Smart sensor/actuator node reprogramming in changing environments using a neural network model

Abstract

The techniques currently developed for updating software in sensor nodes located in changing environments require usually the use of reprogramming procedures, which clearly increments the costs in terms of time and energy consumption. This work presents an alternative to the traditional reprogramming approach based on an on-chip learning scheme in order to adapt the node behaviour to the environment conditions. The proposed learning scheme is based on C-Mantec, a novel constructive neural network algorithm especially suitable for microcontroller implementations as it generates very compact size architectures. The Arduino UNO board was selected to implement this learning algorithm as it is a popular, economic and efficient open source single-board microcontroller. C-Mantec has been successfully implemented in a microcontroller board by adapting it in order to overcome the limitations imposed by the limited resources of memory and computing speed of the hardware device. Also, this work brings an in-depth analysis of the solutions adopted to overcome hardware resource limitations in the learning algorithm implementation (e.g.; data type), together with an efficiency assessment of this approach when the algorithm is tested on a set of circuit design benchmark functions. Finally, the utility, efficiency and versatility of the system is tested in three different-nature case studies in which the environmental conditions change its behaviour over time.