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Fault tolerance in fog-based Social Internet of Things

Knowledge-Based Systems 2023年第1期265卷

作者： Mohammadi, Venus Rahmani, Amir Masoud Darwesh, Aso Sahafi, Amir Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Information Technology University of Human Development Sulaimanyah Iraq Department of Computer Engineering South Tehran Branch Islamic Azad University Tehran Iran

The Social Internet of Things (SIoT) is a glimpse into the future for self-establishment of inter-things social relationships that guarantee object-to-object transactions. This scenario adopted an intelligent strategy by the trust characteristics and typology of leveraging objects to eradicate the interference of defective nodes. In addition, SIoT networks are error-prone due to heterogeneity and potential resource constraints on the devices, which necessitates the inclusion of a mechanism to enhance the lifetime and dependability of devices. Therefore, fault tolerance mechanisms are indispensable to ensure optimal performance through fault detection and recovery. Although fog architecture and SIoT are two stand-alone paradigms, we extend fog competence into SIoT to empower resource-poor objects for handling intensive tasks. Hence a fault-tolerant model based on a clustering algorithm for fault detection and retrieval is presented. In the proposed scheme, the Markov chains model all faults diagnosed by the fog node. In the recovery stage, the status of the nodes is determined based on the type of faults and the assigned threshold to make a substitution by hot standby nodes. The four performance criteria, namely the reliability, availability, probability density function, the average time to failure, and their mathematical equations were developed on the Markov model. Furthermore, the achievements were demonstrated through either theoretical analysis or simulation results. Then, the proposed scheme increases the number of live nodes, improves the detection accuracy in forwarded requests, lowers the number of invalid requests, and enhances availability and reliability compared to networks with analogous properties. © 2023 Elsevier B.V.

关键词： Internet of things

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Green Leaf Disease Detection Using Raspberry Pi and Random Forest Machine Learning Algorithm: An Automated Approach for Precision Agriculture

Green Leaf Disease Detection Using Raspberry Pi and Random F...

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International Conference on Smart Computing & Communications (ICSCC)

作者： Chitra Sabapathy Ranganathan Amjath Ali Jakul Basha A Vijayalakshmi N. Mohankumar S. Sujatha C. Sasikala Mphasis Corporation Chennai Tamil Nadu India Department of Engineering Computer Engineering Electrical and Electronic Section University of Technology and Applied Sciences Ibra Sultanate of Oman Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Nagpur Maharashtra India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sri Sai Ranganathan Engineering College Coimbatore Tamil Nadu India

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

Farm production and food security are affected by green leaf diseases. Modern disease detection technologies may discover plant health issues early. This work uses Raspberry Pi, a cheap and flexible single-board computer, along the advanced Random Forest machine learning technique to identify a green leaf disease. To identify crop diseases, the study uses automated, non-invasive technologies. From labeled images of good and sickly leaves, the algorithm extracts color histograms and texture, and form. It feeds a Random Forest classifier these properties for classifying data with high dimensions. Several green leaf diseased plant species were examined. This method detected plant health concerns with high precision. Movement and low cost make the Raspberry Pi ideal for remote and resource-limited agriculture, increasing farming accuracy. Helping farmers and users identify and develop green leaf diseases improves efficient farming. This methodology has the potential to improve crop management, optimize pesticide usage, and increase agricultural productivity. These results might contribute to the development of advanced automatic disease detection techniques to address green leaf diseases in modern agriculture.

关键词： Productivity Precision agriculture Machine learning algorithms Accuracy Plants (biology) Crops Real-time systems Random forests Diseases Farming

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Advancing Blackboard Framework: Responsive Trigger Mechanisms and Dynamic Feasibility Conditions for Optimal Knowledge Source Selection

Advancing Blackboard Framework: Responsive Trigger Mechanism...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Hana Munira Muhd Mukhtar Husna Sarirah Husin Suriana Ismail Azizah Rahmat Roslan Ismail Software Engineering Section Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia School of Computer Science Taylor's University Kuala Lumpur Malaysia Centre of Innovative Digital Education & Emerging Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

