Towards a cognitive MAC layer: Predicting the MAC-level performance in   Dynamic WSN using Machine learning
  Predictable network performance is key in many low-power wireless sensor network applications. In this paper, we use machine learning as an effective technique for real-time characterization of the communication performance as observed by the MAC layer. Our approach is data-driven and consists of three steps: extensive experiments for data collection, offline modeling and trace-driven performance evaluation. From our experiments and analysis, we find that a neural networks prediction model shows best performance. 
Merima Kulin, Eli de Poorter, Tarik Kazaz, Ingrid Moerman
cs.NI