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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Tanzir Ahmed Jargis Ahmed Department of Computer Science and Engineering Green Computing and Communications Green University of Bangladesh Bangladesh

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Mobile Edge Computing (MEC) server plays a crucial role in executing the tasks offloaded by the edge users. As MEC is deployed at the edge of the network, it helps to reduce computation latency and increase reliability for users. Due to enormous tasks offloading requests MEC may require extra resources to mitigate resource demand. In this case, MEC can deploy multiple unmanned aerial vehicles (UAVs) as computing nodes to assist with resource requirements. Now distributing the offloaded tasks and reducing the latency for task completion is a challenging problem. In this regard, we developed a delay minimization problem considering the delay and latency constraints in this paper. The formulated problem is a mixed integer problem and NP-Hard in nature. Hence we proposed an ant colony optimization (ACO) based algorithm to handle task placement in different server nodes. Extensive performance analysis indicates significant improvement in reducing delay, energy consumption of users, and success rate compared to the state-of-the-art work.

关键词： Ant colony optimization Multi-access edge computing Wireless networks Metaheuristics Autonomous aerial vehicles Minimization Delays Performance analysis Servers Reliability

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Increasing Automotive Electrical Equipment Effectiveness with Enhanced Iron Loss Estimation Techniques 6

Increasing Automotive Electrical Equipment Effectiveness wit...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Gupta, Neetu Singh, Arjun Saxena, Nitesh Ravivarman, G. Gajendran, P. Deshpande, Pallavi K. R. Mangalam University Department of Computer Science and Engineering Gurugram India Dev Bhoomi Uttarakhand University Electronics and Communications Engineering Uttarakhand India Invertis University Department of Computer Applications Bareilly India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Vishwakarma Institute of Information Technology Electronics and Telecommunications Engineering Department Pune India

ISBN: (纸本)9798350304480

For improved transformer design in car uses, precise modeling of ferrous failures of flexible superconductors for a variety of frequency bands and eddy currents concentrations is essential. [1] For this reason, various empirical iron-loss models were put forth that describe the processes that cause loss. The majority of these have issues with magnetization flux concentrations and bad precision for longer wavelengths. The usual iron loss formulas and a sophisticated iron loss algorithm are compared in this article. By including a high order component for the flux density of magnetic matter, the suggested Formula addresses a problem with the widely used metal models. To determine the iron losses of an electrical machine for the driveline of a fully electric car, for example, one uses the iron-loss algorithm. © 2023 IEEE.

关键词： Efficiency Improvement Empirical Iron-Loss Models Ferrous Failures Flexible Superconductors Iron Loss Prediction Models Iron-Loss Calculation Magnetization Flux Concentrations Power Systems and Machine Loss Analysis Transformer Design

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Comparative Analysis of Machine Learning Algorithms in Thyroid Disease Prediction 23th

Comparative Analysis of Machine Learning Algorithms in Thyro...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Preethiya, T. Tyagi, Amit Kumar Pandiarajan, T. Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Kanchipuram 603203 India Department of Fashion Technology National Institute of Fashion Technology New Delhi India Department of Computer Science Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9783031648120

Thyroid disease is estimated to be the most common illness in the twenty-first century. The thyroid hormones are produced by a butterfly-shaped gland in the throat. The crucial function these hormones play in bodily metabolism, mental health, and general well-being. In this study, we present a method for accurately analysing thyroid function in an unbalanced dataset using Machine Learning (ML) algorithms. This dataset's accuracy score is ambiguous because most target variable classes are 1. Performance measurements are accuracy and recall, but when class distribution is asymmetrical, the F1-score balances the other parameters. The performance is evaluated using the F1-score, which is one of the best output metrics for unbalanced classification tasks. Simulation results shows that the Decision Tree (DT) algorithm outperforms the other algorithms with an F1-score of 0.9957, the Area Under Curve (AUC) score of 0.9917 and the kappa coefficient of 0.978 in the disease prediction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Support vector machines

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Intelligent Aeroponic System for Real-time Control and Monitoring of Lactuca Sativa Production 17

Intelligent Aeroponic System for Real-time Control and Monit...

