Evaluation of a wireless low-energy mote with fuzzy algorithms and neural networks for remote environmental monitoring

The devices developed for applications in the internet of things have evolved technologically in the improvement of hardware and software components, in the area of optimization of the life time and to increase the capacity to save energy. This paper shows the development of a fuzzy logic algorithm and a power propagation neural network algorithm in a wireless mote (IoT end device). The fuzzy algorithm changes the transmission frequency according to the battery voltage and solar cell voltage. Moreover, the implementation of algorithms based on neural networks, implied a challenge in the evaluation and study of the energy commitment for the implementation of the algorithm, memory space optimization and low energy consumption