Developing innovative systems and operations to monitor forests and send alerts in
dangerous situations, such as fires, has become, over the years, a necessary task to protect forests.
In this work, a Wireless Sensor Network (WSN) is employed for forest data acquisition to identify
abrupt anomalies when a fire ignition starts. Even though a low-power LoRaWAN network is
used, each module still needs to save power as much as possible to avoid periodic maintenance
since a current consumption peak happens while sending messages. Moreover, considering the
LoRaWAN characteristics, each module should use the bandwidth only when essential. Therefore,
four algorithms were tested and calibrated along real and monitored events of a wildfire. The first
algorithm is based on the Exponential Smoothing method, Moving Averages techniques are used
to define the other two algorithms, and the fourth uses the Least Mean Square. When properly
combined, the algorithms can perform a pre-filtering data acquisition before each module uses the
LoRaWAN network and, consequently, save energy if there is no necessity to send data. After the
validations, using Wildfire Simulation Events (WSE), the developed filter achieves an accuracy rate
of 0.73 with 0.5 possible false alerts. These rates do not represent a final warning to firefighters, and a
possible improvement can be achieved through cloud-based server algorithms. By comparing the
current consumption before and after the proposed implementation, the modules can save almost
53% of their batteries when is no demand to send data. At the same time, the modules can maintain
the server informed with a minimum interval of 15 min and recognize abrupt changes in 60 s when
fire ignition appears.
This work has been supported by SAFe Project through PROMOVE—Fundação La Caixa.
The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and
UIDP/05757/2020) and SusTEC (LA/P/0007/2021). Thadeu Brito is supported by FCT PhD Grant
Reference SFRH/BD/08598/2020, and Beatriz Flamia Azevedo is supported by FCT PhD Grant
Reference SFRH/BD/07427/2021
