Research Output
A New Annulus-based Distribution Algorithm for Scalable IoT-driven LoRa Networks
  Long-Range (LoRa) has been a major avenue for deploying the Internet of Things (IoT) in large scale environments due to its long-scale connection, energy efficiency, and cost-effectiveness. LoRa networks provide multiple configurable transmission parameters that greatly affect the performance of the overall network. To the best of our knowledge, the optimal combination of these parameters that can allow orthogonal simultaneous transmissions to be successfully decoded by the gateway has not been reported in the literature. Exploiting all the transmission parameters in the physical layer to find optimal combinations between them will inevitably increase the throughput of LoRa without affecting the energy consumption. Hence, in this paper, we propose annulus-based distribution algorithm of LoRa transmission parameters to mitigate wellknown and challenging issues, namely the capture effect and the limited scalability. The performance of the proposed algorithm has been compared with the Adaptive Data Rate(ADR) algorithm of LoRaWAN and the simulation results show that the proposed algorithm significantly outperforms the ADR especially in large scale dense networks. Specifically, the proposed algorithm has improved the network throughput by an average of 59% compared to the legacy LoRaWAN.


Alahmadi, H., Bouabdallah, F., & Al-Dubai, A. (2021). A New Annulus-based Distribution Algorithm for Scalable IoT-driven LoRa Networks. In ICC 2021 - IEEE International Conference on Communications



Internet of Things, LP-WAN, LoRa, MAC protocols, SF Allocation, energy efficiency

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