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17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Calzita, Cris Ramil Jubilo, Kehndee Ann Permejo, Glenn Reas, Roxcella Baun, Jonah Jahara Concepcion, Ronnie De Leon, Joseph Aristotle Bandala, Argel Mayol, Andres Philip Vicerra, Ryan Rhay Dadios, Elmer Eastern Visayas State University Department of Electronics and Communications Engineering Tacloban City Philippines De la Salle University Department of Electronics and Computer Engineering Manila Philippines De la Salle University Department of Manufacturing Engineering and Management Manila Philippines

ISBN: (纸本)9781665453486

Urban farming is becoming more popular in recent years as the community began to focus more on the product's quality that is now being consumed. Aeroponics is one of the new urban farming techniques that is more effective than traditional farming since it involves growing plants without soil using nutrient solutions sprayed into the roots. However, proper monitoring of the cultivation environment and control of environmental factors is crucial for efficient aeroponic farming. This study focuses on developing an IoT-based-intelligent monitoring and controlling mechanism of an aeroponic system for the effective production of lettuce (Lactuca sativa). Raspberry Pi is employed for the system's real-time monitoring capabilities of growth parameters in the data collection system based on temperature, relative humidity with respect to the root system, and light intensity. The system is capable of automatically adjusting the amount of light each sample will receive over time and automatically activates the thermoelectric cooling system, exhaust, and mister anytime the ambient temperature is too high for plant development. The monitoring system effectively logged the expected growth parameters per minute upon testing and was able to store the logged data in a Comma-Separated Value (CSV) file format. The recorded values retrieved by the system from the sensors for temperature, humidity, and light intensity were within the range of the settling, threshold, or daily amount. The real-time data can be accessed successfully in the developed web application via smartphones or personal computers. This system offers a positive financial impact on society and its consumers. © 2023 IEEE.

关键词： Lettuce

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Quantum Annealing-Based Sum Rate Maximization for Multi-UAV-Aided Wireless Networks

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IEEE Internet of Things Journal 2025年第12期12卷 21225-21239页

作者： Jeong, Seon-Geun Duc, Pham Dang Anh Do, Quang Vinh Noh, Dae-Il Tung, Nguyen Xuan Chien, Trinh Van Pham, Quoc-Viet Hasegawa, Mikio Sekiya, Hiroo Hwang, Won-Joo Department of Information Convergence Engineering Pusan National University Pusan National University Busan46241 Korea Republic of Wireless Communications Research Group Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Graduate School of Engineering Chiba University Chiba263-8522 Japan Hanoi100000 Viet Nam School of Computer Science and Statistics Trinity College Dublin DublinD02PN40 Ireland Department of Electrical Engineering Tokyo University of Science Tokyo162-8601 Japan School of Computer Science and Engineering Center for Artificial Intelligence Research Pusan National University Busan46241 Korea Republic of

In wireless communication networks, it is difficult to solve many NP-hard problems owing to computational complexity and high cost. Recently, quantum annealing (QA) based on quantum physics was introduced as a key enabler for solving optimization problems quickly. However, only some studies consider quantum-based approaches in wireless communications. Therefore, we investigate the performance of a QA solution to an optimization problem in wireless networks. Specifically, we aim to maximize the sum rate by jointly optimizing clustering, sub-channel assignment, and power allocation in a multi-unmanned aerial vehicle-aided wireless network. We formulate the sum rate maximization problem as a combinatorial optimization problem. Then, we divide it into two sub-problems: 1) a QA-based clustering and 2) sub-channel assignment and power allocation for a given clustering configuration. Subsequently, we obtain an optimized solution for the joint optimization problem by solving these two sub-problems. For the first sub-problem, we convert the problem into a simplified quadratic unconstrained binary optimization (QUBO) model. As for the second sub-problem, we introduce a novel QA algorithm with optimal scaling parameters to address it. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of the sum rate and running time. © 2025 IEEE.

