I received my B.Sc. and M.Sc. in Electrical Engineering from Technische Universität Berlin in 2015 and 2017 respectively. During my master studies I was working as a student research assistant with the Telecommunication Networks Group at TU Berlin. Since 2017 I have been a visiting research student with the Distributed Sensing Systems Group at CSIRO, Australia. In 2018 I joined the Network Embedded Systems Lab as a PhD student.
The focus of my work is on battery-free, energy harvesting devices. My vision is to push the boundaries of what these tiny and sustainable devices can do by allowing them to communicate and to collaborate.
I am the lead developer of the Shepherd testbed, a distributed tool for experiments with collections of battery-free devices. With Find, we developed the first neighbor discovery protocol for battery-free devices that allows devices to initiate direct device-to-device communication. Most recently, I’ve been working on a connection protocol that enables two battery-free devices to establish and maintain long-running connections under dynamically changing energy conditions.
2023
Deumer, Milan; Sandhu, Moid; Khalifa, Sara; Kusy, Brano; Geissdoerfer, Kai; Zimmerling, Marco; Jurdak, Raja
A Battery-free Wearable System for On-device Human Activity Recognition Using Kinetic Energy Harvesting Conference
Proceedings of the 20th International Conference on Embedded Wireless Systems and Networks (EWSN), 2023.
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year = {2023},
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Geissdoerfer, Kai; Splitt, Ingmar; Zimmerling, Marco
Demo Abstract: Building Battery-free Devices with Riotee Presentation
09.05.2023.
@misc{Geissdoerfer2023,
title = {Demo Abstract: Building Battery-free Devices with Riotee},
author = {Kai Geissdoerfer and Ingmar Splitt and Marco Zimmerling},
year = {2023},
date = {2023-05-09},
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abstract = {Battery-free devices eliminate the need for batteries, which are expensive, environmentally harmful, and require frequent replacement, thus reducing waste and making devices more cost-effective. We introduce Riotee, the next-generation platform for the battery-free Internet of Things. The platform comprises a base module, a debug probe that allows to conveniently update the firmware on the base module, and a number of expansion boards that extend the capabilities of the platform without the need to design a custom printed circuit board (PCB). We provide a brief overview of Riotee, and describe a demo setup that showcases the key functionality and how to get started with the platform in less than three minutes.},
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Battery-free devices eliminate the need for batteries, which are expensive, environmentally harmful, and require frequent replacement, thus reducing waste and making devices more cost-effective. We introduce Riotee, the next-generation platform for the battery-free Internet of Things. The platform comprises a base module, a debug probe that allows to conveniently update the firmware on the base module, and a number of expansion boards that extend the capabilities of the platform without the need to design a custom printed circuit board (PCB). We provide a brief overview of Riotee, and describe a demo setup that showcases the key functionality and how to get started with the platform in less than three minutes.
2022
Geissdoerfer, Kai
Methods and Tools for Battery-free Wireless Networks PhD Thesis
2022.
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title = {Methods and Tools for Battery-free Wireless Networks},
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Geissdoerfer, Kai; Zimmerling, Marco
Learning to Communicate Effectively Between Battery-free Devices Conference
Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2022.
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2021
Geissdoerfer, Kai; Zimmerling, Marco
Bootstrapping Battery-free Wireless Networks: Efficient Neighbor Discovery and Synchronization in the Face of Intermittency Conference
Proceedings of the 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2021.
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Sandhu, Muhammad Moid; Khalifa, Sara; Geissdoerfer, Kai; Jurdak, Raja; Portmann, Marius
SolAR: Energy Positive Human Activity Recognition using Solar Cells Conference
Proceedings of the 19th IEEE International Conference on Pervasive Computing and Communications (PerCom), 2021.
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2020
Geissdoerfer, Kai; Chwalisz, Mikołaj; Zimmerling, Marco
Taking a Deep Dive Into The Batteryless Internet of Things With Shepherd Journal Article
In: ACM GetMobile: Mobile Computing and Communications, vol. 24, iss. 3, 2020.
