a b s t r a c tWater is an essential resource for the development of agriculture. In several locations like the southeast of Spain water is scarce and its cost is high, so optimal management of this important resource is essential. Therefore, the application of irrigation strategies to improve the watering process, affects the profitability of crops quite significantly. It is necessary to carry out the instrumentation of the variables that affect the growing process of the crop (soil, water and plant) and use the techniques associated with this instrumentation to take actions to optimize the production. The system proposed in this paper uses information and communication technologies, allowing the user to consult and analyze the information obtained by different sensors from any device (computer, mobile phone or tablet) in an easy and comfortable way. The proposed architecture is based on different wireless nodes equipped with GPRS connectivity. Each wireless node is completely autonomous and makes use of solar energy, giving it virtually unlimited autonomy. Different commercial sensors for measuring the wide range of parameters of the soil, plant and atmosphere can be connected to the nodes. The data is sent and processed on a remote server, which stores the information of the sensors in a database, allowing further consultation and analysis of data in a simple and versatile way.
Several research programs are tackling the use of Wireless Sensor Networks (WSN) at specific fields, such as e-Health, e-Inclusion or e-Sport. This is the case of the project “Ambient Intelligence Systems Support for Athletes with Specific Profiles”, which intends to assist athletes in their training. In this paper, the main developments and outcomes from this project are described. The architecture of the system comprises a WSN deployed in the training area which provides communication with athletes’ mobile equipments, performs location tasks, and harvests environmental data (wind speed, temperature, etc.). Athletes are equipped with a monitoring unit which obtains data from their training (pulse, speed, etc.). Besides, a decision engine combines these real-time data together with static information about the training field, and from the athlete, to direct athletes’ training to fulfill some specific goal. A prototype is presented in this work for a cross country running scenario, where the objective is to maintain the heart rate (HR) of the runner in a target range. For each track, the environmental conditions (temperature of the next track), the current athlete condition (HR), and the intrinsic difficulty of the track (slopes) influence the performance of the athlete. The decision engine, implemented by means of (m, s)-splines interpolation, estimates the future HR and selects the best track in each fork of the circuit. This method achieves a success ratio in the order of 80%. Indeed, results demonstrate that if environmental information is not take into account to derive training orders, the success ratio is reduced notably.
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