tinyML Challenge 2022: Smart weather station

Developing Countries is the area of the globe where land-based, in situ monitoring of weather and climate is at its scarcest, but at the same time has arguably the most potential to benefit society.

Rainfall and temperature can have high spatial variability due to the strong feedback that can exist between the land and atmosphere. Temperature can change rapidly in space due to land-cover heterogeneity and changing altitude over complex mountainous terrain. This means that a weather station tens of kilometers away may measure conditions that have little relevance to your location, making it hard to make informed local decisions.

The goal of this challenge is to create a low-cost, low-power, reliable, accurate, easy to install and maintain weather station, with no mechanical moving parts for measuring all weather conditions with a focus on rain and wind, based on based on ultra-low power machine learning at the edge, that can be deployed locally.

This talk will introduce the 2022 tinyML Challenge and how you can participate in this Challenge.

Date

June 29, 2022

Location

Virtual

Contact us

Discussion

Schedule

Timezone: PDT

TinyML Challenge 2022: Smart weather station

Thomas BASIKOLO, Programme Officer

ITU

Marco ZENNARO, Research Scientist

the Abdus Salam International Centre for Theoretical Physics

Alessandro GRANDE, Director of Product

Edge Impulse

Thomas BASIKOLO, Programme Officer

ITU

Thomas Basikolo works with the ITU coordinating and managing the AI for Good’s ML5G activities and as an advisor of the ITU-T Focus Group on Autonomous Networks. He received a PhD in Electrical and Computer Engineering from Yokohama National University, Japan. During his studies, he was awarded the Japanese Government (Monbukagakushō) scholarship. He was also a recipient of grants for Non-Japanese Researchers from the NEC C&C Foundation, and a visiting researcher at the NEC Data Science Research Laboratories. Prior to joining ITU, he worked as a Research Engineer in the Engineering Department of Microwave Factory Co., Ltd, Tokyo, Japan.

He is recipient of multiple Best Paper Awards, the IEEE AP–S Japan Student Award and the Young Engineer of the year award by IEEE AP–S Japan in 2018. He has co-authored peer-reviewed journal and conference papers, predominantly in the areas wireless communications and antenna engineering. He serves as a Reviewer of IEEE and IEICE Journals. His interests includes, machine learning, deep learning and network science, and their applications in wireless networks.

Marco ZENNARO, Research Scientist

the Abdus Salam International Centre for Theoretical Physics

Marco Zennaro is a research scientist at the Abdus Salam International Centre for Theoretical Physics in Trieste, Italy, where he coordinates the Science, Technology and Innovation Unit. Marco co-chairs the tinyML4D working group. He received his PhD from the KTH-Royal Institute of Technology, Stockholm. His research interest is in ICT4D, the use of ICT for Development, and in particular he investigates the use of Internet of Things for Development (#IoT4D). He has organized more than 30 training activities on IoT in Developing Countries. Marco is a Visiting Professor at Kobe Institute of Computing (KIC) in Kobe, Japan. More info at: http://users.ictp.it/~mzennaro/ 

Alessandro GRANDE, Director of Product

Edge Impulse

Alessandro is a physicist, an engineer, a community builder and a communicator with a visceral passion for connecting and empowering humans to build a more sustainable world through the aid of technology. Alessandro is the Director of Technology at Edge Impulse and co-organizes the tinyML Meetups in UK and Italy.

Prior to Edge Impulse, Alessandro worked at Arm as a developer evangelist and ecosystem manager with a focus on IoT and TinyML. While at Arm Alessandro launched a weekly live stream – Innovation Coffee with his colleague Robert Wolff.

Alessandro holds a master’s degree in nuclear physics from the University of Rome “La Sapienza”.

Schedule subject to change without notice.