MARS: Multi-radio Architecture with ML-powered Radio Selection for Mesoscale IoT Applications

Jothi Prasanna Shanmuga Sundaram, Arman Zharmagambetov, Magzhan Gabidolla, Miguel Á. Carreira-Perpiñán, Alberto E. Cerpa

Abstract: IoT is rapidly expanding from traditional small-scale (0–100m) applications like smart homes and large-scale (1–5km) applications like Microsoft’s FarmBeats to emerging mesoscale (0.1–1.5km) applications such as smart-grid NANs and peer-to-peer energy trading in smart homes. These applications demand high throughput and low latency but currently lack dedicated radio technologies. Our qualitative analysis identified Zigbee and LoRa as promising candidates. Further quantitative analysis revealed that a multi-radio architecture combining these radios achieves the best throughput. However, within the 500–1200m range, termed the gray region, it is unpredictable which radio offers higher throughput at any given moment. To address this, we developed MARS, a Multi-radio Architecture with Radio Selection, powered by TAO-optimized decision trees that select the high-throughput radio at the time of transmission. These decision trees require instantaneous path quality estimates, but traditional multi-hop Zigbee networks cannot provide these promptly due to propagation and queuing delays. We overcome this challenge by introducing Decision Tree-based updates to instantaneously estimate end-to-end path quality. Large-scale, real-world experiments with MARS demonstrated average throughput gains of 48.2% and 49.79% at two different locations.

External IDs:dblp:conf/etfa/SundaramZGCC25