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In this paper, we explore the possibility of using directionality of free-space-optical (a.k.a. optical wireless) communications for solving the 3-D localization problem in ad-hoc networking environments. Range-based localization methods either require a higher node density (i.e., at least three other localized neighbors must exist) than required for assuring connectedness or a high-accuracy power-intensive ranging device such as a sonar or laser range finder which exceeds the form factor and power capabilities of a typical ad-hoc node. Our approach exploits the readily available directionality information provided by a physical layer using optical wireless and uses a limited number of GPS-enabled nodes, requiring a very low node density (2-connectedness, independent of the dimension of space) and no ranging technique. We investigate the extent and accuracy of localization with respect to varying node designs (e.g., increased number of transceivers with better directionality) and density of GPS-enabled and ordinary nodes as well as messaging overhead per re-localization. Although denser deployments are desirable for higher accuracy, our method still works well with sparse networks with little message overhead and small number of anchor nodes (as little as 2). We also present a proof-of-concept prototype of our FSO-based localization techniques and show the validity of our approach even with three transceivers per node.

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Abstract.Funding from the government agencies has been the driving force for the research and educational institutions particularly in the United States. The government funds billions of dollars every year to lead research initiatives that will shape the future. In this paper, we analyze the funds distributed by the United States National Science Foundation (NSF), a major source of academic research funding, to understand the collaboration patterns among researchers and institutions. Using complex network analysis, we interpret the collaboration patterns at researcher, institution, and state levels by constructing the corresponding networks based on the number of grants collaborated at different time frames. Additionally, we analyze these networks for small, medium, and large projects in order to observe collaboration at different funding levels. We further analyze the directorates to identify the differences in collaboration trends between disciplines. Sample networks can be found at