AI-Driven Adaptive Data Rate for LoRaWAN Location-Based Services

Abstract

Because a typical adaptive data rate (ADR) of the long-range wide-area network (LoRaWAN) is designed for static services such as smart grid, monitoring, and metering, its performance is limited in mobile environments for location-based services (LBS). Indeed, ADR cannot respond to frequent channel environment changes, thus allocates incorrect data rate (DR) settings to devices, resulting in generation of massive packet loss. To improve mobile support in LoRaWAN location-based services, this paper proposes a robust ADR (RADR) based on an artificial intelligence (AI) approach. The proposed RADR enhances the DR allocation by applying an AI model trained on various channel environments of LBS. Its AI model infers efficient DR, ensuring reliable packet delivery even in mobile scenarios. To verify and demonstrate our AI-driven RADR performance, we built a testbed composed of RADR-embedded LoRaWAN network server/end devices and evaluated their performance in field tests assuming LBS. The results showed a significant improvement in the packet success rate, with the proposed RADR scheme achieving an average increase of 30% compared to the typical ADR.

Keywords

• Adaptive data rate (ADR)
• deep learning
• Internet of Things (IoT)
• location-based services (LBS)
• long range wide area network (LoRaWAN)
• resource allocation