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Geissdoerfer, Kai; Schmidt, Friedrich; Kusy, Brano; Zimmerling, Marco
Demo: Bootstrapping Batteryless Networks Using Fluorescent Light Properties Presentation
21.04.2020.
@misc{Geissdoerfer_2020a,
title = {Demo: Bootstrapping Batteryless Networks Using Fluorescent Light Properties},
author = {Kai Geissdoerfer and Friedrich Schmidt and Brano Kusy and Marco Zimmerling},
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Sandhu, Muhammad Moid; Geissdoerfer, Kai; Khalifa, Sara; Jurdak, Raja; Portmann, Marius; Kusy, Brano
Towards Optimal Kinetic Energy Harvesting for the Batteryless IoT Workshop
Proceedings of the 18th IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2020.
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title = {Towards Optimal Kinetic Energy Harvesting for the Batteryless IoT},
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Sandhu, Muhammad Moid; Geissdoerfer, Kai; Khalifa, Sara; Jurdak, Raja; Portmann, Marius; Kusy, Brano
Towards Energy Positive Sensing using Kinetic Energy Harvesters Conference
Proceedings of the 18th IEEE International Conference on Pervasive Computing and Communications (PerCom), 2020.
@conference{Sandhu2020,
title = {Towards Energy Positive Sensing using Kinetic Energy Harvesters},
author = {Muhammad Moid Sandhu and Kai Geissdoerfer and Sara Khalifa and Raja Jurdak and Marius Portmann and Brano Kusy},
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abstract = {Conventional systems for motion context detection rely on batteries to provide the energy required for sampling a motion sensor. Batteries, however, have limited capacity and, once depleted, have to be replaced or recharged. Kinetic Energy Harvesting (KEH) allows to convert ambient motion and vibration into usable electricity and can enable batteryless, maintenance free operation of motion sensors. The signal from a KEH transducer correlates with the underlying motion and may thus directly be used for context detection, saving space, cost and energy by omitting the accelerometer. Previous work uses the open circuit or the capacitor voltage for sensing without using the harvested energy to power a load. In this paper, we propose to use other sensing points in the KEH circuit that offer information-rich sensing signals while the energy from the harvester is used to power a load. We systematically analyze multiple sensing signals available in different KEH architectures and compare their performance in a transport mode detection case study. To this end, we develop four hardware prototypes, conduct an extensive measurement campaign and use the data to train and evaluate different classifiers. We show that sensing the harvesting current signal from a transducer can be energy positive, delivering up to ten times as much power as it consumes for signal acquisition, while offering comparable detection accuracy to the accelerometer signal for most of the considered transport modes.},
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Conventional systems for motion context detection rely on batteries to provide the energy required for sampling a motion sensor. Batteries, however, have limited capacity and, once depleted, have to be replaced or recharged. Kinetic Energy Harvesting (KEH) allows to convert ambient motion and vibration into usable electricity and can enable batteryless, maintenance free operation of motion sensors. The signal from a KEH transducer correlates with the underlying motion and may thus directly be used for context detection, saving space, cost and energy by omitting the accelerometer. Previous work uses the open circuit or the capacitor voltage for sensing without using the harvested energy to power a load. In this paper, we propose to use other sensing points in the KEH circuit that offer information-rich sensing signals while the energy from the harvester is used to power a load. We systematically analyze multiple sensing signals available in different KEH architectures and compare their performance in a transport mode detection case study. To this end, we develop four hardware prototypes, conduct an extensive measurement campaign and use the data to train and evaluate different classifiers. We show that sensing the harvesting current signal from a transducer can be energy positive, delivering up to ten times as much power as it consumes for signal acquisition, while offering comparable detection accuracy to the accelerometer signal for most of the considered transport modes.
2019
Geissdoerfer, Kai; Chwalisz, Mikolaj; Zimmerling, Marco
Detailed Recording and Emulation of Spatio-temporal Energy Environments with Shepherd Presentation
New York (NY, USA), 13.11.2019.
