Burning Questions: A Novel Approach to Wildfire Detection

Table of Contents:

1. From Devastation to Destruction: IoT and LoRaWAN for Wildfire Prevention
2. Internet of Things
3. Edge Computing
4. Applications in Wildfire Detection
5. Traditional Methods
6. My LoRaWAN Model
7. Conclusion
8. Key Highlights

From Devastation to Destruction: IoT and LoRaWAN for Wildfire Prevention

Wildfires have always been an essential part of natural ecosystems, but as of the last few years, wildfires have left a trail of unimaginable destruction. In 2024, wildfires burned 8.9 million acres of land throughout America and destroyed over 1700 structures in California alone. However, in just a single month, 2025 already became the second most destructive fire year in state history through the Southern California wildfires, which destroyed over 16,000 structures (including homes, businesses, and schools) and tragically killed 29 people. The reason for this widespread destruction is mainly climate change which is making droughts longer, and ecosystems drier, providing more fuel for these ferocious flames.

The aftermath of the LA wildfires, now in the top 5 deadliest wildfires in California history. Proves that we must take action to stop these fires and prevent further disasters. Source: https://plaincompassion.org/wildfires-in-los-angeles-ca/

If we are to stop disastrous stories like these from ever repeating, we must take action to stop these wildfires in their tracks. To achieve this, we must alter our mindset from that of reactive wildfire prevention to proactive wildfire detection. Proactively detecting wildfires will allow us to discover these flames while they are still smoldering and provide communities and firefighters plenty of time to prepare or evacuate before disaster strikes. Easily some of our best tools to accurately implement this are the Internet of Things (IoT) and edge computing. Both of these revolutionary technologies have mass potential in proactive wildfire detection, as demonstrated by Dryad Networks and similar companies.

In this article, I will be discussing IoT and edge computing, and especially their applications in wildfire detection; strengths and limitations of existing wildfire detection technologies and the room for IoT and edge computing; before finally elaborating on my own model of LoRaWAN protocol based wildfire detection.

Internet of Things

The Internet of Things (IoT) is a transformative technology that interconnects physical devices, sensors, and software to facilitate seamless data exchange over the internet. By embedding computational intelligence into everyday objects, IoT enables autonomous decision-making, predictive analytics, and real-time monitoring, revolutionizing industries ranging from healthcare and manufacturing to smart cities and environmental conservation. At its core, IoT operates through a distributed network of edge devices, which collect, process, and transmit data to centralized cloud platforms or decentralized edge computing systems. This architecture ensures low-latency communication, optimized energy consumption, and enhanced scalability — essential features for mission-critical applications like wildfire detection, where rapid response times can mean the difference between containment and catastrophe. Today, IoT is famous for its low-power wide-area networks (LPWANs) such as LoRaWAN, NB-IoT, and Sigfox. These communication protocols optimize long-range data transmission while minimizing power consumption, making them ideal for battery-operated sensor networks deployed in remote regions. Industry leaders such as Cisco, IBM, and Bosch have been at the forefront of IoT innovation, developing intelligent infrastructure for industrial automation, smart agriculture, and environmental monitoring. Furthermore, the integration of artificial intelligence (AI) and machine learning with IoT devices has amplified their predictive capabilities, enabling anomaly detection and adaptive responses based on real-time data streams.

IoT is a revolutionary technology that allows devices to communicate with and send data to each other. They decrease the need for human involvement, massively scaling effiiciency which is critical in time sensitive application like wildfire detection. Source: https://www.cloudblue.com/blog/what-is-the-internet-of-things-iot/

Edge Computing

Similarly, Edge computing is a distributed computing mechanism that processes data at or near the source of generation rather than transmitting it to a distant cloud server. This architecture reduces latency, enhances data privacy, and minimizes bandwidth constraints, making it indispensable for time-sensitive applications, like proactive wildfire detection. Traditionally, IoT devices relied heavily on cloud computing for data storage and analytics, but the exponential growth of connected devices and the volume of generated data have rendered this approach inefficient. Edge computing mitigates these challenges by decentralizing computation, enabling devices to preprocess and filter data before sending only relevant insights to the cloud. This shift not only accelerates response times but also reduces the strain on network resources, leading to cost-effective and scalable deployments. Companies such as NVIDIA, Intel, and Microsoft have been pioneers in edge computing innovation, developing hardware accelerators, AI-driven inference engines, and software frameworks that empower intelligent decision-making at the edge.

