Edge AI is leveraging the long-range communication and low power consumption of LoRa® technology to enable real-time (Long Range) data processing and decision-making at the device level. This approach allows the outcome to occur locally, near the data source, rather than relying on centralized systems.
Additionally, the mesh network topology allows the devices to transfer data to each other in a decentralized way, enabling extended coverage even in remote or inaccessible areas.
One of the most representative use cases of the Edge AI and LoRa® Mesh networks is forest fire detection. Early recognition of fire has become a critical factor in mitigating damage to the environment, wildlife, and communities.
The Challenge: Wildfire Detection and Prevention
Wildfires are a growing global threat, and the traditional methods of detecting them, such as human surveillance or satellite imagery, can be slow and inefficient. Wildfires often spread quickly in areas with weak wireless signals, making early detection essential for timely intervention. Furthermore, extensive remote forest areas where these fires are most likely to occur often lack the infrastructure to support traditional communication systems (GSM networks), leading to delayed detection and response.
The Solution: Edge AI and LoRa Mesh Network
( LoRa® Mesh Sensor Network in a rural area for wildfire prevention )
Edge AI uses artificial intelligence (AI) algorithms and models that run locally on IoT sensors, where the data is obtained, instead of relying on a centralized networks. This allows real-time decision-making without the need for constant connectivity.
LoRa® provides long-range communication with minimal energy consumption. A LoRa® mesh network combines the benefits of LoRa® technology with the mesh network architecture, allowing devices to relay data to each other in a decentralized topology, enabling widespread coverage even in remote or inaccessible areas.
The combination of both technologies results on smart sensors that continuously analyze the incoming data (temperature, humidity, air quality, smoke levels, …) to detect patterns indicative of a potential fire.
In the event of a fire, the system can autonomously trigger emergency responses, including sending real-time updates to fire departments, enabling them to take immediate action. The system may even be able to activate fire suppression mechanisms without human intervention.
( LoRa® Mesh Fire sensor, powered by solar energy )
Key Benefits
- Early Detection: By continuously monitoring environmental factors with Edge AI, the system is capable of detecting forest fires in their earliest stages, potentially hours or even days before traditional methods would identify them.
- Reduced Latency: Edge AI ensures real-time processing of sensor data, allowing for immediate decision-making and faster response times, which is critical in preventing the spread of wildfires.
- Energy Efficiency: LoRa’s low-power design makes it ideal for use in remote areas, where maintaining a power supply for traditional networks may be impractical. Sensors can run for months or even years on a single battery, reducing maintenance costs and environmental impact.
- Scalability: LoRa® Mesh Networks can be easily scaled by adding more sensors to cover larger areas. This flexibility makes it possible to expand the system to different regions or environments, such as agricultural fields or national parks.
- Resilience: The decentralized nature of LoRa® Mesh Networks ensures that the system is fault-tolerant. If one sensor goes offline, the mesh can reroute data, ensuring uninterrupted coverage.
In addition to wildfire detection, the same technology can be applied to other applications like smart farming, where sensors can monitor soil moisture, cattle tracking, temperature, and crop health, providing farmers with real-time insights and automated control over irrigation systems, pest detection, and more.
Conclusion
The combination of Edge AI and LoRa® Mesh Networks offers a powerful solution for real-time monitoring, early detection, and autonomous response. By bringing AI processing to the edge and enabling long-range, low-power communication, this technology is helping protect the environment and communities from the devastating impacts of wildfires. As this technology continues to evolve, it holds great promise for a variety of other critical applications, from smart cities to agriculture, demonstrating the vast potential of Edge AI and IoT networks in transforming the way we interact with and protect our world.
Contribution and Role of IMST
As a contributor to the Edge AI project IMST develops a new 2.4 GHz LoRa® Mesh Network system based on existing hardware components and a new mesh protocol stack for gateways and wireless end nodes. On the end-node side the design and implementation will be made with respect to low energy budgets of battery powered end devices. The new 2.4 GHz LoRa® modulation technology and a mesh radio protocol is a promising combination which will deliver a reliable communication layer without the regulatory constraints for duty cycle management as in the 868MHz ISM Band and thus will offer more bandwidth and lower latencies for many applications. The relatively new technology solves some of the problems of sub-gigahertz LoRa® technology such as low bandwidth, duty cycle and the use of a worldwide harmonized frequency band, but also has a smaller range due to the higher frequency. This condition requires a meshed network approach. The protocol stack required for this is a new development, the already existing wireless node and the multipath concentrator are improved with this software. This new communication system on 2.4 GHz enables the wireless connectivity in numerous heterogeneous applications like wild fire detection and prevention. The new 2.4 GHz Lora® Mesh Network can be considered as an improved technology for wireless communications. It will enable other products and business by reliable and energy efficient connectivity as a service layer.
Blog signed by: IMST team