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Self-learning and adaptive scheme for Supporting periodic Multi-flows in Wireless Sensor Networks

In this research report we propose a novel decentralized and self-learning framework to support both communication reliability and energy-efficiency for periodic traffic applications in WSNs. Our autonomous approach comprises there main components: estimation and identification of the flows, asynchronous channel hopping and local dynamic multiple sleep states scheduling. We also propose a light and efficient controller to eliminate the collision caused by multi-flow overlap. We present detailed design, implementation, and evaluation of our autonomous framework using real-life measurements, and realistic trace-based simulation. The results show that our asynchronous channel hopping solution improves the packet reception rate without the need of an expensive signalling and time synchronization overhead. We also show that with this scheme the average energy consumption yields a ≈ 50% lower than the single channel solution. This work is to the best our knowledge, the first to explore channel hopping without maintaining a tight time synchronization protocol.