Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and in a timely manner, as AI models highly depend on the underlying data used to justify the predictions. Unfortunately, in urban contexts, the hyper-locality of air quality, varying from street to street, makes it difficult to monitor using high-end sensors, as the cost of the amount of sensors needed for such local measurements is too high. In addition, development of pollution dispersion models is challenging. The deployment of a low-cost sensor network allows a more dense cover of a region but at the cost of noisier sensing. This paper describes the development and deployment of a low-cost sensor network, discussing its challenges and applications, and is highly motivated by talks with the local municipality and the exploration of new technologies to improve air quality related services. However, before using data from these sources, calibration procedures are needed to ensure that the quality of the data is at a good level. We describe our steps towards developing calibration models and how they benefit the applications identified as important in the talks with the municipality.

Contactless communication technology in mobile phones (e.g., near field communication) has a potential to simplify our everyday life by enabling services like mobile payment, ticketing and information sharing services. The recommendations in this paper are based on a comparative case study of previous mobile service ecosystems used in Norway. The findings of this study add insights into key success factors for the mobile network operator during the different business ecosystem evolutionary stages. The recommendations include the fact that mobile network operators can succeed in kick-starting contactless mobile payment services by taking a role as a trusted service manager, focusing on establishing the ecosystem and contactless payment service together with partners in the bank sector. Furthermore, in an expansion phase the mobile network operator must open up for collaboration and connect with a portfolio of aggregators, merchants and third party niche players offering secure and high quality services and applications.

Temporal drift of low-cost sensors is crucial for the applicability of wireless sensor networks (WSN) to measure highly local phenomenon such as air quality. 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 under-researched in the context of blind WSN calibration, particularly in scenarios with networks that mix static and mobile sensors. In this paper we investigate the use of DL architectures for such scenarios, including the effects of weather in both drifting and sensor measurement. New models are proposed and compared against a baseline, based on a previous proposed model and extended to include mobile sensors and weather data. Also, a procedure for generating simulated air quality data is presented, including the emission, dispersion and measurement of the two most common particulate matter pollutants: PM 2.5 and PM 10 . Results show that our models reduce the calibration error with an order of magnitude compared to the baseline, showing that DL is a suitable method for WSN calibration and that these networks can be remotely calibrated with minimal cost for the deployer.