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Abstract-This paper is inspired by the project proposal ID 7807232014 submitted for the EU Horizon 2020 topic ICT-11-2017 in April 25, 2017. It aims at applying state-of-the-art ICT technologies, systems and functions such as Cloud/Fog and IoT to enable food supply chain. A new approach will lead to trusted cooperative applications and services within the agro-food chains. Blockchain technologies will enhance the transparency, information flow and management capacity allowing better interactions of farmers with other part of supply chain, especially the consumer. Our research will provide better performing value chains by proposing new food-on-demand business model, based on new Quality of Experience (QoE) food metrics, bridging the gap between subjective experience and objective matrics based on quality standards. Finally, we provided an awareness qustionaire for fresh food products (FFP) and survay for a group of 30 students from the University of Skopje. This study showed that the majority of students are aware and focused just on few common FFP aspects without deeper knowledge of FFP quality.

The effects of air pollution on people, the environment, and the global economy are profound -and often under-recognized. Air pollution is becoming a global problem. Urban areas have dense populations and a high concentration of emission sources: vehicles, buildings, industrial activity, waste, and wastewater. Tackling air pollution is an immediate problem in developing countries, such as North Macedonia, especially in larger urban areas. This paper exploits Recurrent Neural Network (RNN) models with Long Short-Term Memory units to predict the level of PM10 particles in the near future (+3 hours), measured with sensors deployed in different locations in the city of Skopje. Historical air quality measurements data were used to train the models. In order to capture the relation of air pollution and seasonal changes in meteorological conditions, we introduced temperature and humidity data to improve the performance. The accuracy of the models is compared to PM10 concentration forecast using an Autoregressive Integrated Moving Average (ARIMA) model. The obtained results show that specific deep learning models consistently outperform the ARIMA model, particularly when combining meteorological and air pollution historical data. The benefit of the proposed models for reliable predictions of only 0.01 MSE could facilitate preemptive actions to reduce air pollution, such as temporarily shutting main polluters, or issuing warnings so the citizens can go to a safer environment and minimize exposure.

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.