Anders Eivind Bråten
Background and activities
2016- PhD candidate in telematics, IIK, NTNU
2006-2016 System analyst, interaction designer and concept developer
2002-2006 Cand.Scient in human-computer interaction, IDI, NTNU
2000-2002 Front-end developer
1998-2000 Bachelor in computer engineering, IDI, NTNU (formerly HiST)
1996-1997 Student at the bachelor programme for electrical engineering, IES, NTNU (formerly HiST)
Current research interest:
Autonomous Device Management of Constrained IoT Nodes
I'm studying how constrained IoT devices located in non-stationary environments adapt to changes, by the use of virtual agents, machine-learning and cognitive computing. The goal is to identify and provide mechanisms that these devices can use to adjust their operations autonomously, for instance in order to achieve energy-neutral operation.
Keywords: Cognitive computing, Internet of things, cloud support, autonomous sensor networks, machine learning, device management, energy efficiency.
Scientific, academic and artistic work
Displaying a selection of activities. See all publications in the database
- (2019) Adaptive, Correlation-Based Training Data Selection for IoT Device Management. 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS).
- (2018) Analysis and Visualization of Urban Emission Measurements in Smart Cities. International Conference on Extending Database Technology (EDBT) ; Vienna. 2018-03-26 - 2018-03-29.
- (2018) Towards Cognitive IoT: Autonomous Prediction Model Selection for Solar-Powered Nodes. 2018 IEEE International Congress on Internet of Things (ICIOT).
- (2017) Towards Cognitive Device Management: A Testbed to Explore Autonomy for Constrained IoT Devices. The 7th International Conference on the Internet of Things (IoT 2017) ; Linz. 2017-10-22 - 2017-10-25.
- (2017) Solar Energy Prediction for Constrained IoT Nodes based on Public Weather Forecasts. The 7th International Conference on the Internet of Things - IoT 2017 . ACM; Linz. 2017-10-22 - 2017-10-25.
- (2017) Fog Computing in Healthcare – A Review and Discussion. IEEE Access. vol. 5.
- (2017) Machine Learning in IoT for Autonomous, Adaptive Sensing. ERCIM News. vol. 2017 (110).