ESCAPE: Efficient and Scalable Collection, Analytics and Processing of Big Data for Disaster Applications

Efficient management of natural and man-made disasters poses technical challenges like energy efficient collection of sensor data, optimizing the network bandwidth, faster analysis of the past data/events and dissemination of timely and correct information using Sensor cloud architecture to the people involved in decision-making.
The outcomes from this project is to assist human operators in their disaster management coordination and planning like directing a medical physician’s team to their nearest cluster of affected people in a region and administer medications as necessary or finding a safe route for evacuation of affected people. Sensor data integrated with microblogs such as Tweets help identifying some local events and people sentiments, which is significantly useful in handling/understanding disaster situations. It will also benefit other applications such as real-time tracking of road/driving conditions in vehicular networks.

Researcher

Md. Yasin Kabir