Interference Aware Self-Organization for Wireless Sensor Networks: a Reinforcement Learning Approach

Reliability is a key issue in wireless sensor networks. Depending on the targeted application, reliability is achieved by establishing and maintaining a certain number of network functionalities: the greatest among those is certainly the capability of nodes to communicate. Sensors communications are sensible to interference that might corrupt packets transmission and even preclude the process of network formation. In this paper we propose a new scheme that allows to establish and maintain a connected topology while dealing with this problem. The idea of channel surfing (already introduced in [1]) is exploited to avoid interference; in the resulting multi-channel environment nodes discover their neighbors in a distributed fashion using a reinforcement learning (RL) algorithm. Our scheme allows the process of network formation even in presence of interference, overcoming thus the limit of algorithms currently implemented in state of the art standards for wireless sensor networks. By means of reinforcement learning the process of neighbor discovery is carried out in a fast and energy efficient way.