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

TL;DR

This paper proposes a mobility-aware, predictive data dissemination approach using D2D caching helpers in industrial IoT environments to enhance reliability and reduce latency of high-rate mmWave connections.

Contribution

It introduces a novel predictive mode selection strategy leveraging D2D caching helpers for industrial IoT data dissemination, addressing reliability and latency issues.

Findings

Predictive strategies improve data delivery reliability.

D2D caching reduces content delivery latency.

System-level evaluation confirms benefits of the proposed approach.

Abstract

Industrial automation deployments constitute challenging environments where moving IoT machines may produce high-definition video and other heavy sensor data during surveying and inspection operations. Transporting massive contents to the edge network infrastructure and then eventually to the remote human operator requires reliable and high-rate radio links supported by intelligent data caching and delivery mechanisms. In this work, we address the challenges of contents dissemination in characteristic factory automation scenarios by proposing to engage moving industrial machines as device-to-device (D2D) caching helpers. With the goal to improve reliability of high-rate millimeter-wave (mmWave) data connections, we introduce the alternative contents dissemination modes and then construct a novel mobility-aware methodology that helps develop predictive mode selection strategies based on the anticipated radio link conditions. We also conduct a thorough system-level evaluation of representative data dissemination strategies to confirm the benefits of predictive solutions that employ D2D-enabled collaborative caching at the wireless edge to lower contents delivery latency and improve data acquisition reliability.