Low-Altitude-Platform-Based Airborne IoT Network (LAP-AIN) for Water Quality Monitoring in Harsh Tropical Environment

Abstract

This article proposes a novel Airborne Internet of Things Network (AIN) system architecture for monitoring water quality, combining existing wireless technologies with the aid of a low altitude platform (LAP) to relay data over long distances in hilly terrain. The proposed system consists of water quality sensors, smart utility network (SUN) devices, long range (LoRa) wireless devices, an LAP air balloon, and Wi-Fi devices. A measurement campaign was conducted to assess the proposed system, focusing on the communication link reliability and the LAP stability and robustness. Several constraints, such as payload limit and safe weather conditions, were also highlighted for operating the LAP with extensive and reliable coverage. On the other hand, characterizing the wireless channel has become a crucial parameter for planning and deploying Internet of Things (IoT) applications. Accordingly, this work proposes a novel hybrid machine learning (ML)-based semi-empirical path loss (PL) model for LoRa wireless communication. The results validated the proposed system’s effectiveness, unique characteristics, and capability to monitor water quality in a harsh environment. Results also revealed a significant difference in packet delivery rate (PDR) for different gateway height and spreading factor (SF) configurations. For instance, switching SF7 to SF12 increased PDR by 28.7%. Meanwhile, increasing gateway height increased PDR by 29.2% for similar SF configurations. The evaluation also revealed that none of the established PL models are suitable to represent harsh tropical environments. Finally, the proposed model achieved nearly 90% prediction accuracy for testing samples and 95% accuracy for training and overall measurement samples, vastly outperforming conventional models.