Although mature industrial wireless sensor network applications increasingly require low-power operations, deterministic communications, and end-to-end reliability, it is very difficult to achieve these goals because of link burstiness and interference. In this paper, we propose a novel link quality estimation mechanism named the burstiness distribution metric, which uses the distribution of burstiness in the links to deal with variations in wireless link quality. First, we estimated the quality of the link at the receiver node by counting the number of consecutive packets lost in each link. Based on that, we created a burstiness distribution list and estimated the number of transmissions. Our simulation in the Cooja simulator from Contiki-NG showed that our proposal can be used in scheduling as an input metric to calculate the number of transmissions in order to achieve a reliability target in industrial wireless sensor networks.
Machine Learning for Wireless Link Quality Estimation: A Survey
