  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.
