Open Access
Tracking Precarious Aerial Swaggers using IoT-Enabled Drone
Author(s) -
Nagaraj Telkar,
Pavankumar Naik,
Shrikanta Jogar,
Pratibha Hulagur,
Smeeta Policepatil,
Sushma Giraddi,
Vanaja Koppad
Publication year - 2019
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit195330
Subject(s) - drone , internet of things , computer security , emergency response , computer science , identification (biology) , natural disaster , control (management) , aeronautics , real time computing , engineering , geography , artificial intelligence , medical emergency , genetics , biology , medicine , botany , meteorology
Emergency response teams are accused with ensuring citizen safety from life-threatening events such as structural fires, gas leakages, vehicle accidents, and precarious material spills. While overseeing such occasions is dangerous, the release of precarious materials, such as toxic chemicals, into the atmosphere is particularly challenging. Upon landing in a scene, response teams must quickly identify the precarious substance and the contaminated area to limit exposure to nearby population centres. For airborne toxins, this appraisal is confounded by natural conditions, for example, alters in wind speed and course that can cause unstable, elevated swaggers to move powerfully. Without a way to dynamically monitor and assess atmospheric conditions during these events, response teams must conservatively predict the extent of the contaminated area, then orchestrate evacuations, and reroute traffic to ensure the safety of nearby populations. In this paper, we propose outfitting drone with Internet of Things (IoT) sensor platforms to enable dynamic tracking of precarious aerial swaggers. Augmenting drone with sensors enables emergency response teams to maintain safe distances during precarious identification, minimizing first response team exposure. Additionally, we integrate sensor-based particulate detection with autonomous drone flight control providing the capability to dynamically identify and track the boundaries of aerial swaggers in real time. This empowers specialists on call for outwardly recognize swagger development and better foresee and disconnect the effect zone. We describe the composition of our prototype IoT-enhanced drone system and describe our initial evaluations.