New affordable in-situ sensors are part of the Internet of Things (IoT) and enable us to build denser environmental sensor networks that measure phenomena more efficiently which vary greatly in space and time, such as air or noise pollution. However, such dense wireless sensor networks can put additional strain on bandwidth in areas with less network coverage, and they threaten citizens' privacy. One solution is to move the processing and analysis of collected geodata to the sensors. This type of edge computing has been made possible by advances in deep learning methods on embedded hardware, e.g., Tensorflow Lite for microcontrollers.
You will support our ambitions by working together with colleagues from several departments on projects that use small, affordable sensors (embedded devices) to collect environmental data and submit this data wirelessly. This position will therefore advance our understanding of processing geodata on the (network) edge and combine different geodata sources (remote and in-situ) in reproducible, distributed (networked) workflows. More specifically, you will advance the state-of-the-art in embedded devices and Internet of Things technologies (hardware platforms, network protocols) by designing software solutions for environmental sensing using diverse open hardware platforms, adapting and implementing major developments in the application of machine learning / AI on embedded devices, and propose and implement new workflows that incorporate IoT technology in research and capacity development projects.
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