comparative study shows that our solution can effectively interconnect up to 10000 servers with lower environmental footprint and end-to-end delay than other DCN s. © 2023 IEEE.

关键词： Network architecture

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Enhanced Water Quality Control Based on Predictive Optimization for Smart Fish Farming

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computers, Materials & Continua 2023年第6期75卷 5471-5499页

作者： Azimbek Khudoyberdiev Mohammed Abdul Jaleel Israr Ullah DoHyeun Kim Department of Applied Informatics Kimyo International University in TashkentTashkent100121Uzbekistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Computer Engineering Jeju National UniversityJeju63243Korea

The requirement for high-quality seafood is a global challenge in today’s world due to climate change and natural resource *** of Things(IoT)based Modern fish farming systems can significantly optimize seafood production by minimizing resource utilization and improving healthy fish *** objective requires intensive monitoring,prediction,and control by optimizing leading factors that impact fish growth,including temperature,the potential of hydrogen(pH),water level,and feeding *** paper proposes the IoT based predictive optimization approach for efficient control and energy utilization in smart fish *** proposed fish farm control mechanism has a predictive optimization to deal with water quality control and efficient energy consumption *** farm indoor and outdoor values are applied to predict the water quality parameters,whereas a novel objective function is proposed to achieve an optimal fish growth environment based on predicted *** logic control is utilized to calculate control parameters for IoT actuators based on predictive optimal water quality parameters by minimizing energy *** evaluate the efficiency of the proposed system,the overall approach has been deployed to the fish tank as a case study,and a number of experiments have been carried *** results show that the predictive optimization module allowed the water quality parameters to be maintained at the optimal level with nearly 30%of energy efficiency at the maximum actuator control rate compared with other control levels.

关键词： Smart fish farming internet of things(IoT) predictive optimization objective function fuzzy logic control(FLC)

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Scheduling Tool for Deterministic Communication in Distributed Real-Time Systems 10

Scheduling Tool for Deterministic Communication in Distribut...

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10th Workshop on Software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2023

作者： Mach, Jan Tašková, Valéria Kohútka, Lukáš Institute of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia

A network with critical data streams, where the timing of incoming and outgoing data is a necessity, is called a deterministic network. These networks are mostly used in association with real-time systems that use periodic hard-real-time data which need to be scheduled. The algorithm proposed in this paper is focused on achieving the lowest possible latency within the specified network topology depending on the physical parameters of the network and the periodic requirements of the communicating nodes. The algorithm allows to add priorities to the data flow between nodes and also the option to specify if preemption is allowed within the network, since not all devices support it. The result is a schedule of hard-real-time data communication within a specified network topology where all requirements of communicating nodes are met if such a solution is possible. © 2023 Copyright for this paper by its authors.

关键词： deadline deterministic scheduling Ethernet scheduling algorithm time-sensitive networking

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A Graph Neural Network Based Learning Model for Urban Metro Flow Prediction 22

A Graph Neural Network Based Learning Model for Urban Metro ...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Drosouli, Ifigenia Voulodimos, Athanasios Mastorocostas, Paris Miaoulis, Georgios Ghazanfarpour, Djamchid Engineering University of West Attica Department of Informatics & Computer Athens Greece School of Electrical & Computer Engineering National Technical Univ. of Athens Athens Greece University of Limoges Department of Informatics Limoges France

ISBN: (纸本)9798350345346

- Abstract-Transport data with dynamic spatial-temporal dependencies elevates transportation flow forecasting to a significant issue for operational planning, managing passenger flow, and arranging for individual travel in a smart city. The task is challenging due to the composite spatial dependency on transportation networks and the non-linear temporal dynamics with mobility conditions changing over time. To address these challenges, we propose a Spatial- Temporal Graph Convolutional Recurrent Network that learns from both the spatial stations network data and time-series of historical mobility changes so as to predict urban metro flow at a future time. The model is based on Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) in order to further improve the estimation accuracy. Extensive experiments on a real-world dataset of Hangzhou metro system prove the effectiveness of the proposed model. © 2023 IEEE.

