Abstract: The Internet of Things, the idea that the physical world around us can be digitized, monitored and controlled, is fascinating as it complex. IoT is a mix of smart and dumb ‘things’, a digital ecosystem that keeps growing in size and complexity, generating a vast variety of incomplete, unstructured data. IoT is emerging as one of the biggest big-data problems at hand but is unlike any other data science projects. It is a complex spatio-temporal problem, whereby data sources are heterogeneous, unreliable, unreliably connected, and often hard to correlate. So how can we make sense of IoT data? How can we avoid turning it into an unpredictable mess?
In this talk, I explore the missed potential of Cloud-based IoT systems, whereby the sensed data is transferred pretty much un-processed to the Cloud. I argue that to make significant insights from IoT data, we need to initiate intelligent processes at the micro-edge (at the sensor nodes). By means of recent pilot studies, I illustrate the value of shallow learning and other lightweight learning methods, which may be employed to improve data quality and address communication and energy bottlenecks in typical IoT systems. I advocate an extensive use of embedded machine learning to perform a range of data analysis tasks at the very edge of the IoT, employing intelligent processes for tasks such as data cleaning, missing-data management, compression, anomaly detection, and for self-tuning the data collection itself. All-in-all, this talk is about going from ‘cloud-based IoT’ to ‘intelligent IoT’, where learning and sensing take place concurrently.

Biography: Antonio Liotta is Full Professor at the Faculty of Computer Science, Free University of Bolzano (Italy), where he teaches Data Science and Computer Networks. Antonio’s passion for artificial intelligence, has driven his academic career through the meanders of artificial vision, e-health, intelligent networks and intelligent systems. Antonio’s team is renowned for his contributions to micro-edge intelligence and miniaturized machine learning, which have significant potential in harnessing data-intensive systems, for instance in the context of smart cities, cyber-physical systems, Internet of Things, smart energy, and machine learning with humans in the loop. He has led the international team that has recently made a breakthrough in artificial neural networks, initiating a new research strand on sparse neural networks for embedded learning. Antonio was the founding director of the Data Science Research Centre at the University of Derby. He has set up several cross-border virtual teams, and has been credited with over 350 publications involving, overall, more than 150 co-authors. Antonio is Editor-in-Chief of the Springer Internet of Things book series , and associate editor of several prestigious journals. He is co-author of the books Networks for Pervasive Services: six ways to upgrade the Internet and Data Science and Internet of Things.

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