Methodology for Artificial Neural controllers on wireless sensor network

Wireless Sensor Network (WSN) is composed of autonomous devices which collaborate to manage an environment. In this paper, distributed Artificial Neural Network (ANN) learning process methodology is investigated to control such environments. After studying ANN complexity to learn several functioning scenarios, a distributed Neural Voting Procedure is proposed to select the most adapted ANN according to decision-making of all nodes. Finally, the use of several Artificial Neural Controller (ANC) with an arbitration procedure is prefered to the use of one single ANC regarding to execution and deployment costs on the WSN.