EFFICIENT DATA TO DECISION PIPELINES FOR EMBEDDED AND SOCIAL SENSING BY

This dissertation presents results of our studies in making data to decision pipelines for embedded and social sensing ecient. Due to the pervasive presence of wired sensors, wireless sensors, and mobible devices, the amount of data about the physical world (environmental measurements, trac, etc.) and human societies (news, trends, conversations, intelligence reports, etc.) reaches an unprecedented rate and volume. This motivates us to optimize the way information is collected from sensors and social entities. Two challenges are addressed: (i) How can we gather data such that throughput is maximized given the physical constraints of the communication medium? and (ii) How can we process inherently unreliable data, generated by large networks of information and social sources? We present some essential solutions addressing these challenges in this dissertation. The dissertation is organized in two parts. Part I presents our solution to maximizing bit-level data throughput by utilizing multiple radio channels in applications equiped with wireless sensors. Part II presents our solution to dealing with the large amount of information contributed by unvetted sources. i