The prototype developed aims to mitigate and minimize the loss of lives that takes place at Unmanned Railway Level Crossings with an early-warning system. Sensor nodes are used to acquire data of an incoming train and warn the people with audio and visual indicators. From the data acquired over a period of time, we can develop models that can identify optimal speed limits on the sections of track where there are more such crossings,etc.
This project was selected for the Quarter Finals of the Texas Instruments Innovation Challenge - India Design Contest 2015. The project was built from components and kits provided by Texas Instruments worth Rs.50,000(INR).
The implementation was carried out using MSP430 G-Series microcontroller and Beaglebone Black as the hardware kits to build the sensor nodes and data processing/ decision making unit. The communication is facilitated using TI CC2500 in SPI mode and is wireless. The Beaglebone Black was configured to run Debian Wheezy with all the codes to acquire, process and store the data was written in Python. The Beaglebone Black is additionally configured to serve the acquired data visualized as charts and logs to the administrator as web pages. The wireless communication is made secure with XXTEA algorithm implemented on both the transmitting sensor nodes and the receiver.From the data acquired, a suitable learning algorithm can be implemented to predict the trafiic, put up speed limits (or) block section for the trains to prevent future mishaps.
This project was presented at the Second International Conference on on Intelligent Computing, Communication & Convergence(ICCC), Bhubaneswar, India (2016). The paper can be accessed as PDF from Science Direct.