Energy-Efficient Deep Learning-Based Image Compression for LPWANs on Resource-Constrained AIoT Devices

Date: 22nd January 2024, 2:30 PM


Location: Building 10 (MDSI, GALILEO Garching) + Zoom


Speakers: Nikolai Körber, M.Sc.


Bio: Nikolai Körber received his Bachelor of Science in Computer Science from the University of Applied Sciences Landshut in 2017 and his Master of Science in Computer Science from the same university in 2019. He is currently a PhD candidate in Electrical and Computer Engineering at TUM. He previously worked on several AI-related projects in industry (Fraunhofer IPA, iteratec GmbH, MHP Management- und IT Beratung GmbH) and academia (UAS Landshut, TUM). His research interests lie at the intersection of tinyML and computer vision. As part of his work, he is investigating how to enable the benefits of neural/ generative image compression on resource-constrained devices to address the high bandwidth constraints commonly encountered in sensor networks.


Abstract: Recently, there has been a rising demand for Low-Power Wide-Area Network (LPWAN)-based computer vision applications. LPWANs are specifically designed for long battery life, high transmission range and low production costs, which come however at the expense of very low bandwidths. Consequently, data compression plays a crucial role in energy-efficient image communication due to the significantly higher energy costs associated with communication compared to computation. For that, novel compute-intensive compression methods, like deep learning-based image compression techniques, are promising to further reduce the number of packets to be transmitted. Despite their superiority over hitherto established methods, such as JPEG, there is no related research that jointly addresses deep learning-based compression performance and resource efficiency on sensor platforms. Only recently, high computational power at low battery consumption has become possible by exploiting parallel ultra-low power processors like GAP8. The goal of this research is to develop robust, energy-efficient deep learning-based image compression techniques for LPWANs on resource-constrained AI-enabled IoT (AIoT) devices. Having higher compression rates while operating at low power will dramatically reduce network traffic, extend the battery life of visual IoT sensor nodes, and pave the way to a broad range of new data-intensive applications within the LPWAN/ 5G mMTC communication era.

Research Outline: AI based Quality of Service (QoS) Prediction and Repeater Placement

Date: 22nd January 2024, 2:00 PM


Location: Building 10 (MDSI, GALILEO Garching) + Zoom


Speakers: Patrick Purucker, M.Sc.


Bio: Patrick Purucker has received his Bachelor of Engineering in Electrical Engineering and Information Technology from the University of Applied Sciences Amberg-Weiden in 2020 followed by the Master of Science (Applied Research in Engineering Sciences) in 2021 from the same institution. He is currently working on the EU-funded projects A-IQ Ready and Archimedes focusing on resilient, AI supported wireless communications for harsh underground environments. Previously, he was working on the ADACORSA project, which was successfully completed in 2023, investigating cellular based UAS communications.


Abstract: The talk gives an overview of the research work involved in the planned doctoral study, which focuses on resilient communications for agents conducting autonomous search and rescue missions in challenging underground environments (e.g. tunnels). The research will be conducted within the framework of the projects A-IQ Ready and Archimedes. Thereby, a simulation of the wireless ad-hoc network between an operation center and search agents will be implemented to identify potential risks and tackle them with AI based algorithms. One potential approach is to develop a prediction model for the Quality of Service (QoS) in the vicinity of the agent, with the aim of adapting its trajectory to avoid communication blind spots. Furthermore, Reinforcement Learning algorithms will be researched for optimising the repeater placement within the wireless network.