In Edge Computing, rather than relying on sending all the data to the Cloud for analysis, all the computation can be done on the device collecting the data. Similarly to IoT, this massively increases efficiency, which is critical for time sensitive applications like wildfire detection. Source: https://www.orientsoftware.com/blog/edge-computing-vs-cloud-computing/

Applications in Wildfire Detection

The work being done in wildfire detection by Dryad Networks in Germany is a prime example of how IoT and edge computing work in tandem to enhance early warning systems. Dryad has created a proprietary AI supported system of solar powered sensors that monitor for certain gas concentrations, temperature, humidity, and air pressure and subsequently alert local monitoring stations if the normal data points do not fit accepted patterns, which suggests that there is a potential fire nearby, or a high chance of one. Their system likely uses either Long — Short Term Memory Networks (LSTMs) or Recursive Neural Networks (RNNs). Their system utilizes a custom Long Range Wide Area Network (LoRaWAN) for communication. Instead of transmitting raw sensor data to a cloud-based server for analysis, an edge-computing-enabled microprocessor processes this data on-site, running AI-driven inference models to detect potential fire risks. The sensors only send alerts if the edge computing analysis concludes that there is indeed a wildfire present.

This localized processing reduces latency, allowing wildfire detection systems to react within seconds rather than minutes. Additionally, edge computing ensures resilience in remote and off-grid locations, where internet connectivity may be unreliable. By integrating AI at the edge, these systems can also differentiate between false positives — such as harmless temperature fluctuations — and real fire threats, improving the reliability of alerts.

The LoRa frequency for LoRaWAN has a much longer range and lower bandwidth, among other benfits when compared with other communication protocols. Thus, LoRaWAN is one of the best way of communication when it comes to sending messages over long — distances far away from civilization. Source: https://www.thethingsnetwork.org/docs/lorawan/what-is-lorawan/

Traditional Methods

1. Satellite-based detection offers large-scale monitoring, high altitude perspective, and regular updates, but suffers from delayed response times due to satellite revisit intervals, cloud cover interference, and difficulty detecting fires in their early ignition stages. But, they could be integrated into existing IoT systems by serving as an intermediary between the gateways and network servers to increase the range of LoRaWAN detection methods.
2. Optical camera-based systems use AI to analyze smoke and flame patterns but require clear visibility, making them vulnerable to false positives from fog, dust, or reflected sunlight. Still, they can serve as an additional, independent way of validating sensor predictions received by the Network server, or can be integrated as nodes into the broader LoRaWAN to monitor wildfire growth and movement once a fire is identified.
3. Traditional ground-based weather stations provide useful fire risk assessments by measuring temperature, humidity, and wind patterns, but they have a high cost, require regular maintenance and accessibility, and have limited coverage. These will likely be replaced by environmental based LoRaWAN wildfire sensors which have the same functionality but far greater efficiency and accuracy.
4. Community-reliant approaches, such as human fire lookouts and emergency hotlines, are very cost — efficient, but introduce significant delays and depend on external reporting. However, alerting communities of nearby wildfires is essential in any LoRaWAN system and more applications like this must emerge to reduce the destruction caused by wildfires.

While satellites are a good way of monitoring wildfires that have already started, most aren’t capable of detecting small smoldering wildfires hiding beneath the trees above them. Source: https://en-science.huji.ac.il/tiny-satellites-detecting-and-helping-put-out-wildfires

A camera in the hills overlooking Cloverdale, California — one of seven pilot stations Pano AI has installed in Sonoma, Napa, and Lake counties in California. These cameras are good at locating the exact whereabouts of wildfires but have a sizable room for error and false positives. Source: Pano AI.

