66In many environmental monitoring applications, the location of the sensor node is important information. Due to the large number of sensor nodes to be deployed, it is not practical to equip them with global positioning system (GPS) or manually determine their locations. In this chapter, a smart localization algorithm using maximum likelihood estimation (MLE) with negative constraints (NCs) is proposed. Unlike most of the existing methods that only utilize positive constraint information such as internode distances or connectivity, the proposed algorithm also utilizes NC information to achieve more accurate localization. The distribution of sensor nodes’ communication ranges is first studied, and the likelihood function of sensor nodes’ positions is derived based on both the positive and negative constraints. To reduce the computational cost, a novel iterative optimization procedure is also proposed to find the MLE. Simulation and experimental works show that the proposed MLE localization algorithm with NC improves the localization accuracy by 20% as compared to the conventional MLE approach.