A Routing Algorithm based on Semi-supervised Learning for Cognitive Radio Sensor Networks

In Cognitive Radio Sensor Networks (CRSNs), the cognitive radio technology enables sensor nodes to occupy licensed bands in a opportunistic manner and provides advantages in terms of spectrum utilization and system throughput. This paper proposes a routing scheme based on semi-supervised learning, which jointly considers energy efficiency, context-awareness, and optimal path configuration to enhance communication efficiency. A context-aware module is developed to collect and learn context information in an energy-efficient way and a new semi-supervised learning algorithm is proposed to estimate dynamic changes in network environment. A novel routing metric is used to select the most reliable and stable path. Our simulation study shows that the proposed routing algorithm enhances the reliability and stability for CRSNs, and at the same time, significantly improves the packet delivery ratio.