| dc.description.abstract | Temporal drift of low-cost sensors is a crucial problem when considering the applicability of wireless sensor networks (WSN). Since they provide highly local measurements, which is key to combat the ever increasing problem of air pollution, calibrating such networks effectively becomes a high priority. The emergence of wireless sensor networks in locations without available reference data makes calibrating such networks without the aid of true values a key area of research. While deep learning (DL) has proved successful on numerous other tasks, it is sorely under-researched in the context of WSN calibration. To further this research, this thesis will explore the applicability of DL for blind WSN calibration by improving upon the only previously existing DL model and explore other possible models. Promising architectures are found by a structured literature search on DL methods in other related fields. To test architectures, a synthetic dataset has been implemented after analysing real sensor data. The new models presented in this thesis obtains a smaller calibration error with an order of magnitude compared to the previous model, with temporal convolutions in 2 dimensions proving most promising. All code used in this thesis is available at: \href{https://github.com/ntnu-ai-lab/dl-wsn-calibration}{https://github.com/ntnu-ai-lab/dl-wsn-calibration}. | |