@misc{Geissdoerfer2019b,
title = {Detailed Recording and Emulation of Spatio-temporal Energy Environments with Shepherd},
author = {Kai Geissdoerfer and Mikolaj Chwalisz and Marco Zimmerling},
url = {http://localhost:8081/wp-content/uploads/2019/11/geissdoerfer19shepherd2.pdf, Paper},
year = {2019},
date = {2019-11-13},
address = {New York (NY, USA)},
organization = { In Proceedings of the 17th ACM Conference on Embedded Networked Sensor Systems (SenSys)},
abstract = {Collaboration of batteryless nodes is essential to their success inreplacing traditional battery-based systems. This abstract describesa demonstration of the recently proposedShepherdtestbed thatallows to record and reproduce spatio-temporal characteristics ofreal energy environments. It consists of a number of spatially dis-tributedShepherdnodes that are tightly time-synchronized witheach other and record synchronized energy traces with a resolutionof3μAand50μVat a rate of100 kHz. Additionally,Shepherdcanfaithfully replay these traces to any number of nodes to study theirbehavior, both individually and as an ensemble.Shepherdworkswith various sources of energy harvesting, such as kinetic or solar,is based on a modular design and provides a generic interface forsensor nodes allowing users to experiment with new platforms.},
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Collaboration of batteryless nodes is essential to their success inreplacing traditional battery-based systems. This abstract describesa demonstration of the recently proposedShepherdtestbed thatallows to record and reproduce spatio-temporal characteristics ofreal energy environments. It consists of a number of spatially dis-tributedShepherdnodes that are tightly time-synchronized witheach other and record synchronized energy traces with a resolutionof3μAand50μVat a rate of100 kHz. Additionally,Shepherdcanfaithfully replay these traces to any number of nodes to study theirbehavior, both individually and as an ensemble.Shepherdworkswith various sources of energy harvesting, such as kinetic or solar,is based on a modular design and provides a generic interface forsensor nodes allowing users to experiment with new platforms.
Geissdoerfer, Kai; Chwalisz, Mikolaj; Zimmerling, Marco
Shepherd: A Portable Testbed for the Batteryless IoT Conference
Proceedings of the 17th ACM Conference on Embedded Networked Sensor Systems (SenSys), 2019.
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Sommer, Philipp; Geissdoerfer, Kai; Jurdak, R; Kusy, B; Liu, J; Zhao, K; Mckeown, A; Westcott, D
Energy- and Mobility-Aware Scheduling for Perpetual Trajectory Tracking Journal Article
In: IEEE Transactions on Mobile Computing, pp. 1-1, 2019, ISSN: 2161-9875.
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title = {Energy- and Mobility-Aware Scheduling for Perpetual Trajectory Tracking},
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Chwalisz, Mikołaj; Geissdoerfer, Kai; Wolisz, Adam
Walker: DevOps Inspired Workflow for Experimentation Workshop
IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops 2019.
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Geissdoerfer, Kai; Jurdak, Raja; Kusy, Brano; Zimmerling, Marco
Getting More Out of Energy-harvesting Systems: Energy Management under Time-varying Utility with PreAcT Conference
Proceedings of the 18th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), 2019.
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2018
Geissdoerfer, Kai; Jurdak, Raja; Kusy, Brano
Long-term Energy-neutral Operation of Solar Energy-harvesting Sensor Nodes under Time-varying Utility Presentation
11.04.2018.
@misc{Geissdoerfer2018,
title = {Long-term Energy-neutral Operation of Solar Energy-harvesting Sensor Nodes under Time-varying Utility},
author = {Kai Geissdoerfer and Raja Jurdak and Brano Kusy},
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2017
Pflugradt, Maik; Geissdoerfer, Kai; Goernig, Matthias; Orglmeister, Reinhold
A fast multimodal ectopic beat detection method applied for blood pressure estimation based on pulse wave velocity measurements in wearable sensors Journal Article
In: Sensors (Basel), 2017.
@article{Pflugradt2017,
title = {A fast multimodal ectopic beat detection method applied for blood pressure estimation based on pulse wave velocity measurements in wearable sensors},
author = {Maik Pflugradt and Kai Geissdoerfer and Matthias Goernig and Reinhold Orglmeister},
doi = {10.3390/s17010158},
year = {2017},
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