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Difference between revisions of "Low-Power Environmental Sensing"

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Revision as of 11:16, 14 May 2024

Smart sensors, which are sensors equipped with advanced computation capabilities, present a unique opportunity for innovation in multiple critical domains such as environmental sensing to reduce global greenhouse gas emissions or infrastructure monitoring. Our infrastructure is responsible for almost 80% of all global greenhouse gas emissions. Therefore, it is very important to better understand the individual contributors and to be able to build precise models to plan future infrastructure.

Multi-modal sensing enables on-device feature extraction, reducing communication overhead, perform predictions, increases privacy and enable on-device learning.
Smart sensors allow the extraction of meaning from the sensed data.

A near-sensor processing of the gathered data that directly extracts meaning from the signal can lead to huge improvements in terms of energy efficiency and increased privacy. The reduced energy consumption comes from the fact that more specialized hardware such as the PULP platform including the GAP9 can be used, and, additionally, that the information density of the transmitted data is higher, and thus less data must be transmitted via the often very power-intensive wireless link.

Our long-term goal is to develop a low-power sensing node with very low power consumption. These nodes should incorporate multiple sensors to enable multi-modal sensing. In other words, combine the data of the different sensors to extract additional data. The idea is to use the very efficient GAP9 multicore processor and deploy multi-modal neural networks to perform feature extraction, prediction, anomaly detection, and even on-device learning to adaptively fine-tune the model based on the measured data.

Contact Information

Philip Wiese
Victor Kartsch Morinigo
Victor Kartsch Morinigo
Dr. Andrea Cossettini


Projects Overview

Available Projects

External Links


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