The process of determining the physical locations of nodes in a wireless sensor network is known as localization . Self-localization is critical for large-scale sensor networks, because manual or assisted localization is often impractical due to time requirements, economic constraints, or inherent limitations of the deployment scenarios. We propose scalable solutions for reliably localizing wireless sensor networks in environments conducive to several types of ranging errors. We follow a hybrid hardware-software approach for acoustic ranging or radio interferometry to acquire internode distance measurements, and a resilient self-localization algorithm to compute the node location estimates. The acoustic ranging method improves on previous work, extending the practical measurement range up to 35 m in grassy outdoor environments, achieving a distance-invariant median measurement error of about 1% (33 cm). The localization algorithm is based on least-squares scaling with soft constraints. Empirical evaluation using ranging results obtained from sensor network field experiments and simulations confirms that our approach is more resilient than multidimensional scaling (MDS) algorithms against large-magnitude ranging errors and sparse range measurements: conditions that are common in large-scale outdoor sensor network deployments.

The process of computing the physical locations of nodes in a wireless sensor network is known as localization. Self-localization is critical for large-scale sensor networks because manual or assisted localization is often impractical due to time requirements, economic constraints or inherent limitations of deployment scenarios. We have developed a service for reliably localizing wireless sensor networks in environments conducive to ranging errors by using a custom hardware-software solution for acoustic ranging and a family of self-localization algorithms. The ranging solution improves on previous work, extending the practical measurement range threefold (20-30m) while maintaining a distance-invariant median measurement error of about 1% of maximum range (33cm). The localization scheme is based on least squares scaling with soft constraints. Evaluation using ranging results obtained from sensor network field experiments shows that the localization scheme is resilient against large-magnitude ranging errors and sparse range measurements, both of which are common in large-scale outdoor sensor network deployments.

We present a novel method for tracking the movement of people or vehicles in open outdoor environments using sensor networks. Unlike other sensor network-based methods, which depend on determining distance to the target or the angle of arrival of the signal, our cooperative tracking approach requires only that a sensor be able to determine if an object is somewhere within the maximum detection range of the sensor. We propose cooperative tracking as a method for tracking moving objects and extrapolating their paths in the short term. By combining data from neighboring sensors, this approach enables tracking with a resolution higher than that of the individual sensors being used. We employ statistical estimation and approximation techniques to further increase the tracking precision, and to enable the system to exploit the tradeoff between accuracy and timeliness of the results. We analyze the behavior of the cooperative tracking algorithm through simulation, focusing on the effects of approximation techniques on the quality of estimates achieved. This work focuses on acoustic tracking, however the presented methodology is applicable to any sensing modality where the sensing range is relatively uniform.