CADD: Collaborative adaptive duty cycle and data reduction for congestion control in LoRaWAN

The widespread adoption of Internet of Things (IoT) technologies in smart agriculture, environmental monitoring, and industrial automation has significantly increased the number of connected devices and the volume of data generated. LoRaWAN, originally designed for low-bandwidth and sporadic transmissions, faces challenges in dense deployments because limited channel capacity and uncoordinated ALOHA-based access increase congestion, packet collisions, and energy inefficiency. This paper presents CADD, a channel congestion control mechanism for LoRaWAN networks that combines dynamic data reduction at end devices with adaptive duty cycle regulation based on gateway-side congestion observations and data priority. The method improves use of the communication channel while preserving reliable delivery of critical data. Experiments with realistic datasets in large-scale scenarios with up to 500 devices show up to a 4x higher packet delivery ratio and approximately 54% lower energy consumption compared with a state-of-the-art congestion control scheme and a static duty cycle baseline.