Expert systems (ES) rely heavily on effective control mechanisms to navigate complex problem-solving tasks. These mechanisms govern how ES utilizes knowledge and data to make decisions and execute actions. Optimizing these mechanisms, or addressing the “control problem,” remains a central challenge. Blackboard architectures, with their separation of knowledge and control processes, offer a promising approach. This study introduces an improved control mechanism for blackboard architectures. It focuses on a critical aspect-trigger mechanisms and feasible conditions-for optimal knowledge source selection to participate in the problem-solving process. By leveraging these features, this study aims to enhance decision-making and problem-solving capabilities within complex domains. This paper focuses on the technical details of exploring how it manages the flow of rule activation and execution throughout the problem-solving process.

关键词： Navigation Decision making Process control Information management Problem-solving Expert systems

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Design and Stability Analysis of Control System in Multiport Autonomous Reconfigurable Solar Power Plants (MARS)

Design and Stability Analysis of Control System in Multiport...

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IEEE Workshop on computers in Power Electronics

作者： Qianxue Xia Suman Debnath Phani R V Marthi Maryam Saeedifard Energy Systems Integration & Control Section Oak Ridge National Lab Knoxville United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States

The Multiport Autonomous Reconfigurable Solar Power Plant (MARS) is an integrated photovoltaic (PV) power generation and energy storage system (ESS), that is designed to connect to both alternating current (AC) transmission grids and high-voltage direct current (HVDC) links. It is a three-phase plant consisting of numerous components with a complex hardware and hierarchical control architecture. This paper presents an approach to decouple the multivariable system of MARS using a recursive reduced-order and boundary layer system methodology. This approach enables efficient computation of the control parameters for the Ll, L2, and L3 controllers. To validate the effectiveness of the proposed control strategy, cyclic tests in accordance with pre-defined performance criteria using controller Hardware-in-the-Loop (cHIL) experiments are conducted. The results demonstrate that the MARS system operates consistently under steady-state conditions. Furthermore, the dynamic response of the MARS system to various grid events is analyzed, underlining the resilience of MARS in presence of faults or loss of generation within the connected WECC system.

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Human Movement Science-Informed Multi-Task Spatio Temporal Graph Convolutional Networks for Fitness Action Recognition and Evaluation

SSRN

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SSRN 2023年

作者： Chang, Jia-Wei Chen, Ming-Hung Ma, Hao-Shang Liu, Hao-Lan Department of Computer Science and Information Engineering National Taichung University of Science and Technology No. 129 Section 3 Sanmin Road North District Taichung City404 Taiwan Physical Education Section National Taichung University of Science and Technology No. 129 Section 3 Sanmin Road North District Taichung City404 Taiwan

In recent years, with the rise of health consciousness, people's demand for fitness has been steadily increasing. Improper exercise movements can easily lead to injuries while working out. Therefore, if we could utilize automated human action recognition to continuously monitor users' movements during exercise, it would effectively help prevent incorrect movements. Human action recognition has found extensive applications in multimedia computing, and we aim to leverage it to assess users' fitness conditions. Some existing action motion recognition models rely heavily on CNNs for image processing, which introduces unnecessary noise from the background and surroundings. To address this issue, some researches focus on the skeleton structure information for the action recognition and apply the graph convolutional network to enhance the performance. Generally, the proposed action human recognition method only recognize the type of actions. In this study, we are trying to design an automated fitness adviser system to improve the exercise experience in fitness gyms. The adviser system not only detect the correctness of fitness action but also evaluate the action quality simultaneously. To address this issue, we involve the human movement science such as Five Primary Kinetic Chains(5PKC) to provide the more accurate relations of human skeleton and muscles. Five Primary Kinetic Chains(5PKC) defines the primary physiological principle in human movement. The proposed model adopts Five Primary Kinetic Chains(5PKC) as a partitioning strategy to explore the skeleton partition and designs a multi-task objective function based on the Spatio Temporal Graph Convolutional Networks(ST-GCN) framework. For the experiments, a custom fitness dataset is collected from the gym and used to evaluate the performance of proposed model. In addition, we also compare with existing methods to confirm the effect of the proposed model. © 2023, The Authors. All rights reserved.

