Guest Editorial: An End-to-End Machine Learning Perspective on Industrial IoT

The world is witnessing the emergence of billions of IoT devices fueled by tremendous leaps in computing paradigms spanning clients, the edge, and the cloud, as well as the ultra-low-latency and gigabit-per-second connectivity offered by 5G networks. It is estimated that IoT devices will generate 90 Zettabytes of data at the network edge by 2025 (IDC Data Age 2025 Whitepaper). The Industry 4.0 revolution is one of the major drivers for this large-scale adoption of IoT and is taking place rapidly, transforming a wide range of markets including manufacturing, energy, agriculture, transportation, and logistics. For example, in smart manufacturing, predictive maintenance for minimizing downtime, location tracking for inventory, and automation are some key areas that can boost yield and decrease time to market. Similarly, in energy, smart metering and detecting defects in oil/gas extraction stations as well as in the extracted matter are leading to significant improvements in operational and capital expenditures.