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    Wyświetlanie 1-4 z 4
    Tytuł:
    IoT sensing networks for gait velocity measurement
    Autorzy:
    Chou, Jyun-Jhe
    Shih, Chi-Sheng
    Wang, Wei-Dean
    Huang, Kuo-Chin
    Powiązania:
    https://bibliotekanauki.pl/articles/330707.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    internet of things
    IoT middleware
    data fusion
    data reduction
    internet rzeczy
    oprogramowanie pośredniczące
    fuzja danych
    redukcja danych
    Opis:
    Gait velocity has been considered the sixth vital sign. It can be used not only to estimate the survival rate of the elderly, but also to predict the tendency of falling. Unfortunately, gait velocity is usually measured on a specially designed walk path, which has to be done at clinics or health institutes. Wearable tracking services using an accelerometer or an inertial measurement unit can measure the velocity for a certain time interval, but not all the time, due to the lack of a sustainable energy source. To tackle the shortcomings of wearable sensors, this work develops a framework to measure gait velocity using distributed tracking services deployed indoors. Two major challenges are tackled in this paper. The first is to minimize the sensing errors caused by thermal noise and overlapping sensing regions. The second is to minimize the data volume to be stored or transmitted. Given numerous errors caused by remote sensing, the framework takes into account the temporal and spatial relationship among tracking services to calibrate the services systematically. Consequently, gait velocity can be measured without wearable sensors and with higher accuracy. The developed method is built on top of WuKong, which is an intelligent IoT middleware, to enable location and temporal-aware data collection. In this work, we present an iterative method to reduce the data volume collected by thermal sensors. The evaluation results show that the file size is up to 25% of that of the JPEG format when the RMSE is limited to 0.5º.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2019, 29, 2; 245-259
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Feature optimization using a two-tier hybrid optimizer in an Internet of Things network
    Autorzy:
    Agrawal, Akhileshwar Prasad
    Singh, Nanhay
    Powiązania:
    https://bibliotekanauki.pl/articles/15548024.pdf
    Data publikacji:
    2023
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    IoT
    Internet of Things
    anomaly mitigation
    GWO
    Gray Wolf Optimizer
    feature optimization
    PSO
    particle swarm optimizer
    Internet Rzeczy
    optymalizacja funkcji
    Opis:
    The growing use of the Internet of Things (IoT) in smart applications necessitates improved security monitoring of IoT components. The security of such components is monitored using intrusion detection systems which run machine learning (ML) algorithms to classify access attempts as anomalous or normal. However, in this case, one of the issues is the large length of the data feature vector that any ML or deep learning technique implemented on resource-constrained intelligent nodes must handle. In this paper, the problem of selecting an optimal-feature set is investigated to reduce the curse of data dimensionality. A two-layered approach is proposed: the first tier makes use of a random forest while the second tier uses a hybrid of gray wolf optimizer (GWO) and the particle swarm optimizer (PSO) with the k-nearest neighbor as the wrapper method. Further, differential weight distribution is made to the local-best and global-best positions in the velocity equation of PSO. A new metric, i.e., the reduced feature to accuracy ratio (RFAR), is introduced for comparing various works. Three data sets, namely, NSLKDD, DS2OS and BoTIoT, are used to evaluate and validate the proposed work. Experiments demonstrate improvements in accuracy up to 99.44%, 99.44% and 99.98% with the length of the optimal-feature vector equal to 9, 4 and 8 for the NSLKDD, DS2OS and BoTIoT data sets, respectively. Furthermore, classification improves for many of the individual classes of attacks: denial-of-service (DoS) (99.75%) and normal (99.52%) for NSLKDD, malicious control (100%) and DoS (68.69%) for DS2OS, and theft (95.65%) for BoTIoT.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2023, 33, 2; 313--326
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Edge computing in IoT-enabled honeybee monitoring for the detection of Varroa destructor
    Autorzy:
    Wachowicz, Anna
    Pytlik, Jakub
    Małysiak-Mrozek, Bożena
    Tokarz, Krzysztof
    Mrozek, Dariusz
    Powiązania:
    https://bibliotekanauki.pl/articles/2172108.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    Internet of Things
    IoT
    Varroa destructor
    precision beekeeping
    machine learning
    image processing
    edge device
    Internet Rzeczy
    pszczelarstwo
    uczenie maszynowe
    przetwarzanie obrazu
    urządzenie brzegowe
    Opis:
    Among many important functions, bees play a key role in food production. Unfortunately, worldwide bee populations have been decreasing since 2007. One reason for the decrease of adult worker bees is varroosis, a parasitic disease caused by the Varroa destructor (V. destructor) mite. Varroosis can be quickly eliminated from beehives once detected. However, this requires them to be monitored continuously during periods of bee activity to ensure that V. destructor mites are detected before they spread and infest the entire beehive. To this end, the use of Internet of things (IoT) devices can significantly increase detection speed. Comprehensive solutions are required that can cover entire apiaries and prevent the disease from spreading between hives and apiaries. In this paper, we present a solution for global monitoring of apiaries and the detection of V. destructor mites in beehives. Our solution captures and processes video streams from camera-based IoT devices, analyzes those streams using edge computing, and constructs a global collection of cases within the cloud. We have designed an IoT device that monitors bees and detects V. destructor infestation via video stream analysis on a GPU-accelerated Nvidia Jetson Nano. Experimental results show that the detection process can be run in real time while maintaining similar efficacy to alternative approaches.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2022, 32, 3; 355--369
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The IoT gateway with active queue management
    Autorzy:
    Domański, Adam
    Domańska, Joanna
    Czachórski, Tadeusz
    Klamka, Jerzy
    Szyguła, Jakub
    Marek, Dariusz
    Powiązania:
    https://bibliotekanauki.pl/articles/1838178.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    active queue management
    PI controller
    dropping packets
    fractional calculus
    IoT gateway
    aktywne zarządzanie kolejkami
    regulator PI
    pochodna ułamkowa
    Internet Rzeczy
    Opis:
    As the traffic volume from various Internet of things (IoT) networks increases significantly, the need for adapting the quality of service (QoS) mechanisms to the new Internet conditions becomes essential. We propose a QoS mechanism for the IoT gateway based on packet classification and active queue management (AQM). End devices label packets with a special packet field (type of service (ToS) for IPv4 or traffic class (TC) for IPv6) and thus classify them as priority for real-time IoT traffic and non-priority for standard IP traffic. Our AQM mechanism drops only non-priority packets and thus ensures that real-time traffic packets for critical IoT systems are not removed if the priority traffic does not exceed the maximum queue capacity. This AQM mechanism is based on the PIα controller with non-integer integration order. We use fluid flow approximation and discrete event simulation to determine the influence of the AQM policy on the packet loss probability, queue length and its variability. The impact of the long-range dependent (LRD) traffic is also considered. The obtained results show the properties of the proposed mechanism and the merits of the PIα controller.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 1; 165-178
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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