Machine learning (ML) applications in Internet of Things (IoT) scenarios face the issue that labeled data is expen- sive and hard to obtain. In addition, the performance of the trained models usually depends on a specific context: (1) lo- cation, (2) …
Influencing transportation demand can significantly reduce CO2 emissions. Individual user mobility models are key to influencing demand at personal and structural levels. Constructing such models is a challenging task that depends on a number of …
We introduce Tutor4RL, a method to improve reinforcement learning (RL) performance during training, using external knowledge to guide the agents' decisions and experience. Current approaches of RL need extensive experience to de- liver good …
Due to the highly variable execution context in which edge services run, adapting their behavior to the execution context is crucial to comply with their requirements. However, adapting service behavior is a challenging task because it is hard to …
Due to the highly variable execution context in which edge services run,adapting their behavior to the execution context is crucial to comply withtheir requirements. However, adapting service behavior is a challengingtaskbecause it is hard to …
This paper explores the potential of machine learning (ML) systems which use data from in-vehicle sensors as well as external IoT data sources to enhance autonomous driving for efficiency and safety in urban environments. We propose a system which …
Innovations in mobile hardware and software need corresponding advances in the accurate assessment of power consumption under realistic conditions. This isespecially relevant for smartphone-based background sensing applications. Assessing the power …
Edge computing enables new, low-latency services close to data producers and consumers. However, edge service management is challenged by high hardware heterogeneity and missing elasticity capabilities. To address these challenges, this paper …
Radio frequency (RF) signals have been used extensively to enable (indoor) localization and proximity detection based on Received Signal Strength Indication (RSSI). However, localization systems often suffer from large data collection and calibration …