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Efficient Frequency Planning for LoRaWAN networks with urgent traffic

LoRaWAN is one of the most promising standards for IoT applications. Slotted ALOHA has been proposed as a LoRaWAN MAC protocol, to achieve performance improvement. This paper explores the effect of using clusters in a multichannel Slotted ALOHA-based LoRaWAN network, for event detection, like forest fires, where a small part of the network nodes, at the pace of the event, produce data at a high rate. It proposes a new frequency planning for such environments, that aims to reduce the number of collisions, and consequently to reduce the mean packet delay, as the data is important to be transmitted as fast as possible. Extensive simulation results indicate a promising performance improvement in terms of throughput and mean packet delay, compared to a random frequency channel assignment to the network nodes.