Event Source Position Estimation Using Sensor Networks

This paper investigates the use of a wireless sensor network (WSN) for estimating the location of an event source that releases a certain signal or substance in the environment which is then propagated over a large area. The concentration of the substance at the source location is assumed unknown. The sensor nodes are able to measure the substance concentration at their own locations but the measurements are noisy. Based on these concentration readings we use nonlinear Least Squares (LS) optimization to estimate the event source position. Such a network can be of tremendous help for environmental monitoring or for emergency personnel responding to a catastrophic event. Our simulation results indicate that the LS method performs significantly better that the Closest Point Approach (CPA) where the source location is assumed to be the sensor node with the highest measurement. Furthermore, our results indicate that in the presence of a draft that pushes the substance in certain direction, a threshold is necessary for the LS method to yield accurate results. In addition, our results show that the use of unconstrained optimization or the existing knowledge of the wind direction can further improve the performance of the location estimate.