In this seminar, we will examine communication-efficient policies tailored to the unique demands of IoT applications. Rather than focusing solely on maximizing throughput, we will explore how messages contribute to overarching objectives—such as monitoring system status or controlling physical processes—and balance these factors to prevent undue network congestion. Over the course of the seminar, students will gain a deeper understanding of relevant modeling approaches, design considerations, and performance trade-offs. Depending on the student’s interests and background, the seminar can be oriented toward methods grounded in reinforcement learning or dynamic programming to devise and analyze these communication-efficient strategies.

Inspired by the framework of real-time tracking under imperfect forward and feedback channels (as discussed in [arXiv:2407.06749v2]), we will examine how to minimize a time-averaged distortion (or estimation error) while respecting energy constraints. The overarching theme is deciding “when” and “how” to send status updates so that network congestion is avoided and physical processes are accurately tracked, despite error-prone acknowledgments and partial knowledge at the transmitter.

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Voraussetzungen