An Efficient and Scalable Reinforcement Learning-Driven Intelligent Resource Management and Secure Framework for LoRaWAN

This study proposes a Q-learning-based adaptive duty cycle scheduling algorithm for LoRaWAN in a smart city eco-system to enhance the energy efficiency, reduce transmission delay, and handle dynamic traffic conditions. Additionally, it also incorporates an intelligent and efficient channel utilization scheme for LoRaWAN-enabled IoT networks and also integrates a lightweight security strategy at the edge (gateways), making it suitable for low-power, low-computation LoRaWAN environments. In this adaptive and intelligent LoRaWAN framework Q-learning agent dynamically selects various transmission actions based on the contextual states, including buffer size, energy levels, and channel conditions, which optimizes energy efficiency and also enhances the reliability of data transmission in LoRaWAN. The light-weight intrusion detection mechanism also filters suspicious packets using trust scores and payload analysis to ensure secure data delivery and adaptive, scalable, and proactive protection against several prevalent threats in LoRaWAN-driven IoT. It also incorporates a channel-aware scheduling to avoid congestion and improve overall transmission performance. Experimental outcome further confirms improvement over throughput, delay, bandwidth utilization, energy conservation, and resilience against malicious or faulty transmissions, demonstrating the framework’s ability to optimize the resource allocation performance while balancing the above metrics adaptively.