Bell Knob Tower, originally built in 1931 on Dolly Sods in West Virginia. During World War II, it was part of the area where the U.S. Army trained its soldiers. This tower played a crucial role in detecting wildfires for the army. But, the problems with this tower system are also evident here, as shown by the differences in terrain and the sheer distance that one tower would need to cover. Source: https://abandonedonline.net/location/fire-lookout-towers/

My LoRaWAN Model

Due to the work being done by Dryad Networks, and the urgency of ever worsening wildfires, I was inspired to design my own LoRaWAN protocol system for wildfire detection. At its core, the system uses a combination of environmental sensors, such as the Bosch BME688, to monitor critical parameters like carbon monoxide (CO), temperature, humidity, and other environmental factors indicative of wildfire conditions. Leveraging LoRaWAN’s long-range, low-power communication capabilities, the system allows for real-time data transmission across remote and difficult-to-reach areas, facilitating early detection of wildfires before they escalate into catastrophic events. My network works by the end devices/nodes, in this case the wildfire detection sensors, processing the environmental detection parameters to generate a prediction on whether or not there is a nearby wildfire, before transmitting that prediction to gateways through the LoRa Chirp Spread Spectrum. This information can be sent over multiple miles and because the gateways are not tied to specific sensors, the end antennas can send the signal out in all directions ensuring a gateway receives it. These gateways then relay the data to network servers over a backhaul, like cellular or wifi, which process it, and send it to application servers for analysis. Application servers are responsible for using the prediction for alerts or further actions, such as notifying local fire departments or emergency response teams. In addition to the core network architecture, my LoRaWAN system has a crucial step called the join process. When a device first connects to the network, it performs a join procedure through a join server. This process authenticates the device and generates a unique session key for secure communication. This mechanism ensures that only authorized devices can send data to the network, adding another layer of security to the system.

My own LoRaWAN network design for my early wildfire detection project.

I choose LoRaWAN over other IoT protocols due to the ability to establish private networks with AES encryption, ensuring security and privacy of data and information throughout the whole process; the low latency in communication; and the long distances over which LoRaWAN protocols can operate. LoRaWAN devices are also known for their long battery lives (5–10 years minimum on a single battery), their energy efficiency, the ease of triangulating end nodes, and the ability to establish end nodes that can also be used to stop fires (for example, the application server is alerted of a nearby wildfire so it turns on all the sprinklers in nearby communities in addition to sending alerts).

Conclusion

Overall, the worsening wildfire crisis demands a proactive approach, and LoRaWAN offers a game-changing solution. Unlike traditional methods, which suffer from delays and inefficiencies, my LoRaWAN-based detection system ensures long-range, secure, and real-time wildfire monitoring. By integrating edge computing, IoT innovations, and automated response mechanisms, this system has the potential to revolutionize wildfire prevention. Investing in such technologies is essential to protecting lives, communities, and ecosystems from devastating losses.

Key Highlights

1. Wildfires have intensified due to climate change, with 2025 already becoming the second most destructive fire year in California’s history, signaling the need for proactive detection of wildfires, rather than reactive suppression
2. IoT enables real-time environmental monitoring, while edge computing processes data locally, reducing latency and bandwidth constraints
3. Traditional methods like satellites, optical cameras, ground based weather stations, and community reporting offer their individual benefits, but their weaknesses create a desire for a better, more efficient and accurate, system
4. Strength of LoRaWAN for Wildfire Detection:

• Long-range, low-power, and energy-efficient communication
• Secure data transmission with AES encryption
• Scalable network architecture with authentication via join servers
• Potential for integration with automated fire suppression systems

5. My LoRaWAN System:

• Uses sensors to monitor wildfire indicators like CO levels, temperature, and humidity
• Utilizes LoRa Chirp Spread Spectrum for efficient data transmission
• Implements multi-layered security and triangulation for precise fire location tracking
• Alerts communities and fire fighters of local fires so they can prepare adequately, decreasing the destruction of these infernos