The integration of the Internet of Things (IoT) and artificial intelligence (AI) is critical to the advancement of ambient intelligence (AmI), as it enables systems to understand contextual information and react accordingly. While many solutions focus on user-centric services that provide enhanced comfort and support, few expand on scenarios in which multiple users are present simultaneously, leaving a significant gap in service provisioning. To address this problem, this paper presents a multi-agent system in which software agents, aware of context, advocate for their users’ preferences and negotiate service settings to achieve solutions that satisfy everyone, taking into account users’ flexibility. The proposed negotiation algorithm is illustrated through a smart lighting use case, and the results are analyzed in terms of the concrete preferences defined by the user and the selected settings resulting from the negotiation in regard to user flexibility.

Precision agriculture uses new technologies to improve crop yields and increase profitability for farmers while reducing the inputs required to grow crops, such as land, water, fertilizers, pesticides, etc. Environmental microclimate data (e.g., air and soil temperature or moisture) are needed as inputs to precision agriculture applications so that adequate decisions and agrotechnical measures can be applied in the fields. Most of the existing precision agriculture solutions for environmental and crop monitoring use locally deployed sensors as the main data source. Since the deployment and maintenance of physical sensors in the fields potentially involves significant costs and human effort, open-access data sources may be an effective complement to environmental data from deployed sensors, but the question remains whether open-access data sources are comparable to locally deployed sensors in terms of accuracy. This paper analyzes the correlation between open environmental data sources provided by the Copernicus ERA5-Land and Agri4Cast data sets, and data collected by locally deployed sensors to determine the extent to which open data sources can be used in precision agriculture.