Location Estimation Using RSSI and Application of Extended Kalman Filter in Wireless Sensor Networks

Wireless sensor networks are mostly used for their monitoring applications in these applications, the network captures the environment and searches for occurrences of events by sensing different physical properties, such as temperature, humidity, pressure, ambient light, and movement. Location in time and space is a paramount issue and nodes are usually required for tracking purpose. The availability of RSSI in the motes help us to estimate finding the location in terms of time and space. And then sensor nodes deliver the coordinate location to base station through neighboring sensor nodes. The availability of RSSI in the sensor networks made us to try an attempt to identify the locations of unknown sensor nodes. It is greatly needed because of their location and data both are combined in a real time manner as they separate with other it becomes meaningless. Our method showed better results in comparison with the existing methods.