SF-Adaptive Duty-Cycled LoRa Networks: Scalability, Reliability, and Latency Tradeoffs

Abstract

This paper investigates the performance of adaptive LoRa networks with dynamic SF allocation accounting for Duty Cycle (DC) restrictions and quantifying the imperfect orthogonality of Spreading Factor (SF)s. The study presents a novel spatiotemporal model that combines stochastic geometry and queuing theory where LoRa devices are perceived as interacting two-dimensional DTMCs. Each chain jointly tracks the number of packets in the buffer and the node’s protocol state. Numerical simulations are carried out to validate the accuracy of the proposed model. The network performance is studied in terms of Pareto frontiers under different orthogonality assumptions and adaptation settings, showcasing the ranges of sensing applications that LoRa can accommodate without compromising the network stability. The evolution of SFs activity distribution, coverage probability and average latency is examined against different network parameters. The results show that activating SF adaptation with higher cardinality is not always advantageous and evince the existence of an adaptation cardinality that minimises the delay. The study also identifies regimes where SF adaptation is advantageous for the network scalability and reveals ‘SF-Up’ and ‘SF-Down’ rates that maximise the coverage or minimise the delay. Comparing dynamic to static SF allocations, the results highlight a tradeoff between coverage and latency yielding valuable insights into scenarios where either of the allocation strategies would be more beneficial to the network.