Persation: an IoT Based Personal Safety Prediction Model Aided Solution

Olasunkanmi Alofe, Kaniz Fatema, Fatih Kurugollu, Muhammad Azad

Research output: Contribution to journalArticlepeer-review

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 the OCTAVE ALLEGRO model and the implementation of our prediction model.

Original languageEnglish
Pages (from-to)1034-1047
Number of pages13
JournalInter Journal of Computing and Digital Systems
Volume9
Issue number6
DOIs
Publication statusPublished - 1 Nov 2020

Bibliographical note

Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International

Keywords

  • GPS
  • IoT
  • Location Prediction
  • Mobile Application
  • Vehicle Location Identification

Fingerprint Dive into the research topics of 'Persation: an IoT Based Personal Safety Prediction Model Aided Solution'. Together they form a unique fingerprint.

Cite this