LoRa Network Performance Under Ambient Energy Harvesting and Random Transmission Schemes

TL;DR

This paper analyzes how ambient energy harvesting impacts LoRa network performance, providing mathematical models for device operation, interference, and adaptive charging to enhance IoT deployments.

Contribution

It introduces a combined stochastic geometry and Markov analysis framework to model LoRa networks with energy harvesting, including adaptive charging schemes.

Findings

Derived steady-state capacitor voltage distribution

Calculated outage probability due to interference

Proposed adaptive charging time schemes

Abstract

LoRa networks have been deployed all over the world and are a major enabling wireless technology for the Internet of Things (IoT). Massive connectivity applications such as smart metering, agriculture, and supply chain \& logistics are most suitable for LoRa deployments due to their long range, low cost, and low power features. Meanwhile, energy harvesting technologies that extract energy from ambient sources have enabled the battery-less operation of many small wireless sensors. This paper studies the merger of these two technologies and mathematically models device and network performance using tools from stochastic geometry and Markov analysis. To that end, we derive the steady-state distribution of the capacitor voltage, the outage probability due to co-spreading factor interference at the LoRa gateway, and propose adaptive charging time schemes in order to mitigate energy outage events.