Experimental Comparison of SNR and RSSI for LoRa-ESL Based on Machine Clustering and Arithmetic Distribution

TL;DR

This paper compares SNR and RSSI methods for LoRa-based electric shelf labels, demonstrating that RSSI provides more accurate localization and that arithmetic distribution reduces near-far effects, enhancing network reliability.

Contribution

The study introduces a machine clustering approach with dynamic SF and TP to improve localization and reduce near-far effects in LoRa-ESL systems.

Findings

RSSI outperforms SNR in localization accuracy

Arithmetic distribution minimizes near-far effects

Higher received power at clusters ensures reliable communication

Abstract

LoRa lacks the sensing capabilities of channel status. Received signal strength indicator (RSSI) decreases due to collision, interference, and near-far effect while for signal-to-noise ratio (SNR), the packets are rejected by decreasing the transmission power (TP) at a higher spreading factor (SF). To overcome these challenges in the case of electric shelf label (ESL) to minimize the dependency on retransmission and acknowledgment, the end devices (EDs) are allocated around gateways (GWs) based on machine clustering with dynamic SF for SNR while dynamic TP for RSSI. The experimental results determined that the RSSI approach is more dominant than SNR because of determining the exact locality of the ED that diminished the capture effect. Arithmetic distribution of EDs for various GWs in different clusters helps to minify the near-far effect. The resultant received power (RP) at each cluster is higher for most of the connected EDs than the threshold RP.