关键词： Kinetics

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The artificial intelligence technologies in Industry 4.0: A taxonomy, approaches, and future directions

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computers and Industrial engineering 2023年 185卷

作者： Alenizi, Farhan A. Abbasi, Shirin Hussein Mohammed, Adil Masoud Rahmani, Amir Electrical Engineering Department College of Engineering Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia Computer Engineering Department Science and Research Branch Islamic Azad University Tehran Iran Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

Industry 4.0 transforms the manufacturing sector with dynamic, networked, complex industrial environments. These environments generate vast amounts of data and require technology and Artificial Intelligence (AI) to achieve intelligent, efficient, and sustainable production processes. This paper comprehensively reviews 45 articles on AI and Industry 4.0 integration. We propose a taxonomy for AI in Industry 4.0 and classify approaches into Industry 4.0 design and product quality control methods. Our analysis shows that 58% of papers use product quality control methods. Besides, this paper identifies challenges and open issues by illuminating the current landscape. The findings showed that machine learning is the most common AI method for improving Industry 4.0 with 41%, and Python is the most used tool for simulation. © 2023 Elsevier Ltd

关键词： Internet of things

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An Optimized Technique For Developing Optical Band Pass Filter

An Optimized Technique For Developing Optical Band Pass Filt...

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IEEE Calcutta Conference (CALCON)

作者： Arup Kumar Ghosh Gautam Garai Subhankar Bhattacharjee A.K.C. School of Information Technology University of Calcutta Kolkata India Computer Section Saha Institute of Nuclear Physics Kolkata India Electronics and Communication Engineering Techno International New Town Kolkata India

ISBN: (数字)9798331516666

ISBN: (纸本)9798331516673

In this paper we have introduced an innovative method for designing a one dimensional photonic crystal (1D PhC) based Optical Band Pass Filter (OBPF) using the Differential Evolution (DE) algorithm. These filters are essential in optical communication systems because of their ability to control light propagation. The DE algorithm efficiently optimizes the PhC structure to achieve the desired optical band pass characteristics and simplifying the design process. The OBPFs are made of multiple dielectric layers with varying thicknesses and refractive indices to enable high transmission at specific wavelengths and low transmission elsewhere. Optimization helps to find the optimal thickness and refractive index. MATLAB implementation shows encouraging results with high transmittance (99.2214%) and a narrow full width at half maximum (FWHM) of $0.6 ~\mathrm{n m}$. The experimental results demonstrate the superiority of this approach over the existing well-known traditional methods.

关键词： Optical filters Band-pass filters Optical design Photonic band gap Optical propagation Refractive index Optical variables control Optical fiber communication Optical refraction Optimization

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Interleukin-6 electrochemical sensor using poly(o-phenylenediamine)-based molecularly imprinted polymer

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Sensors and Actuators B: Chemical 2024年 404卷

作者： Ting, Wei-Ting Wang, Meng-Jiy Howlader, Matiar M.R. Department of Chemical Engineering National Taiwan University of Science and Technology No. 43 Keelung Road Section 4 Taipei106 Taiwan Department of Electrical and Computer Engineering McMaster University 1280 Main Street West HamiltonONL8S 4K1 Canada Taiwan Building Technology Center National Taiwan University of Science and Technology No. 43 Keelung Road Section 4 Taipei106 Taiwan

