User location prediction based on past measurements is extensively studied and we use one of the commonly used approach based on Hidden Markov Model. As we are specifically considering second order Hidden Markov model, which takes into consideration the direction of motion to improve the prediction performance over a first order HMM. Other methods like kalman filter can also be employed but considering the sample size due to past measurements the filter computation will be quite complex due to increase in matrix sizes. Using the HMM model algorithm, among the available sensors the one with lowest energy is selected and used in determining the location. Also it is shown in literature that the computational overhead for a second order HMM is quite negligible and can be safely ignored.