Unmanned Aerial Vehicles (UAVs) are autonomous devices employed as data collectors in precision agriculture to support a large number of applications. These UAVs gather data from on-the-ground wireless sensor networks, especially in scenarios that lack any kind of fixed communication infrastructure or where the available infrastructure does not fit the application requirements. Sensors on the ground can store sensing data, and in scenarios that do not require a real-time observation and analysis of the data, like in smart farming, a drone can be used maybe once or two times each day to collect and report the data to a command-and-control center that use them directly, without any other infrastructure (cloud or edges). In this paper, we study analytically how close the drone, that uses a LoRa radio, needs to fly over the sensors to collect data with a given quality of data collection. This can be used to properly spacing the sensor on the field at deployment time, to select among different type of drones, and to properly solve some tradeoff related to field size vs autonomy of the drones and the path used by the latter when collecting the data.

This paper presents an in-depth study of several system partitioning procedures. It is based on the appropriate formulation of a general system model, being therefore independent of either the particular co-design problem or the specific partitioning procedure. The techniques under study are a knowledge-based system and three classical circuit partitioning algorithms (Simulated Annealing, Kernighan&Lin and Hierarchical Clustering). The former has been entirely proposed by the authors in previous works while the later have been properly extended to deal with system level issues. We will show how the way the problem is solved biases the results obtained, regarding both quality and convergence rate. Consequently it is extremely important to choose the most suitable technique for the particular co-design problem that is being confronted.

Outdoor IoT applications usually exploit energy harvesting systems to guarantee virtually uninterrupted operations. However, the use of energy harvesting poses issues concerning the optimization of the utility of the application while guaranteeing energy neutrality of the devices. In this context, we propose a new dynamic programming algorithm for the optimization of the scheduling of the tasks in IoT devices that harvest energy by means of a solar panel. We show that the problem is NP-Hard and that the algorithm finds the optimum solution in a pseudo-polynomial time. Furthermore, we show that the algorithm can be executed with a small overhead on three popular IoT platforms (namely TMote, Raspberry PI and Arduino) and, by simulation, we show the behavior of the algorithm with different settings and at different conditions of energy production.

Software development for smart cities bring into light new concerns, such as how to deal with scalability, heterogeneity (sensors, actuators, high performance computing devices, etc.), geolocation information or privacy issues, among some. Traditional approaches to distributed systems fail to address these challenges, because they were mainly devoted to enterprise IT environments. This paper proposes a middleware framework, called Civitas, specially devoted to support the task of service development for the Smart City paradigm. This paper also analyzes the main drawbacks of traditional approaches to middleware service development and how these are overcome in the middleware framework proposed here.