An Intelligent Wireless Probe for landslide detection: Nowadays, wireless sensor network (WSN) is used in most of the real time monitoring applications such as flood monitoring, landslide detection, habitat monitoring, health monitoring, military applications etc. WSN consist of distributed autonomous devices which sense the environmental or physical conditions cooperatively and pass the information through the network to a base station. Here landslide scenario is considered. Landslides are one of the major catastrophic disasters happening around the world. In India, landslide occurs mainly due to heavy rainfall and earth quake. Sensor nodes have various energy and computational constraints because of their inexpensive nature and ad hoc method of deployment. Energy optimization is a critical challenge in WSN since sensor nodes only have a small and finite source of energy. Energy optimization is more complicated in sensor networks because it involved not only reduction of energy consumption but also prolonging the life of the network as much as possible.
The main aim of this research is to design an intelligent wireless probe for landslide detection system. Main focus is given in optimizing the energy consumption in each node in the network. Most of the energy consumed in the system is for sensing, processing, transmission and reception of data. For sensing each sensor will consume different amount of energy so certain paradigm is to be designed which intelligently on and off the sensors in mote so that minimal energy is used. The sampling rate of the sensor must be intelligently selected by the node according to the requirement. Power management in radios is very important because radio communication consumes a lot of energy during operation of the system. So, number of transmission and reception of data must reduce based on the application requirement. An efficient software and hardware solution is to be designed to design the intelligent probe which takes adaptive decision base on the dynamic environmental condition.
Team Members
Leader Of the Team : Maneesha V. Ramesh
Faculty : Rekha P.
Student: Divya P. L.
