Channel List Selection Based on Quality Prediction in WirelessHART Networks

Abstract

WirelessHART is one of the most widely used technologies in industrial wireless networks. However, its performance is highly influenced by the quality of wireless channels. To improve the reliability of wireless communications, WirelessHART employs channel blacklisting and channel hopping mechanisms, which highlights the importance of channel assessment. Traditional methods generally resort to packet reception ratio (PRR) of the previous time slot to assess and allocate channels, but this is not accurate. In this paper, we propose a learning-based framework for predicting the PRR, and on the basis of the predicted PRR, we develop a heuristic channel selection algorithm to confirm the channel list, which takes into account the balance of channel diversity and route diversity. Simulation results demonstrate that our algorithm outperforms existing ones in terms of achieved reliability.