关键词： Combinatorial optimization

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Initial Seeds Generation Using LLM for IoT Device Fuzzing

Initial Seeds Generation Using LLM for IoT Device Fuzzing

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International Conference on Internet of Things: Systems, Management and Security

作者： Hibiki Nakanishi Kota Hisafuru Kento Hasegawa Seira Hidano Kazuhide Fukushima Kazuo Hashimoto Nozomu Togawa Department of Computer Science and Communications Engineering Waseda University KDDI Research Inc.

ISBN: (数字)9798350366501

ISBN: (纸本)9798350366518

In recent years, security measures for IoT devices have become more important. Fuzzing of IoT devices is an effective way to find unknown vulnerabilities. In IoT device fuzzing, a large number of test cases are generated based on a set of initial seeds and sent to a target device to monitor its behavior, in which a device crash means that a vulnerability has been discovered. However, generating a set of initial seeds is difficult because technical knowledge in security and adaptation to various IoT devices are quite required. In this paper, we propose a method to generate initial seeds for IoT device fuzzing effectively utilizing a large language model (LLM). The proposed method generates initial seeds for efficient fuzzing of the target IoT device by inputting only the specifications of the IoT device and the name of the vulnerability to be inspected into an LLM, with no specific technical knowledge in security. Experimental results show that the proposed method detected crashes in 5.4 minutes and 12.43 minutes from the start of fuzzing, that could not be detected by the initial seeds generated manually.

关键词： Large language models Fuzzing computer crashes Internet of Things Security Monitoring

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An Ensemble Deep Learning Model for Diabetic Retinopathy Identification 9

An Ensemble Deep Learning Model for Diabetic Retinopathy Ide...

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Hussain, Mohammad Manzoor Shanmugam, P. Moorthi, K. Sakthivelu, U. Rajasekar, A. Kumar, R. Naresh B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Medak India Rajalakshmi Engineering College Department Computer Science and Engineering Chennai India Sairam Engineering College Department Electronics and Communication Engineering Chennai India Srm Institute of Science and Technology School of Computing Department of Networking and Communications Tamilnadu Kattankulathur India Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350384208

Diabetic Retinopathy (DR) is an eye disease that can potentially cause significant injury in those diagnosed with diabetes. The condition arises due to issues with blood circulation to the retina, the delicate tissue in the posterior part of the eye. DR is recognized as the leading factor contributing to visual impairment among those affected by diabetes. However, timely identification and intervention might mitigate the occurrence of this condition. Conversely, the timely identification of DR poses challenges, while the diagnostic process can be laborious and time-intensive. The automated detection of DR necessitates categorizing colour retinal fundus pictures. This article presents a computer-aided diagnostic strategy that utilizes Deep Learning (DL) algorithms. In order to classify retinal fundus pictures, this study employed a combination of DL models, specifically ResNet50 and Inception V3. The studies are being conducted on the DIARETDB1 dataset. An analysis is conducted to compare the results of the suggested methodology. The comparison study results show that the proposed approach has more significant accuracy levels than the other methods. A high degree of accuracy, that is, 98.37%, has been attained by utilising the proposed techniques. © 2023 IEEE.

关键词： Deep learning

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Resource Sharing in Federated Edge Network Using Fractional Knapsack Based Genetic Algorithm

Resource Sharing in Federated Edge Network Using Fractional ...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md. Emon Sheikh Jargis Ahmed Department of Computer Science and Engineering Green Computing and Communications Green University of Bangladesh Bangladesh

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Mobile edge computing (MEC) servers made the execution of offloaded tasks faster and more reliable for edge users compared to cloud servers. Due to the limited resource availability at MEC, sometimes edge servers could be over-whelmed by the incoming tasks offloaded by the users. Hence MEC needs external support to fulfill the resource demand. In this regard sharing unused resources of nearby MEC servers could ease the situation and better utilization of edge resources. When sharing resources all the responsible MEC servers would try to maximize their benefits at the same time hence the decision problem will become NP-Hard. Therefore in the paper, we proposed a method using a fractional knapsack-based genetic algorithm (FKGA) where sharing resources among MEC servers is formulated as a fractional knapsack problem and solved using a meta-heuristic approach. The results from a comprehensive performance analysis demonstrate the effectiveness of the proposed method compared to existing literature works.

