Towards a cognitive MAC layer: Predicting the MAC-level performance in Dynamic WSN using Machine learning

Abstract

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.

2 pages, 3 figures, accepted for publication in the EWSN'17 Proceedings of the 2017 International Conference on Embedded Wireless Systems and Networks, Uppsala, Sweden - February 20-22, 2017