• Machine Learning-based Occupancy Estimation Using Multivariate Sensor Nodes 

      Singh, Adarsh Pal; Jain, Vivek; Chaudhari, Sachin; Kraemer, Frank Alexander; Werner, Stefan; Garg, Vishal (Chapter, 2019)
      In buildings, a large chunk of energy is spent on heating, ventilation and air conditioning systems. One way to optimize their usage is to make them demand-driven depending on human occupancy. This paper focuses on accurately ...
    • Modelling the Energy Consumption of NB-IoT Transmissions 

      Haukland, Johannes (Master thesis, 2019)
      Narrow-Band Internet of Things (NB-IoT) er en voksende Low Power Wide Area Network (LPWAN) kommunikasjonsprotokoll designet for IoT applikasjoner, som har blitt implementert i mange land - inkludert Norge. Avhandlingen gir ...
    • Operationalizing Solar Energy Predictions for Sustainable, Autonomous IoT Device Management 

      Kraemer, Frank Alexander; Palma, David; Bråten, Anders Eivind; Ammar, Doreid (Peer reviewed; Journal article, 2020)
      For sustainable Internet-of-Things (IoT) systems, the solar power prediction is an essential element to optimize performance, allowing devices to schedule energy-intensive tasks in periods with excess energy. In regions ...
    • Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting 

      Murad, Abdulmajid; Kraemer, Frank Alexander; Bach, Kerstin; Taylor, Gavin (Peer reviewed; Journal article, 2021)
      Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. However, despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty ...
    • Properties of Energy Planners for Energy-Harvesting IoT Devices 

      Koltveit, Carl Erik (Master thesis, 2021)
      Monteringen av et lite solcellepanel på kommuniserenede enheter i et utendørs IoT-system, ville gjort det mulig for fjerntliggende sensorenheter å lade energi bufferne selvstendig. Å lade opp tomme buffere uten menneskelig ...
    • Resource-aware Acoustic Event Classification 

      Bjørkli, Marius (Master thesis, 2019)
      Akustisk hendelse klassifisering (AEC) er en kollektiv samlingsbetegnelse for algoritmer som er i stand til å skille mellom ulike lydhendelser. AEC gjør at maskiner kan lære å gjenkjenne akustiske hendelser, og deretter ...
    • Runtime Support for Executable Components with Sessions 

      Bjerke, Marius (Master thesis, 2009)
      Reactive systems that provide services to an environment typically interact with numerous users or other components. Session multiplicity enables a component to keep track of these interactions by handling each of them as ...
    • 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 ...
    • Security of Domestic Products in the IoT 

      Chiem, Henriette Victoria (Master thesis, 2015)
      In the last few years, the idea of connecting existing computing devices through wireless communication has given place to the concept of the Internet of Things (IoT). Many products are already available for the domestic ...
    • Solar Energy Prediction for Constrained IoT Nodes based on Public Weather Forecasts 

      Kraemer, Frank Alexander; Ammar, Doreid; Bråten, Anders Eivind; Tamkittikhun, Nattachart; Palma, David (Chapter, 2017)
      Solar power is important for many scenarios of the Internet of Things (IoT). Resource-constrained devices depend on limited energy budgets to operate without degrading performance. Predicting solar energy is necessary for ...
    • Synthesizing Components with Sessions from Collaboration-Oriented Service Specifications 

      Kraemer, Frank Alexander; Bræk, Rolv; Herrmann, Peter Michael (Journal article; Peer reviewed, 2007)
      A fundamental problem in the area of service engineering is the so-called cross-cutting nature of services, i.e., that service behavior results from a collaboration of partial component behaviors. We present an approach ...
    • Team-Based Learning: A Practical Approach for an Engineering Class 

      Kraemer, Frank Alexander (Journal article, 2017)
      We describe the framework of Team-Based Learning and its application in an engineering course at NTNU. We present the process of applying TBL to a new course, discuss the results on the course outcome and close with some ...
    • Towards Modeling of Data in UML Activities with the SPACE Method: An Example-Driven Discussion 

      Heitmann, Nina (Master thesis, 2008)
      The focus of this work is the rapid engineering method SPACE, developed at NTNU. In this method, services are modeled using UML 2.0 collaborations and activities, and from these executable code can be generated. Services ...
    • Transforming Collaborative Service Specifications into Efficiently Executable State Machines 

      Kraemer, Frank Alexander; Herrmann, Peter (Journal article; Peer reviewed, 2007)
      We describe an algorithm to transform UML 2.0 activities into state machines. The implementation of this algorithm is an integral part of our tool-supported engineering approach for the design of interactive services, in ...
    • Treasure Hunt Components 

      Adrah, Charles Mawutor (Master thesis, 2012)
      The development of distributed, reactive and collaborative services is quite challenging. Rapidly composing services for collaborative learning activities require some development methods and tools. This thesis presents ...
    • Uncertainty-Aware Autonomous Sensing with Deep Reinforcement Learnings 

      Murad, Abdulmajid (Doctoral theses at NTNU;2023:64, Doctoral thesis, 2023)
      The goal of many Internet of Things (IoT) sensing applications, such as environmental monitoring, is to support decision-making by providing valuable information about various phenomena. One approach to achieve this goal ...
    • 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 ...