通过云边协同与知识蒸馏，提升大规模LoRaWAN网络的传输成功率和能效
Optimizing Multi-Gateway LoRaWAN via Cloud-Edge Collaboration and Knowledge Distillation
- 采用云边协同架构，结合演员-评论家与李雅普诺夫优化实现智能下行控制
- 在信号良好时调度终端节点，丢失指令时利用本地模型自主决策
- 相比最优对比算法，包成功率达20.5%提升，能效提高88.1%，适合物联网场景
针对大规模多网关LoRaWAN网络，本文提出基于边缘智能的云边协同资源分配与决策方法HEAT-LDL（HEAT-Local Distill Lyapunov），实现网关与终端节点间的协同决策。HEAT-LDL融合演员-评论家架构与李雅普诺夫优化方法，达成智能下行控制与网关负载均衡。当信号质量良好时，网络服务器使用HEAT算法调度终端节点；为提升终端节点自主决策效率，HEAT-LDL在终端侧对云端的HEAT教师模型进行云边知识蒸馏。当下行决策指令丢失时，终端节点基于学生模型与边缘决策器，结合先验知识与本地历史进行协同自主决策。仿真结果表明，相较于所有对比算法的最优结果，HEAT-LDL在包成功率和能效上分别提升20.5%和88.1%。
原文摘要 · Abstract (English)
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.
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