The promised benefits of the Internet of Things (IoT) are predicated on the notion that better decisions will be enabled through a multitude of autonomous sensors (often deployed by different companies) providing real-time knowledge of the state of things. This knowledge will be imperfect, however, due to sensor quality limitations. A sensor can improve its estimation quality by soliciting a state estimate from other sensors operating in its general environment. Target selection (which sensors to talk with to solicit their estimates) is challenging because sensors may not know the underlying inference models or qualities of sensors deployed by other companies. This lack of trust (or familiarity) in others' inference models creates noise in the received estimate, but trust builds and noise reduces the more frequently a sensor targets any given sensor. We characterize the initial and long-run information sharing network for an arbitrary collection of sensors operating in an autoregressive environment. The state of the environment plays a key role in mediating quality and trust in target selection. When qualities are known and asymmetric, each sensor eventually settles on a constant target set in all future periods but this long-run target set is sample-path dependent and varies by sensor. When qualities are ambiguous, ongoing randomization across different subsets of sensors may be optimal. [This is joint work with Soroush Saghafian from the Harvard Kennedy School and Stephan Biller from IBM.]