the paper briefly describes the specifics of precision agriculture from the point of view of data needs and the possibility of providing such data. It points to the most convenient and efficient form of collecting the necessary data using drones. Specific drone characteristics are recorded/discussed in order to facilitate selecting the right one according to the farmers' heterogeneous requirements regarding the data collection on their crops. Selecting the appropriate drone for the specific needs of farmers is carried out by a multi-criteria decision-making software.

The goal of the paper is to give an overview of the most relevant aspects of mobile crowdsensing that are already utilized by the society. The paper focuses on best practices applied in smart cities today, how these applications can be motivated (incentives), and how they rely on technology enablers of today's vertical silos and future's horizontal approaches. We introduce a path for transforming the vertical silos of today containing separated solutions in various domains into a horizontal, unified ecosystem, giving a way to novel technology and business opportunities.

Energy efficiency is one of leading design principles for the current deployment of cellular mobile networks. A first driving reason for this is that half of the operating costs for the network providers comes from the energy spent to power the network, with almost 80% of it being consumed at the base stations. A second reason is related to the high environmental pollution, which makes the green cellular networks deployment mandatory. Cooperation between mobile network providers can be an effective way to reduce the CO2 emissions and, simultaneously, reduce the operating expenditures. In this paper, a game theoretic approach is proposed to introduce fairness and stability into an optimal algorithm for switching off the cooperating base stations. This aims at making such a solution more attractive in real implementation scenarios where profit-driven network providers act as rational players.

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Personal mobile devices are widespread and carried by their users most of the time over the day. Thanks to the integrated sensors they can report about visited places, movement types and speed of the users. However, efficient stopping event detection on public transport vehicles is still a challenge. These events, associated with the coordinates of real stations, can be useful to update public transit timetables according to real-time traffic. In field tests we evaluated the most commonly available suitable sensors' precision and efficiency and developed our Stopping Event Detection Algorithm (SEDA), which utilizes only the accelerometer to find potential stopping times and the Wi-Fi sensor to validate or discard them by a novel localization method. Wi-Fi is used only 6.66% of the time of actual traveling on public vehicles. Our algorithm is shown to recognize properly 82.9-89.47% of public traffic stations while consuming daily only 13% the capacity of an average smartphone's battery.