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Air pollution is a global problem, especially in urban areas where the population density is very high due to the diverse pollutant sources such as vehicles, industrial plants, buildings, and waste. North Macedonia, as a developing country, has a serious problem with air pollution. The problem is highly present in its capital city, Skopje, where air pollution places it consistently within the top 10 cities in the world during the winter months. In this work, we propose using Recurrent Neural Network (RNN) models with long short-term memory units to predict the level of PM10 particles at 6, 12, and 24 h in the future. We employ historical air quality measurement data from sensors placed at multiple locations in Skopje and meteorological conditions such as temperature and humidity. We compare different deep learning models’ performance to an Auto-regressive Integrated Moving Average (ARIMA) model. The obtained results show that the proposed models consistently outperform the baseline model and can be successfully employed for air pollution prediction. Ultimately, we demonstrate that these models can help decision-makers and local authorities better manage the air pollution consequences by taking proactive measures.

The growing popularity of the Video on Demand service in the Internet Protocol Television environments and the demand for increased quality of the offered videos are becoming a serious threat for the service providers because the high amounts of video traffic are causing congestion in the delivery networks. One of the most acceptable approaches to solve this issue is the peer-assisted streaming, where the peers participate in the streaming process in order to alleviate the load on the streaming servers and in the core of the network. Although the reliability of the Peer-to-Peer service is considerably improved in the managed networks because of the control that the operators have over the clients’ Set-Top Boxes, the failures of the peers still cannot be completely eliminated. The operator can take advantage of the streaming and storage resources of the clients and use them for peer-assisted streaming only while they are watching a video, but not after they finish the streaming session because they may turn off their receiving devices until the next session. In this chapter, we address the issue of the failures of the peers in such environments and their influence on the traffic requested from the servers for providing uninterrupted video experience. For that purpose, we propose a precise mathematical tool for modelling a peer-assisted system for Video on Demand streaming in managed networks with non-cooperative peers, which may decide not to share their resources while they are not active. This tool calculates the performance of the system taking into consideration large variety of system parameters, including the failure probability and the time the peers spend until they decide to turn on the STB and join the network. As the results from the simulations verify the correctness of the mathematical model, we use it to analyse how the failures of the peers are affecting the system’s performance for different system parameters.