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Internet of medical things for smart D3S to enable road safety

Author

Listed:
  • Muhammad Ramzan
  • Shahid Mahmood Awan
  • Hamza Aldabbas
  • Adnan Abid
  • Muhammad Farhan
  • Shehzad Khalid
  • Rana M Amir Latif

Abstract

It has been seen that most of the accidents occur due to driver’s fatigue. Drowsiness is a state of mind before the driver falls asleep, which means the driver could not accomplish his actions, such as vehicular braking, controlling vehicular motion, properly. We have built an Internet of things–based medical application to analyze driver’s drowsiness. An architecture has been proposed and a simulation of that scenario in NS3 WSN simulation tool has been done. This simulation shows that the ratio of accidents can be majorly reduced. When drowsiness of drivers is captured, a message alert is delivered to all other drivers of the vehicles that are near to the sleeping driver; for this, different sensor nodes are used. Another unique feature of the sensor network used here is the collaborative effect of sensor nodes. So for measurement and analysis of applications on Google Play, a dataset of the medical applications category was scraped. The scraping was done with 550 applications of each category of medical applications. On each application on Google Play store, almost 70 attributes for each category were scraped. It is envisioned that, in future, wireless sensor networks will be an integral part of our lives, more so than the present-day personal computers.

Suggested Citation

  • Muhammad Ramzan & Shahid Mahmood Awan & Hamza Aldabbas & Adnan Abid & Muhammad Farhan & Shehzad Khalid & Rana M Amir Latif, 2019. "Internet of medical things for smart D3S to enable road safety," International Journal of Distributed Sensor Networks, , vol. 15(8), pages 15501477198, August.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:8:p:1550147719864883
    DOI: 10.1177/1550147719864883
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    References listed on IDEAS

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    1. Chaowei Wang & Chensheng Li & Cai Qin & Weidong Wang & Xiuhua Li, 2018. "Maximizing spatial–temporal coverage in mobile crowd-sensing based on public transports with predictable trajectory," International Journal of Distributed Sensor Networks, , vol. 14(8), pages 15501477187, August.
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