Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

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

Loading...
Thumbnail Image

Date

Authors

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

Journal Title

Journal ISSN

Volume Title

Publisher

Access Statement

Research Projects

Organizational Units

Journal Issue

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 together with suggestions for creating a robust detection system to combat the growing threat of wildfires worldwide are discussed.

Description

Citation

Source

IEEE Access

Book Title

Entity type

Publication

Access Statement

License Rights

Restricted until