关键词： Graph Convolutional Networks spatial-temporal dependencies urban metro flow prediction

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Enhancing intrusion detection in IIoT: optimized CNN model with multi-class SMOTE balancing

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Neural Computing and Applications 2024年第24期36卷 14643-14659页

作者： Eid, Abdulrahman Mahmoud Soudan, Bassel Nassif, Ali Bou Injadat, MohammadNoor Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan

This work introduces an intrusion detection system (IDS) tailored for industrial internet of things (IIoT) environments based on an optimized convolutional neural network (CNN) model. The model is trained on a dataset that was balanced using a novel multi-class implementation of synthetic minority over-sampling technique (SMOTE) that ensures equal representation of all classes. Additionally, systematic optimization will be used to fine tune the hyperparameters of the CNN model and mitigate the effects of the increased size of the training dataset. Evaluation results will demonstrate substantial improvement in performance when the optimized CNN model is trained on the balanced dataset. The proposed IDS will be evaluated using the IIoT-specific WUSTL-IIOT-2021 dataset, and then its generalization capability will be verified using the non-domain specific UNSW_NB15 dataset. The model’s performance will be evaluated using accuracy, precision, recall, and F1-score metrics. The results will demonstrate that the proposed IDS is highly effective with performance exceeding 99.9% on all performance metrics. The IDS is also highly effective in detecting intrusion for generic IT networks achieving improvements in excess of 30% compared to the default baseline model. The results emphasize the versatility and effectiveness of the proposed IDS model, making it a reliable and adaptable solution for enhancing network security across diverse network environments. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Neural network models

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Model-to-Circuit Cross-Approximation for Printed Machine Learning Classifiers

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2023年第11期42卷 3532-3544页

作者： Armeniakos, Giorgos Zervakis, Georgios Soudris, Dimitrios Tahoori, Mehdi B. Henkel, Jorg Karlsruhe Institute of Technology Karlsruhe76131 Germany National Technical University of Athens School of Electrical and Computer Engineering Athens15780 Greece University of Patras Computer Engineering and Informatics Department Patras26504 Greece Karlsruhe Institute of Technology Department of Computer Science Karlsruhe76131 Germany

Printed electronics (PEs) promises on-demand fabrication, low nonrecurring engineering costs, and subcent fabrication costs. It also allows for high customization that would be infeasible in silicon, and bespoke architectures prevail to improve the efficiency of emerging PE machine learning (ML) applications. Nevertheless, large feature sizes in PE prohibit the realization of complex ML models in PE, even with bespoke architectures. In this work, we present an automated, cross-layer approximation framework tailored to bespoke architectures that enable complex ML models, such as multilayer perceptrons (MLPs) and support vector machines (SVMs), in PE. Our framework adopts cooperatively a hardware-driven coefficient approximation of the ML model at algorithmic level, a netlist pruning at logic level, and a voltage overscaling at the circuit level. Extensive experimental evaluation on 12 MLPs and 12 SVMs and more than 6000 approximate and exact designs demonstrates that our model-to-circuit cross-approximation delivers power and area optimal designs that, compared to the state-of-the-art exact designs, feature on average 51% and 66% area and power reduction, respectively, for less than 5% accuracy loss. Finally, we demonstrate that our framework enables 80% of the examined classifiers to be battery-powered with almost identical accuracy with the exact designs, paving thus the way toward smart complex printed applications. © 1982-2012 IEEE.

关键词： Timing circuits

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Human-Robot Interaction on Elderly Companion Robot Development Using Dual Intent Entity Transformer 7

Human-Robot Interaction on Elderly Companion Robot Developme...

