Unsupervised learning clustering and dynamic transmission scheduling for efficient dense LoRaWAN networks

Mohammed Alenezi, Kok Keong Chai, Atm S. Alam, Yue Chen, Shihab Jimaa

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Long-Range (LoRa) communication technology is considered as a promising connectivity solutions for Internet of Things (IoT) dense applications. In particular, LoRa has drawn the interest due to its low power consumption and wide area coverage. Despite the benefits of LoRaWAN protocol, it still suffers from excessive random and simultaneous transmissions due to the adoption of ALOHA protocol. Therefore, resulting in severe packet collision rate as the network scales up. This leads to continuous retransmission attempts, which in return increase the transmission delay and energy consumption. Thus, this paper proposes a dynamic transmission Priority Scheduling Technique (PST) based on the unsupervised learning clustering algorithm to reduce the packet collision rate and enhance the network's transmission delay and energy consumption. Particularly, the LoRa gateway classifies the nodes into different transmission priority clusters. While the dynamic PST allows the gateway to configure the transmission intervals for the nodes according to the transmission priorities of the corresponding clusters. This work allows scaling up the network densitywhile maintaining low packet collision rate and significantly enhances the transmission delay & the energy consumption. Simulation results show that the proposed work outperforms the typical LoRaWAN and recent clustering & scheduling schemes. Therefore, the proposed work is well suited for dense applications in LoRaWAN.

Original languageBritish English
Article number9229061
Pages (from-to)191495-191509
Number of pages15
JournalIEEE Access
StatePublished - 2020


  • Collision rate
  • Energy consumption
  • IoT
  • LoRa
  • Naive Bayes classifier
  • Packet delivery rate
  • Priority scheduling
  • Transmission delay
  • Unsupervised clustering


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