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Location tracking has several applications in mobile (cellular or ad hoc) networks, such as location-based routing algorithms and consumer services. It is often difficult to compute the location of a node precisely because of the infrastructure costs and the errors inherent in most tracking techniques. Furthermore, this accuracy differs amongst nodes based on the scattered availability of equipment such as GPS. We focus on heterogeneous mobile networks, wherein some nodes know their locations more precisely than others and there is a short-range peer-to-peer communication channel such as Bluetooth or 802.11. We consider a generalized notion of location, called vicinity, which is the set of potential locations for a node. We formulate a hierarchy of distance constraints that can be applied in a network and devise efficient distributed techniques for computing the most optimal (smallest) vicinities under various constraint classes. In particular, our algorithms use both proximity and non-proximity relationships between the nodes. We present simulation results establishing the effectiveness of using these different types of constraints.