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Industrial automation deployments constitute challenging environments wheremoving IoT machines may produce high-definition video and other heavy sensordata during surveying and inspection operations. Transporting massive contentsto the edge network infrastructure and then eventually to the remote humanoperator requires reliable and high-rate radio links supported by intelligentdata caching and delivery mechanisms. In this work, we address the challengesof contents dissemination in characteristic factory automation scenarios byproposing to engage moving industrial machines as device-to-device (D2D)caching helpers. With the goal to improve reliability of high-ratemillimeter-wave (mmWave) data connections, we introduce the alternativecontents dissemination modes and then construct a novel mobility-awaremethodology that helps develop predictive mode selection strategies based onthe anticipated radio link conditions. We also conduct a thorough system-levelevaluation of representative data dissemination strategies to confirm thebenefits of predictive solutions that employ D2D-enabled collaborative cachingat the wireless edge to lower contents delivery latency and improve dataacquisition reliability.

Caching-Aided Collaborative D2D Operation for Predictive Data Dissemination in Industrial IoT