Connectivity Analysis of LoRaWAN-Based Non-Terrestrial Networks for Subterranean mMTC

TL;DR

This paper investigates the feasibility of integrating underground sensors with non-terrestrial networks like UAVs, HAPs, and satellites to improve underground monitoring, using a Monte Carlo simulation with various models and modulation schemes.

Contribution

It develops a comprehensive simulation framework to evaluate underground-to-NTN connectivity, considering multiple environmental and technical factors, and identifies suitable LoRa modulation schemes for different NTN platforms.

Findings

LoRa SF7 is effective for UAV short-range communication in rural areas.

LR-FHSS is promising for HAP and LEO satellite platforms in large WUSNs.

Connectivity success is influenced by environment, device count, burial depth, and soil water content.

Abstract

Wireless underground sensor networks (WUSNs) offer significant social and economic benefits by enabling the monitoring of subterranean entities. However, the communication reliability of WUSNs diminishes in harsh environments where terrestrial network infrastructure is either unavailable or unreliable. To address this challenge, we explore the feasibility of integrating buried massive machine-type communication (mMTC) sensors with non-terrestrial networks (NTNs), including unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), and low Earth orbit (LEO) satellites, to establish underground-to-NTN connectivity for various large-scale underground monitoring applications. To assess the effectiveness of underground-to-NTN connectivity, we develop a Monte Carlo simulator that incorporates a multi-layer underground attenuation model, the 3GPP empirical path loss model for various NTN platforms, and two LoRaWAN modulation schemes, i.e., LoRa and LoRa-frequency hopping spread spectrum (LR-FHSS). Our results evidence that LoRa SF7 is a strong candidate for short-range UAV communication in rural environments, while LR-FHSS modulation proves to be a promising option for HAP and LEO satellite platforms in massive WUSNs scenarios thanks to its adequate link budget and robustness to the interference. Finally, we demonstrate that the success probability of underground-to-NTN connectivity using LoRa and LR-FHSS is significantly affected by factors such as the monitoring environment, the number of devices, burial depth, and the soil's volumetric water content.