In this paper, we study the source localization problem in wireless sensor networks where the location of the source is estimated according to the quantized measurements received from sensors in the field. We propose an energy efficient iterative source localization scheme, where the algorithm begins with a coarse location estimate obtained from a set of anchor sensors. Based on the available data at each iteration, we approximate the posterior probability density function (pdf) of the source location using a Monte Carlo method and we use this information to activate a number of non-anchor sensors that minimize the Conditional Posterior CrameÂ¿r Rao Lower Bound (C-PCRLB). Then we also use the Monte Carlo approximation of the posterior pdf of the source location to compress the quantized data of each activated sensor using distributed data compression techniques. Simulation results show that the proposed iterative method achieves the mean squared error that gets close to the unconditional Posterior CrameÂ¿r Rao Lower Bound (PCRLB) for a Bayesian estimate based on quantized data from all the sensors within a few iterations. By selecting only the most informative sensors, the iterative approach also reduces the communication requirements significantly and resulting in energy savings.