TDD LoRa and Delta Encoding in Low-Power Networks of Environmental Sensor Arrays for Temperature and Deformation Monitoring

Densely distributed sensor networks can revolutionize environmental observations by providing real-time data with an unprecedented spatiotemporal resolution. However, field deployments often pose unique challenges in terms of power provisions and wireless connectivity. We present a framework for wirelessly connected distributed sensor arrays for near-surface temperature and/or deformation monitoring. Our research focuses on a novel time division duplex implementation of the LoRa protocol, enabling battery powered base stations and avoiding collisions within the network. In order to minimize transmissions and improve battery life throughout the network, we propose a dedicated delta encoding algorithm that utilizes the spatial and temporal similarity in the acquired data sets. We implemented the developed technologies in a AA battery powered hardware platform that can be used as a wireless data logger or base station, and we conducted an assessment of the power consumption. Without data compression, the projected battery life for a data logger is 4.74 years, and a wireless base stations can last several weeks or months depending on the amount of network traffic. The delta encoding algorithm can further improve this battery life with a factor of up to 3.50. Our results demonstrate the viability of the proposed methods for low-power environmental wireless sensor networks.

Automated summary

Densely distributed sensor networks can greatly improve environmental observations by providing real-time data with high spatiotemporal resolution. This study presents a framework for wirelessly connected distributed sensor arrays for monitoring temperature and/or deformation near the surface. The researchers focused on implementing a novel time division duplex version of the LoRa protocol to enable battery powered base stations and enhance network efficiency. They also proposed a delta encoding algorithm to minimize transmissions and improve battery life. The results demonstrate the feasibility of these methods for low-power environmental wireless sensor networks.