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Suche nach „[H.] [Bangui]“ hat 3 Publikationen gefunden
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    DigitalF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    H. Bangui, Mouzhi Ge, B. Buhnova

    Improving Big Data Clustering for Jamming Detection in Smart Mobility

    ICT Systems Security and Privacy Protection, Cham, Switzerland, vol. 580

    2020

    ISBN: 978-3-030-58200-5

    Abstract anzeigen

    Smart mobility, with its urban transportation services ranging from real-time traffic control to cooperative vehicle infrastructure systems, is becoming increasingly critical in smart cities. These smart mobility services thus need to be very well protected against a variety of security threats, such as intrusion, jamming, and Sybil attacks. One of the frequently cited attacks in smart mobility is the jamming attack. In order to detect the jamming attacks, different anti-jamming applications have been developed to reduce the impact of malicious jamming attacks. One important step in anti-jamming detection is to cluster the vehicular data. However, it is usually very time-consuming to detect the jamming attacks that may affect the safety of roads and vehicle communication in real-time. Therefore, this paper proposes an efficient big data clustering model, coresets-based clustering, to support the real-time detection of jamming attacks. We validate the model efficiency and applicability in the context of a typical smart mobility system: Vehicular Ad-hoc Network, known as VANET.

    DigitalF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    L. Trang, H. Bangui, Mouzhi Ge, B. Buhnova

    Scaling Big Data Applications in Smart City with Coresets

    Proceedings of the 8th International Conference on Data Science, Technology and Applications, vol. Vol. 1: DATA

    2019

    DOI: 10.5220/0007958803570363

    Abstract anzeigen

    With the development of Big Data applications in Smart Cities, various Big Data applications are proposed within the domain. These are however hard to test and prototype, since such prototyping requires big computing resources. In order to save the effort in building Big Data prototypes for Smart Cities, this paper proposes an enhanced sampling technique to obtain a coreset from Big Data while keeping the features of the Big Data, such as clustering structure and distribution density. In the proposed sampling method, for a given dataset and an ε>0, the method computes an ε-coreset of the dataset. The ε-coreset is then modified to obtain a sample set while ensuring the separation and balance in the set. Furthermore, by considering the representativeness of each sample point, our method can helps to remove noises and outliers. We believe that the coreset-based technique can be used to efficiently prototype and evaluate Big Data applications in the Smart City.

    DigitalF: Europan Campus Rottal-Inn

    Zeitschriftenartikel

    H. Bangui, Mouzhi Ge, B. Buhnova

    A Research Roadmap of Big Data Clustering Algorithms for Future Internet of Things

    International Journal of Organizational and Collective Intelligence, vol. 9, no. 2, pp. 16-30

    2019

    DOI: 10.4018/IJOCI.2019040102

    Abstract anzeigen

    Due to the massive data increase in different Internet of Things (IoT) domains such as healthcare IoT and Smart City IoT, Big Data technologies have been emerged as critical analytics tools for analyzing the IoT data. Among the Big Data technologies, data clustering is one of the essential approaches to process the IoT data. However, how to select a suitable clustering algorithm for IoT data is still unclear. Furthermore, since Big Data technology are still in its initial stage for different IoT domains, it is thus valuable to propose and structure the research challenges between Big Data and IoT. Therefore, this article starts by reviewing and comparing the data clustering algorithms that can be applied in IoT datasets, and then extends the discussions to a broader IoT context such as IoT dynamics and IoT mobile networks. Finally, this article identifies a set of research challenges that harvest a research roadmap for the Big Data research in IoT domains. The proposed research roadmap aims at bridging the research gaps between Big Data and various IoT contexts.