CAREER: Unified Probabilistic Localization for Sensor Networks: Theoretic and Practical Foundations

Project Details


Spatial localization is a prerequisite for a range of sensor network tasks, such as monitoring, tracking, routing and security services. Currently, a variety of approaches are employed in providing critical positioning information to sensor nodes. Although these approaches provide a rich environment for innovation, their diversified strategies introduce problems of standardization and inefficient resource use. For example, many techniques require unique infrastructures, and among different localization technologies, little is understood about cost performance tradeoffs in the presence of systematic distortions and noise. This research addresses the need to construct a scalable, unified family of localization services over a variety of sensor node types. The algorithmic methodology centers on Bayesian networks. These networks allow for a systematic way to manage signal distortion and noise and enable the integration of the spectrum of current sensor network positioning modalities: signal strength to distance, angle of arrival, time difference of arrival, and fingerprinting. This research will result in a universal localization infrastructure capable of integrating location information into any computing device and provide new benchmarks that describe the limits of localization performance in the presence of distortion and noise.

These findings will allow designers to select the localization modalities that maximize their application's performance goals given their cost constraints and will promote the ubiquitous use of low-cost sensor nodes. Through dissemination of the results in both archival publications and new curricula, this project will advance the development of sensor applications by enabling scalable localization services for a diverse range of sensor networks.

Effective start/end date2/1/051/31/11


  • National Science Foundation: $448,065.00


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