Andreas Soleiman

I am currently a research assistant at Uppsala University where I work on low-power mobile networking and visible light sensing systems. I have a Masters' degree in Engineering Physics from Uppsala University, which is oriented towards embedded systems and machine learning.

I am interested in developing sustainable networked embedded systems to allow for future large-scale deployment of IoT devices. My current work is focused on designing small and low-power sensors from the bottom-up. This includes hardware design, signal processing, and the usage of machine learning to infer physical activity from sensor data.

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Awards and Honors
  • Selected for the Cornell, Maryland, Max Planck Pre-Doctoral Research School (2018)
  • Best Demo Award, ACM WiSec (2018)
  • Best Paper Award, ACM VLCS, held in conjunction with ACM MobiCom (2017)
  • Winner of the Student Research Competition at ACM MobiCom (2017)
Hardware Designs

Battery-free Visible Light Sensing
Ambuj Varshney, Andreas Soleiman, Luca Mottola, Thiemo Voigt,
ACM VLCS (Co-located with ACM MobiCom) 2017, Utah, USA, 2018.
Best paper award

We present the design of the first Visible Light Sensing (VLS) system that consumes only tens of microwatts of power to sense and communicate. Unlike most existing VLS systems, we require no modification to the existing light infrastructure since we useunmodulated light as a sensing medium. We achieve this by designing a novel mechanism that uses solar cells to achieve a sub-microwatt power consumption for sensing. Further, we devise an ultra-low power transmission mechanism that backscatters sensor readings and avoids the processing and computational overhead of existing sensor systems. Our initial results show the ability to detect and transmit hand gestures or presence of people up to distances of 330 meter, at a peak power of 20 microwatts. Further, we demonstrate that our system can operate in diverse light conditions (100 lx to 80 klx) where existing VLS designs fail due to saturation of the transimpedance amplifier (TIA).

MobiCom: G: Battery-free Visible Light Sensing
Andreas Soleiman
ACM Student Research Competition (SRC) at ACM MobiCom 2017, Snowbird, Utah.
Winner of ACM Student Research Competition

We present the first visible light sensing system that can sense and communicate shadow events while only consuming tens of microwatts of power. Our system requires no modification to the existing lighting infrastructure and can use unmodulated ambient light as a sensing medium. We achieve this by designing a sensing mechanism that utilizes solar cells, and an ultra-low power backscatter based transmission mechanism we call Scatterlight, which can communicate sensor readings without the use of any energy-expensive computational block. Our results demonstrate the ability to sense and communicate various hand gestures at peak power consumption of tens of microwatts at the sensor, which represents orders of magnitude improvement over the state-of-the-art.

Demo: Towards Battery-free Radio Tomographic Imaging
Abdullah Hylamia, Ambuj Varshney, Andreas Soleiman, Panagiotis Papadimitratos, Christian Rohner, Thiemo Voigt
In Proceedings of the 11th ACM Conference on Security and Privacy in Wireless and Mobile Networks (ACM WiSec 2018), Stockholm, Sweden.
Best demo award

Radio Tomographic Imaging (RTI) enables novel radio frequency (RF) sensing applications such as intrusion detection systems by observing variations in radio links caused by human actions. RTI applications are, however, severely limited by the requirement to retrofit existing infrastructure with energy-expensive sensors. In this demonstration, we present our ongoing efforts to develop the first battery-free RTI system that operates on minuscule amounts of energy harvested from the ambient environment. Our system eliminates the energy-expensive components employed on state-of-the-art RTI systems achieving two orders of magnitude lower power consumption. Battery-free operation enables a sustainable deployment, as RTI sensors could be deployed for long periods of time with little maintenance effort. Our demonstration showcases an intrusion detection scenario enabled by our system.