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7th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2023

作者： Rauf, Naufal Haidar Santoso, Heru Agus Universitas Dian Nuswantoro Faculty of Computer Science Department of Informatics Engineering Semarang Indonesia

ISBN: (纸本)9798350382266

The growing elderly population and the increasing demand for nursing home care have led to a need to improve the quality of life for residents. A popular solution is developing a companion robot to assist residents with various tasks and provide human-like interaction. In this paper, we present the prototype of a companion robot currently website-based, equipped with a Dual Intent Entity Transformer to understand what the user wants. The results of the study show the prototype is capable of classifying user messages according to their appropriate intent with precision, recall, F-score, and accuracy on average 91 %, 91 %, 90%, and 87% respectively using privately owned dataset. Future works such as expanding the robot's insight and tuning the model would improve the service provided by the companion robot. © 2023 IEEE.

关键词： Human robot interaction

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Navigation System Development on ATRACBOT using F-SFM 25

Navigation System Development on ATRACBOT using F-SFM

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25th International Electronics Symposium, IES 2023

作者： Widarto, Mochamad Farhan Eka Marta, Bayu Sandi Dewantara, Bima Sena Bayu Setiawardhana Politeknik Elektronika Negeri Surabaya Dept. of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350314731

Navigation systems are very important for social robots, especially autonomous robots, because they are required to be adaptive to their surrounding environment. This paper proposes a new navigation system for the Autonomous Trash Can Robot (ATRACBOT) using the Fuzzy-based Social Force Model (F-SFM). The navigation system employed in earlier stages of development depended on the Braitenberg approach, leading to the robot's motion appearing rigid. On the other hand, the Social Force Model (SFM) employs the forces surrounding objects to engage with and evade other entities, presenting a different approach. The magnitude of the force to avoid objects is determined by the SFM parameters and the stimulus received by the robot. The Fuzzy Inference System (FIS) is used to obtain the optimal value of the gain factor, k, which is one of the SFM parameters, based on the relative distance, d, and relative angle, \gamma, between the obstacle and robot. The FIS will process both inputs and produce a suitable gain factor, k, so that the stimulus received can match the robot's reactivity and responsiveness. Based on the results from a realistic 3D simulator V-Rep, the implementation of the F-SFM method can deliver the robot to the goal point without hitting obstacles and make the robot's movements more natural compared to the Braitenberg method. © 2023 IEEE.

关键词： Navigation systems

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A Novel Fuzzy Logic Framework for Model Reliability Evaluation in Permeability Prediction Using GPR 16

A Novel Fuzzy Logic Framework for Model Reliability Evaluati...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Lawal, Ahmad Yang, Yingjie Baisa, Nathanael L. He, Hongmei School of Computer Science and Informatics De Montfort University Leicester United Kingdom School of Computer Science and Informatics University of Nairobi Leicester United Kingdom School of Science Engineering and Environment University of Salford Manchester United Kingdom

ISBN: (纸本)9798331505264

Permeability is a critical parameter in reservoir engineering and hydrocarbon extraction, yet its prediction remains challenging due to inherent uncertainties in subsurface data. While Gaussian Process Regression (GPR) has proven effective in predicting permeability with associated uncertainties, it generates multiple metrics that are difficult to interpret, particularly in high-stakes environments. This study proposes a novel approach using fuzzy logic to compute a single, comprehensive metric that accounts for model reliability. Our method incorporates human input and reasoning into the modelling process, enhancing the model's interpretability and its ability to handle uncertainty. Additionally, we introduce a new visualization technique to simplify the understanding of fuzzy logic outputs for non-technical stakeholders. The proposed methodology demonstrates that GPR achieves a higher reliability level (0.89) compared to traditional machine learning counterparts, which are typically neutral to uncertainties. By providing a comprehensive, transparent, and easily interpretable measure of model reliability, this approach significantly aids in making more informed and responsible decisions in reservoir management. Our framework represents a crucial step towards improving the practical application of advanced machine learning techniques in the oil and gas industry, potentially extending to other fields where uncertainty quantification is vital. © 2024 IEEE.

关键词： Gas industry

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