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    Persation: an IoT based personal safety prediction model aided solution

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    Authors
    Alofe, Olasunkanmi Matthew
    Fatema, Kaniz
    Azad, Muhammad Ajmal
    Kurugollu, Fatih
    Affiliation
    University of Derby
    Aston University, Birmingham
    Issue Date
    2020
    
    Metadata
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    Abstract
    The number of attacks on innocent victims in moving vehicles, and abduction of individuals in their vehicles has risen alarmingly in the past few years. One common scenario evident from the modus operandi of this kind of attack is the random motion of these vehicles, due to the driver’s unpredictable behaviours. To save the victims in such kinds of assault, it is essential to offer help promptly. An effective strategy to save victims is to predict the future location of the vehicles so that the rescue mission can be actioned at the earliest possibility. We have done a comprehensive survey of the state-of-the-art personal safety solutions and location prediction technologies and proposes an Internet of Things (IoT) based personal safety model, encompassing a prediction framework to anticipate the future vehicle locations by exploiting complex analytics of current and past data variables including the speed, direction and geolocation of the vehicles. Experiments conducted based on real-world datasets demonstrate the feasibility of our proposed framework in accurately predicting future vehicle locations. In this paper, we have a risk assessment of our safety solution model based on OCTAVE ALLEGRO model and the implementation of our prediction model.
    Citation
    Alofe, O, M., Fatema, K., Azad, M., A., and Kurugollu, F. (2020). ‘Persation: an IoT based personal safety prediction model aided solution’. International Journal of Computing and Digital Systems, pp. 1-11.
    Publisher
    University of Bahrain
    Journal
    International Journal of Computing and Digital Systems
    URI
    http://hdl.handle.net/10545/625155
    Additional Links
    https://journal.uob.edu.bh/handle/123456789/4034
    Type
    Article
    Language
    en
    ISSN
    2210-142X
    Collections
    University of Derby Online (UDOL)

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