Optimizing Multi-Gateway LoRaWAN via Cloud-Edge Collaboration and Knowledge Distillation
This addresses network efficiency and reliability issues for IoT applications using LoRaWAN, representing an incremental improvement through hybrid methods.
This study tackled the problem of resource allocation and decision-making in large-scale multi-gateway LoRaWAN networks by proposing HEAT-LDL, a cloud-edge collaborative method based on edge intelligence and knowledge distillation, which improved packet success rate by 20.5% and energy efficiency by 88.1% compared to optimal results from other algorithms.
For large-scale multi-gateway LoRaWAN networks, this study proposes a cloud-edge collaborative resource allocation and decision-making method based on edge intelligence, HEAT-LDL (HEAT-Local Distill Lyapunov), which realizes collaborative decision-making between gateways and terminal nodes. HEAT-LDL combines the Actor-Critic architecture and the Lyapunov optimization method to achieve intelligent downlink control and gateway load balancing. When the signal quality is good, the network server uses the HEAT algorithm to schedule the terminal nodes. To improve the efficiency of autonomous decision-making of terminal nodes, HEAT-LDL performs cloud-edge knowledge distillation on the HEAT teacher model on the terminal node side. When the downlink decision instruction is lost, the terminal node uses the student model and the edge decider based on prior knowledge and local history to make collaborative autonomous decisions. Simulation experiments show that compared with the optimal results of all compared algorithms, HEAT-LDL improves the packet success rate and energy efficiency by 20.5% and 88.1%, respectively.