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A Survey on IoT Ground Sensing Systems for Early Wildfire Detection: Technologies, Challenges and Opportunities

Chiu Chun Chan, Sheeraz A. Alvi, Xiangyun Zhou, Salman Durrani, Nicholas Wilson, Marta Yebra

Abstract

The threat posed by wildfires or bushfires has become a severe global issue due to the increase in human activities in forested areas and the impact of climate change. Consequently, there is a surge in the development of automatic wildfire detection methods. Approaches based on long-distance imagery from satellites or watchtowers encounter limitations, such as restricted visibility, which results in delayed response times. To address and overcome these challenges, research interest has grown in the implementation of ground-based Internet of Things (IoT) sensing systems for early wildfire detection. However, research on energy consumption, detection latency, and detection accuracy of IoT sensing systems, as well as the performance of various anomaly detection algorithms when evaluated using these metrics, is lacking. Therefore, in this article, we present an overview of current IoT ground sensing systems for early wildfire detection. Camera and environmental sensing technologies suitable for early wildfire detection are discussed, as well as vision-based detection algorithms and detection algorithms for environmental sensing. Challenges related to the development and implementation of IoT ground sensing systems for early wildfire detection and the future research directions important for creating a robust detection system to combat the growing threat of wildfires worldwide are discussed.

A Survey on IoT Ground Sensing Systems for Early Wildfire Detection: Technologies, Challenges and Opportunities

Abstract

The threat posed by wildfires or bushfires has become a severe global issue due to the increase in human activities in forested areas and the impact of climate change. Consequently, there is a surge in the development of automatic wildfire detection methods. Approaches based on long-distance imagery from satellites or watchtowers encounter limitations, such as restricted visibility, which results in delayed response times. To address and overcome these challenges, research interest has grown in the implementation of ground-based Internet of Things (IoT) sensing systems for early wildfire detection. However, research on energy consumption, detection latency, and detection accuracy of IoT sensing systems, as well as the performance of various anomaly detection algorithms when evaluated using these metrics, is lacking. Therefore, in this article, we present an overview of current IoT ground sensing systems for early wildfire detection. Camera and environmental sensing technologies suitable for early wildfire detection are discussed, as well as vision-based detection algorithms and detection algorithms for environmental sensing. Challenges related to the development and implementation of IoT ground sensing systems for early wildfire detection and the future research directions important for creating a robust detection system to combat the growing threat of wildfires worldwide are discussed.
Paper Structure (33 sections, 4 equations, 9 figures, 5 tables)

This paper contains 33 sections, 4 equations, 9 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Impact of Wildfire Across the Globe
  3. Early Wildfire Detection: Existing approaches
  4. Prior Work and Our Contributions
  5. Paper Organisation
  6. IoT Technologies for Wildfire Sensing
  7. Vision-Based Ground Sensing Technologies
  8. Visible Light Range Cameras
  9. Infrared Range Cameras
  10. Ultraviolet Range Cameras
  11. Environmental Monitoring Technologies
  12. Weather Monitoring Sensor
  13. Gas / Smoke Sensors
  14. Data Analytic Algorithms for Wildfire Detection
  15. Vision-Based Detection Algorithm
  16. ...and 18 more sections

Figures (9)

  • Figure 1: Structure of this article about IoT ground sensing systems for early wildfire detection.
  • Figure 2: A hardware prototype of an IoT end device develpoed by the Bushfire Research Centre of Excellence, which includes a MCU, an memory card reader, a gas sensor, a weather monitoring sensor and a LoRaWAN transceiver.
  • Figure 3: Average temperature and relative humidity obtained from eight BME280 sensors in an experimental outdoor burn. The results show that environmental factors have a substantial influence on temperature and weather data recorded by the sensors, overshadowing the impact of proximity to the fire.
  • Figure 4: Measurement of the concentration of CO$_2$ and TVOC over a period of two hours, the dashed lines indicate the ignition time, distinct peaks in CO$_2$ and TVOC are observed shortly after each ignition, and these spikes occur within a period of 10 minutes.
  • Figure 6: Performance comparison of different types of gas/smoke sensors in IoT applications
  • ...and 4 more figures