Saving Victims in Moving Vehicles: An IoT based prediction model aided solution

Olasunkanmi Matthew Alofe, Kaniz Fatema, John Panneerselvam, Fatih Kurugollu

    Research output: Chapter in Book/Published conference outputConference publication


    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. This paper presents 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.
    Original languageEnglish
    Title of host publication2019 2nd IEEE Middle East and North Africa COMMunications Conference (MENACOMM)
    ISBN (Electronic)978-1-7281-3687-5
    ISBN (Print)978-1-7281-3688-2
    Publication statusPublished - 10 Feb 2020

    Publication series

    Name2019 2nd IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2019


    • GPS
    • IoT
    • location prediction
    • mobile application
    • vehicle location identification


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