Autonomous Adaptive Sensing

Autonomous Adaptive Sensing

Autonomous Adaptive Sensing is a inter-disciplinary research collaboration at the  Faculty of Information Technology and Electrical Engineering, with researchers from the  Department of Information Security and Communication Technology, the Department of Electronic Systems and the Department of Mathematical Sciences.  The research group is connected to the NTNU Internet of Things Lab.

Sensor Test Bed

ART Project

Project ART - Autonomous Resource-Constrained Things

Many applications within the Internet of Things will depend on data delivered by myriads of sensors. Examples are monitoring emissions within a city on a detailed level or the usage of the facilities within a building or production plant. This poses a number of challenges during the deployment of such applications, driven by the following issues:

  • Sensor devices require optimized operations that adapt to their environment to use their energy efficiently.
  • Environments are non-stationary and heterogeneous.
  • Manual optimization is impossible due to the system scale and the unknown deployment conditions.

Instead, sensor devices should learn autonomously about their environment on their own and optimize over time. This optimization also includes adaptive sensing, which means that sensor devices only forward valuable information. In the ART project, we work on approaches that allow sensor devices to benefit from machine-learning despite their computational constraints. This enables cost-effective installation and operation of sensing applications in a wide variety of use cases. 

Project Start: April 1st 2017
Funding: Norwegian Research Council



PhD Fellowship within Statistical Learning

PhD Research Fellowship Position

Our research group was granted one of the PhD fellowships at NTNU for ICT as enabling technologies. The research area will be within statistical learning for autonomous resource constrained sensors. If you qualify for the position, you can apply until April 18th 2017.

Application Form for the PhD Fellowship