Sextant: a unified node and event localization framework using non-convex constraints

Determining node and event locations is a canonical task for many wireless network applications. Yet dedicated infrastructure for determining position information is expensive, energy-consuming, and simply unavailable in many deployment scenarios. This paper presents an accurate, cheap and scalable framework, called Sextant, for determining node position and event location in sensor networks. Sextant operates by setting up and solving a system of geographic constraints based on connectivity information from the underlying communication network. Sextant achieves high accuracy by enabling non-convex constraints to be used to refine position estimates. It represents position estimates as potentially non-contiguous collections of points. This general representation enables Sextant to use _negative information_, that is, information on where a node or event is not located, to refine location estimates. Sextant unifies both node and event detection within the same general framework. It can provide high precision without dedicated localization hardware by aggressively extracting constraints from the link layer, representing areas precisely with Bézier-enclosed polygons and probability distributions, and using event detection to refine node position estimates. A compact representation and a fully distributed implementation make the framework practical for resource-limited devices. The framework has been implemented, deployed and tested on laptops, PDAs and Mica-2 motes. Physical experiments show that a large number (98%) of the nodes in a network can determine their positions based on a small number (30%) of landmark nodes and that a large number (90%) of events can be located with low median error.