mlsanityData to 14 Oct 2026

Optimizing Multi-Gateway LoRaWAN via Cloud-Edge Collaboration and Knowledge Distillation

arXiv 2504.13194 ↗First posted 13 Apr 2025cs.NI cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 6 references that are in the map. First posted on arXiv 13 Apr 2025.

Builds on

2 of 6 references.

References · 2

  1. 2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 485
  2. 2024Logit Standardization in Knowledge Distillationcited by 102

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025HEAT:History-Enhanced Dual-phase Actor-Critic Algorithm with A Shared Transformer
  2. 2025Stable-MoE: Lyapunov-based Token Routing for Distributed Mixture-of-Experts Training over Edge Networks
  3. 2024The Fusion of Deep Reinforcement Learning and Edge Computing for Real-time Monitoring and Control Optimization in IoT Environments
  4. 2025Energy-Efficient UAV-assisted LoRa Gateways: A Multi-Agent Optimization Approach
  5. 2025Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning
  6. 2025Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks
  7. 2025Splitwise: Collaborative Edge-Cloud Inference for LLMs via Lyapunov-Assisted DRL
  8. 2024Digital Twin-assisted Reinforcement Learning for Resource-aware Microservice Offloading in Edge Computing