关键词： Multi-access edge computing NP-hard problem Metaheuristics Performance analysis Servers Resource management Reliability Fifth Industrial Revolution Genetic algorithms

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Tree-Search Techniques for Joint Iterative Compressive Sensing and LDPC Decoding in Wireless Sensor Networks

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IEEE Sensors Journal 2023年第1期23卷 434-451页

作者： Chen, Jue Wang, Tsang-Yi Wu, Jwo-Yuh Li, Chih-Peng Ng, Soon Xin Maunder, Robert G. Hanzo, Lajos University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom Institute of Communications Engineering National Sun Yat-sen University Kaohsiung804 Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University 300 SO17 1BJ Taiwan National Sun Yat-sen University Institute of Communications Engineering Department of Electrical Engineering Kaohsiung804 Taiwan

Novel techniques are conceived for joint compressive sensing (CS) and low-density parity check (LDPC) coding in wireless sensor networks (WSNs), namely, a soft-input soft-output (SISO) tree search sphere decoding (SD) technique and an SISO Hamming distance (HD)-based solution. Factor graphs are utilized to describe the connectivity between the signals and sensors, as well as with the LDPC codes. In the fusion center (FC), the factor graphs may be used for iterative joint LDPC-CS decoding in order to recover the signals observed. However, the CS decoder of the FC suffers from high complexity if the exhaustive Maximum A Posteriori (e-MAP) technique is employed, which considers all possible combinations of source signals detected by each of the associated sensors. Hence, in the proposed SD and HD schemes, only the more likely combinations of source signals are tested for reducing the CS decoding complexity. More specifically, a tree search technique is used in the first step to find the most likely combination of source signal values. Then, in the second step, the proposed SD continues the tree search to find a set of alternative hypotheses. This facilitates the generation of high-quality extrinsic information, which may be iteratively exchanged with the LDPC decoder. By contrast, in the HD approach, the second step obtains the alternative hypotheses within a certain HD of the most likely source signal combination. Both our BLock Error Rate (BLER) results and EXtrinsic Information Transfer (EXIT) charts show that the proposed SD and HD techniques approach the performance of the full-search e-MAP approach at a significantly reduced complexity. In particular, we show that the e-MAP solution is about 56 times more complex than the SD approach and around 210 times more complex than the HD approach. Compared to a separate source-channel coding (SSCC) hard information benchmarker, the proposed SISO schemes improve the decoding performance by about {1}.{7} text {dB}. Furtherm

关键词： Compressed sensing

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On the Challenge of Generating Multivariate Time Series Data from Distributed Sensors in IoT-enabled Scenarios 24

On the Challenge of Generating Multivariate Time Series Data...

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14th International Conference on the Internet of Things, IoT 2024

作者： Bañuelos, Julián Jerónimo Costa-Requena, Jose He, Jiayuan Salim, Flora D. Sigg, Stephan Department of Information and Communications Engineering School of Electrical Engineering Aalto University Espoo Uusimaa Finland School of Computing Technologies RMIT University MelbourneVIC Australia Cumucore Oy Espoo Uusima Finland School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (纸本)9798400712852

Smart sensing environments supported by an Internet of Things (IoT) infrastructure are enabling a wide array of possibilities for data-intensive remote monitoring applications. Although nowadays it is possible to access and deploy complex setups of sophisticated sensors, the telecommunications infrastructures are struggling to deal with the massive amounts of generated data for transmission, processing, and storage from the diverse range of IoT devices. As data corruption and the loss of data are realistic scenarios in current IoT deployments, it is relevant to find an alternative solution that circumvents the limitations of the physical infrastructure for dealing with potential missing data in remote sensing environments. In this paper, we analyze the feasibility of reproducing missing sensor data due to a communication failure from correlated sources in the same experimental context. We rely on generative adversarial models for the prediction of acceleration data and evaluate the usefulness of the synthetic data with a standard human activity recognition (HAR) classifier. © 2024 Copyright held by the owner/author(s).

关键词： Time series

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