Interleukin-6 (IL-6) is an important cytokine involved in immune responses and maintaining body homeostasis. Elevated IL-6 levels, exceeding ∼ 40 pg/mL in bodily fluids, are associated with inflammation and diseases such as COVID-19, cardiovascular disorders, and Alzheimer's, necessitating real-time health monitoring for personalized healthcare. In this study, we developed a highly sensitive and selective IL-6 sensing platform by depositing a poly(o-phenylenediamine) (P(o-PD))-based molecularly imprinted polymer (MIP) onto an oxygen-functionalized screen-printed carbon electrode with gold nanoparticles, 3-aminopropyltriethoxysilane (APTES), and glutaraldehyde (GA). While APTES enhanced the peak current due to redox probe adsorption, GA reduced it due to non-conductive aldehyde groups. This functionalized surface improved hydrophilicity due to the presence of amino and carbonyl groups. We identified that a 120-minute NaCl treatment effectively removed IL-6 templates, ensuring the successful embedding of IL-6 in the P(o-PD) matrix. We calculated an imprinting factor of 11.2, indicating the effective imprinting of IL-6 by P(o-PD) and the presence of IL-6 specific binding sites within the MIP, resulting in a robust current response to IL-6. After optimizing the MIP deposition through five cycles, we detected IL-6 concentrations ranging from 2 to 400 pg/mL, with a sensitivity of 3.48 μA/log(pg/mL) and a limit of detection of 1.74 pg/mL. When tested in real human serum, the sensor encountered challenges due to P(o-PD) matrix adsorbing other active molecules. Despite this challenge, our platform demonstrates high selectivity and long-term stability in IL-6 detection. These qualities position our sensor well-suited for point-of-care diagnostics, emphasizing its potential as a reliable tool for real-world applications. © 2024 The Authors

关键词： Gold nanoparticles

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Cryptanalysis and improvement of quantum secure multi-party summation using single photons

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Physica Scripta 2024年第3期99卷 035114-035114页

作者： Tsai, Chia-Wei Lin, Jason Yang, Chun-Wei Department of Computer Science and Information Engineering National Taichung University of Science and Technology No.129 section 3 Sanmin Rd North District Taichung404 Taiwan Department of Computer Science and Engineering National Chung Hsing University No. 145 Xingda Rd. South District Taichung40227 Taiwan Program for Digital Health Innovation College of Humanities and Sciences China Medical University No. 100 section 1 Jingmao Rd. Beitun Dist. Taichung406040 Taiwan

A previous study [Entropy 25.4 (2023): 590] proposed a quantum secure multi-party summation protocol wherein n participants could obtain the modulo-2 summation result using single photons and single-particle operations. Owing to the absence of an entanglement state, this protocol was more practical than other quantum summation protocols that used entanglement properties. Thus, this study investigated the claim that this proposed protocol prevented the participants’ secret inputs from being disclosed. It was found that the previously proposed protocol was vulnerable to a quantum Trojan horse attack owing to the circular qubit transmission mode. Using a security loophole, this study proposed an attack strategy that allowed an outside attacker to steal any participant’s secret input without being detected by a third party and participants. In addition, a simulation experiment was conducted to demonstrate the feasibility of the proposed attack and evaluate the number of quantum Trojan horse probes that the attacker must inject into regular qubit sequences. Finally, a solution was proposed by modifying the protocol to securely prevent quantum Trojan horse attacks. © 2024 IOP Publishing Ltd.

关键词： Photons

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Fault Location Identification in Meshed AC Microgrids Using Interharmonic Injection 7

Fault Location Identification in Meshed AC Microgrids Using ...

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7th Iran Wind Energy Conference, IWEC 2021

作者： Forouzesh, Alireza Golsorkhi, Mohammad S. Savaghebi, Mehdi Baharizadeh, Mehdi Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan Iran University of Southern Denmark Electrical Engineering Section Department of Mechanical and Electrical Engineering Odense Denmark Islamic Azad University Department of Electrical Engineering Isfahan Iran

ISBN: (纸本)9781665423779

This paper presents a support vector machine (SVM)-based fault detection algorithm for islanded meshed microgrids (MGs). Considering the low level of fault current in islanded MGs, the fault detection is carried out based on the magnitude of the voltage rather than the current. Accordingly, the rms voltages of the DERs are assigned as the feature vector for fault detection SVM. Upon the occurrence of a fault, the fault location is identified by means of an interharmonic injection scheme. To identify the faulty zone, the DER which is electrically closest to the fault injects interharmonic currents into the grid and the zone with highest current is considered as the faulty zone. Then, a second DER located in the same zone inject interharmonic currents at different frequencies. The resulting interharmonic voltages are measured by each DER to form a feature vector. A multi class SVM uses this feature vector to identify the faulty line within that zone. The proposed algorithm is tested on a nine bus meshed MG. Test results show that the proposed method is capable of accurate detection of fault location even with high fault impedances. © 2021 IEEE.

关键词： Fault detection

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