SmartPPM: An Internet of Things Based Smart Helmet Design for Potholes and Air Pollution Monitoring

In a country with an extensive road network, it is very tough for authorities to identify and repair the potholes on time, which emerge due to casual wear and tear of the road. These potholes are dangerous for unsuspecting high-speed vehicles and results in multiple life-threatening accidents year-round. Apart from potholes, another severe concern about the time spent on roads is air pollution. Breathing the polluted air, mainly containing the particulate matter that has a diameter of fewer than 2.5 micrometers, is toxic to humans. In this work, we have judiciously designed an Internet of Things based smart helmet, which uses crowdsourcing to report potholes and collect crucial on-road air pollution data so that a person could avoid risk to life and health. We have also introduced the novel concept of remembrance factor and severity index, which could be useful in dealing with the stale and invalid pothole data in the database. Received on 02 March 2019; accepted on 17 March 2019; published on 26 April 2019

[1]  Robert Ross,et al.  Augmenting GPS with Geolocated Fiducials to Improve Accuracy for Mobile Robot Applications , 2019 .

[2]  Girts Strazdins,et al.  Real time pothole detection using Android smartphones with accelerometers , 2011, 2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS).

[3]  Dan Popescu,et al.  A mobile sensor network based road surface monitoring system , 2013, 2013 17th International Conference on System Theory, Control and Computing (ICSTCC).

[4]  Yoshihide Sekimoto,et al.  Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images , 2018, Comput. Aided Civ. Infrastructure Eng..

[5]  Nhat-Duc Hoang,et al.  An Artificial Intelligence Method for Asphalt Pavement Pothole Detection Using Least Squares Support Vector Machine and Neural Network with Steerable Filter-Based Feature Extraction , 2018, Advances in Civil Engineering.

[6]  Yuriy Vagapov,et al.  Comparative analysis and practical implementation of the ESP32 microcontroller module for the internet of things , 2017, 2017 Internet Technologies and Applications (ITA).

[7]  Natalya Stankevich,et al.  Why road maintenance is important and how to get it done , 2005 .

[8]  Muhammad Haroon Yousaf,et al.  Computer vision based detection and localization of potholes in asphalt pavement images , 2016, 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE).

[9]  Sharmila Sengupta,et al.  Proposing the systems to provide protection of vehicles against theft and accident , 2016, 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT).

[10]  Carlos Pereira,et al.  Assessing the ESP8266 WiFi module for the Internet of Things , 2018, 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA).

[11]  Baoshan Huang,et al.  Investigation on Service Time and Effective Cost of Typical Pothole Patches in Tennessee , 2014 .

[12]  Gustavo E. A. P. A. Batista,et al.  A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.

[13]  Patricio A. Vela,et al.  Automated Pavement Patch Detection and Quantification Using Support Vector Machines , 2018, J. Comput. Civ. Eng..

[14]  Rajeshwari Madli,et al.  Automatic Detection and Notification of Potholes and Humps on Roads to Aid Drivers , 2015, IEEE Sensors Journal.

[15]  D J Victor,et al.  Road accidents in India , 1989 .

[16]  Duo Liu,et al.  PADS: A Reliable Pothole Detection System Using Machine Learning , 2016, SmartCom.

[17]  Varun Gupta,et al.  Convolutional neural networks based potholes detection using thermal imaging , 2019, J. King Saud Univ. Comput. Inf. Sci..

[18]  Christian Koch,et al.  Pothole detection in asphalt pavement images , 2011, Adv. Eng. Informatics.

[19]  P. Sulander,et al.  Diseases and Motor Vehicle Fatalities in Finland in 2001 and 2002 , 2007, Traffic injury prevention.

[20]  Paul W. Fieguth,et al.  A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure , 2015, Adv. Eng. Informatics.