• Information-Driven Adaptive Sensing Based on Deep Reinforcement Learning 

      Murad, Abdulmajid; Kraemer, Frank Alexander; Bach, Kerstin; Taylor, Gavin (Chapter, 2020)
      In order to make better use of deep reinforcement learning in the creation of sensing policies for resource-constrained IoT devices, we present and study a novel reward function based on the Fisher information value. This ...
    • Sampling rate comparison in accelerometer based human activity recognition 

      Castelló Garcia, Daniel (Master thesis, 2019)
      Human activity recognition aims to identify patterns in data generated through human activity. This activity commonly describes movement and can be gathered through a plethora of sensors. Given their low price and ...
    • Using Neural Networks for IoT Power Management 

      Stephansen-Smith, Finn Julius (Master thesis, 2020)
      De fleste enheter i Tingenes Internett (IoT) har begrenset batterilevetid. For å likevel kunne være pålitelige er de nødt til å utnytte batteriet på en så optimal måte som mulig. Dette prosjektet ser på hvorvidt nevrale ...