SESSION SYNOPSIS
Nowadays, there is a vast research interest in the intelligent and automated industry, which should
increase uptime and reliability while decreasing energy usage and maintenance costs. The integration of intelligent digital technologies into manufacturing and industrial processes, including industrial Internet of Things (IoT) networks, Machine Learning (ML), tinyML, low-power always-on sensors, and robotics, is commonly called Industry 4.0. In this scenario, the shared challenge of reliably collecting data from a heterogeneous variety of machinery, processing it locally, and transmitting it remotely while providing accurate system operating condition predictions at low latency is still an open and prolific research challenge.
To this purpose, cloud and edge computing alternate centered management to scattered and autonomous appliances. Nonetheless, emerging technologies and novel combinations of software and hardware open new opportunities; for example, researchers can adopt local analytics of massive data to tackle the needs of Industry 4.0. Conversely, intelligent and low-power sensor nodes can gather information anywhere and anytime from operating equipment without altering routines. Indeed, although modern edge computing allows for running data-driven models locally with benefits for latency and bandwidth, the limited power budget and the constrained memory footprint are open obstacles. Developing engineering-based systems for Industry 4.0 is vital to designing secure, interoperable, portable, plug and play, scalable, low-power, and reactive solutions. However, the research in Industry 4.0 is still embryonic and includes untacked challenges that involve edge computing, artificial intelligence, tinyML, smart sensors, and intelligent IoT.
This special session is a call for researchers, practitioners, and educators interested in any aspect of novel software design, edge computing, and smart sensors in the field of Industry 4.0. We aim to engage developers, teams, and organizations to face and discuss the challenges of cloud and edge computing engineering for smart sensors in Industry 4.0.