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Towards the informed design of large-scale distributed data-gatheringarchitectures under real-world assumptions such as nonzero communication delaysand unknown environment dynamics, this paper considers the effects of allowingfeedback communication from the central processor to external sensors. Usingsimple but representative state-estimation examples, we investigate fundamentaltradeoffs between the mean-squared error (MSE) of the central processor'sestimate of the environment state, and the total power expenditure per sensorunder more conventional architectures without feedback (INformation) versusthose with broadcast feedback (OUTformation). The primary advantage of enablingfeedback is that each sensor's understanding of the central processor'sestimate improves, which enables each sensor to determine when and what partsof its current observations to transmit. We use theory to demonstrateconditions in which OUTformation maintains the same MSE as INformation withless power expended on average, and conditions in which OUTformation obtainsless MSE than INformation at additional power cost. These performance tradeoffsare also considered under settings where environments undergo less variation,and sensors implement random backoff times to prevent transmission collisions.Our results are supported via numerical studies, which show that the propertiesderived in theory still hold even when some of the simplifying assumptions areremoved.

OUTformation: Distributed Data-Gathering with Feedback under Unknown Environment and Communication Delay Constraints