Persation: an IoT based personal safety prediction model aided solution

Journal article


Alofe, Olasunkanmi Matthew, Fatema, Kaniz, Azad, Muhammad Ajmal and Kurugollu, Fatih 2020. Persation: an IoT based personal safety prediction model aided solution. International Journal of Computing and Digital Systems.
AuthorsAlofe, Olasunkanmi Matthew, Fatema, Kaniz, Azad, Muhammad Ajmal and Kurugollu, Fatih
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.

KeywordsIoT, mobile application, vehicle location identification, GPS, location prediction
Year2020
JournalInternational Journal of Computing and Digital Systems
PublisherUniversity of Bahrain
ISSN2210-142X
Web address (URL)http://hdl.handle.net/10545/625155
hdl:10545/625155
Publication dates2020
Publication process dates
Deposited04 Sep 2020, 11:30
Accepted17 Jul 2020
ContributorsUniversity of Derby and Aston University, Birmingham
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