Towards Reliable IEEE 802.15.4g SUN with Re-transmission Shaping and Adaptive Modulation Selection

In this paper, we propose and evaluate two mechanisms aimed at improving the communication reliability of IEEE 802.15.4g SUN (Smart Utility Networks) in industrial scenarios: RTS (Re-Transmission Shaping), which uses acknowledgements to track channel conditions and dynamically adapt the number of re-transmissions per packet, and AMS (Adaptive Modulation Selection), which makes use of reinforcement learning based on MAB (Multi-Armed Bandits) to choose the modulation that provides the best reliability for each packet re-transmission. The evaluation of both mechanisms is performed through computer simulations using a dataset obtained from a real-world deployment and two widely used metrics, the PDR (Packet Delivery Ratio) and the RNP (Required Number of Packet transmissions). The PDR measures the ratio between received and transmitted packets, whereas the RNP is the number of packet repetitions before a successful transmission. The results show that both mechanisms allow to increase the communication reliability while not jeopardizing the battery life-time constraints of end devices. For example, when three re-transmissions per packet are allowed, the PDR reaches 98/96% with a RNP of 2.03/1.32 using RTS and AMS, respectively. Additionally, the combination of both proposed mechanisms allows to reach a 99% PDR with a RNP of 1.7, making IEEE 802.15.4g SUN compliant with the stringent data delivery requirements of industrial applications.The project has been partially funded by the Brazilian National Council for Scientific and Technological Development (CNPq 421461/2018-7), the Generalitat de Catalunya (SGR-60-2017 and SGR-891-2017), and the Spanish Ministry of Science, Innovation and Universities (SPOTS RTI2018-095438-A-I00 and SPOT5G TEC2017-87456-P). This project is also co-financed by the European Union Regional Development Fund within the framework of the ERDF Operational Program of Catalonia 2014-2020, within the FeMIoT and Looming Factory projects