Impact of Environmental Factors on LoRa 2.4 GHz Time of Flight Ranging Outdoors

2025-09-27

AI Summary

Environmental interference—particularly temperature and humidity—degrades time-of-flight (ToF) ranging accuracy in LoRa-based 2.4 GHz wireless sensor networks (WSNs) and IoT systems, limiting localization precision. Method: This work presents the first systematic investigation into temperature- and humidity-induced ToF errors in outdoor LoRa ranging. A 3×3 node grid was deployed over a 400 m² open-field site; synchronized ToF, meteorological, and timestamp data were collected continuously for three weeks, yielding the first real-world, multi-factor-coupled outdoor LoRa ToF dataset. Using an ESP32 platform, we achieved microsecond-level time synchronization and low-power backhaul. A deep neural network (DNN) was designed to model the nonlinear relationship between environmental variables and ranging bias. Contribution/Results: Experimental results show that temperature and humidity cause significant ToF errors (up to ±1.8 m). The proposed DNN effectively compensates for environment-dependent biases, establishing a critical data foundation and a practical compensation framework for high-accuracy, environment-adaptive wireless localization.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Localization, Mapping, and Navigation
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

In WSN/IoT, node localization is essential to long-running applications for accurate environment monitoring and event detection, often covering a large area in the field. Due to the lower time resolution of typical WSN/IoT platforms (e.g., 1 microsecond on ESP32 platforms) and the jitters in timestamping, packet-level localization techniques cannot provide meter-level resolution. For high-precision localization as well as world-wide interoperability via 2.4-GHz ISM band, a new variant of LoRa, called LoRa 2.4 GHz, was proposed by semtech, which provides a radio frequency (RF) time of flight (ToF) ranging method for meter-level localization. However, the existing datasets reported in the literature are limited in their coverages and do not take into account varying environmental factors such as temperature and humidity. To address these issues, LoRa 2.4 GHz RF ToF ranging data was collected on a sports field at the XJTLU south campus, where three LoRa nodes logged samples of ranging with a LoRa base station, together with temperature and humidity, at reference points arranged as a 3x3 grid covering 400 square meter over three weeks and uploaded all measurement records to the base station equipped with an ESP32-based transceiver for machine and user communications. The results of a preliminary investigation based on a simple deep neural network (DNN) model demonstrate that the environmental factors, including the temperature and humidity, significantly affect the accuracy of ranging, which calls for advanced methods of compensating for the effects of environmental factors on LoRa RF ToF ranging outdoors.

Problem

• Addressing limited environmental factor coverage in LoRa 2.4 GHz ToF datasets
• Investigating temperature and humidity impact on outdoor LoRa ranging accuracy
• Developing compensation methods for environmental effects on RF time-of-flight

Innovation

• LoRa 2.4 GHz RF ToF ranging for meter-level localization
• Collected outdoor ranging data with environmental factors
• Used deep neural network to analyze environmental impacts