The core insight behind the design is that no single radio protocol can do everything a forest monitoring network needs. Dense clusters of sensors near the ground need cheap, energy-sipping, short-range links that can relay data through neighbors, while the aggregated information must also travel hundreds of meters or more back to an internet-connected gateway without draining batteries. The researchers resolved this tension with a two-tier architecture: a Zigbee mesh layer handles dense local collection among near-ground sensor nodes, and a LoRa long-range link carries batched payloads from the mesh coordinator to a Wi-Fi gateway that uploads the data to the cloud.

Each sensing node in the prototype is built around an Arduino Uno Rev3 board paired with a Digi XBee S2C Zigbee module operating in API mode, together with a DHT11 sensor that samples temperature and relative humidity once per second. These two variables matter because near-ground microclimate conditions, particularly the combination of heat and dry air close to surface fuels, are strong indicators of ignition risk. One node acts as the Zigbee coordinator while the others participate in the mesh, so packets can reach the collection point either directly or by hopping through intermediate nodes. The coordinator then buffers incoming records and packs up to four of them into a single LoRa payload, a batching strategy that reduces how much airtime the long-range link consumes and lowers the probability of radio collisions when several nodes report at once.

The long-range tier uses Arduino MKR WAN 1310 boards transmitting at 433 MHz with a spreading factor of 7, a 125 kHz bandwidth, and 17 dBm of output power. At the receiving end, an Arduino MKR WiFi 1010 gateway forwards payloads over an inter-integrated circuit bus and uploads structured JSON records to a cloud database over HTTPS. A web dashboard built with the Next.js framework subscribes to those updates in real time and plots node status and risk levels on an interactive map. What makes the dashboard unusual is that the machine learning happens there too: a Random Forest classifier runs client-side, scoring each incoming reading in under 100 milliseconds on standard hardware, so no separate inference server is needed.

The classifier was trained on the public UCI Forest Fires dataset, using a stratified 80:20 train-test split that yielded 413 training samples and 104 test samples. The researchers tested two feature sets: a full-feature model using fire-weather indices such as FFMC, DMC, DC, and ISI alongside temperature, humidity, wind, and rain, and a sensor-proxy model restricted to the temperature and relative humidity values the field nodes can actually measure. Hyperparameters were fixed for reproducibility, including 200 trees, a maximum depth of 10, and balanced class weights. Because the goal is early warning rather than final alarm adjudication, the operating point was deliberately tuned to favor recall, meaning the system accepts more false alarms in exchange for missing as few genuine fire cases as possible.

The communication measurements tell a nuanced story. In an indoor line-of-sight corridor, Zigbee delivered at least 96 percent of packets at hop distances up to 50 meters and 91.7 percent at 80 meters, with application-level round-trip times below 100 milliseconds at short hops. The LoRa backhaul, tested across open-field line-of-sight distances of 100 to 800 meters, sustained packet delivery of at least 90 percent even at the maximum range, with one-way link latency at or below 384 milliseconds. With five sensing nodes running concurrently, the complete sensor-to-cloud path, spanning Zigbee delivery, coordinator batching, LoRa transmission, gateway handling, and cloud upload, averaged roughly 1.8 seconds of delay, with a maximum of 2.4 seconds and an end-to-end delivery ratio of 94.7 percent.

Perhaps the most practically important findings came from the field trials comparing open-field and forested conditions at 25 and 50 meters. Across repeated 600-second runs totaling roughly 870 to 872 transmitted packets per condition, mean packet delivery stayed between 99.77 and 100 percent in every outdoor setting, including forest environments where trees blocked the direct line of sight. Yet the received signal strength told a different story: mean RSSI dropped by roughly 10 to 12 dB in the forest compared with the open field at the same nominal range, falling from about -42.5 dBm to -54.1 dBm at 25 meters and from -50.6 dBm to -61.1 dBm at 50 meters. Statistical analysis confirmed the pattern, with one-way and two-way analyses of variance showing significant effects of environment, distance, and their interaction on signal strength, while receiver logging cadence remained statistically unchanged. In other words, foliage quietly eats into the link margin even when packets keep arriving, which matters for predicting how the network will behave as canopies thicken or weather changes.

On the classification side, the recall-oriented Random Forest achieved a recall of 0.907 on the held-out benchmark, correctly flagging 49 of 54 true fire cases while missing 5. The trade-off was visible in the other metrics: precision reached 0.605, meaning 32 of the 50 no-fire samples were incorrectly flagged as fire, and specificity came in at 0.360. The authors are explicit that this operating point should be read as a conservative screening signal rather than an automatic fire declaration. They suggest that nuisance alerts could be tamed at the dashboard level without touching the communication pipeline, for example by requiring repeated high-risk outputs from the same node or agreement among neighboring nodes before escalating an alert.

Power remains the honest constraint on the vision of unattended forest sensors. Current measurements showed approximately 42 milliamps in idle operation, 58 milliamps during sensor readout, and 90 milliamps during Zigbee transmission bursts, averaging about 43 to 44 milliamps or roughly 1 ampere-hour per day. A nominal 2,000 milliamp-hour battery therefore provides only about two days of battery-only operation, and while each node carries a 5-volt solar panel with maximum power point tracking charging, the solar contribution does not yet create a large endurance margin. The authors frame their energy analysis as a first-order duty-cycle budget rather than a seasonal autonomy model, acknowledging that longer autonomous deployments will need more aggressive duty cycling, lower-power hardware, or a bigger harvesting margin.

The team is careful to bound what the results actually prove. The Zigbee figures come from indoor corridor tests and should not be read as direct estimates of outdoor forest performance, the 800-meter LoRa result is a baseline open-field line-of-sight measurement, and the classifier was trained on a public dataset rather than on fire-labeled records from an intended deployment site. Site-specific calibration, larger node counts, longer-duration stress tests, obstructed outdoor Zigbee trials, and long-term energy characterization under variable solar conditions all remain on the future-work list. Even so, the prototype demonstrates that role-separated networking, coordinator-side batching, and browser-side machine learning can be combined into a working pipeline that puts near-ground ignition risk on an operator’s screen within seconds, a meaningful step toward catching wildfires while they are still small enough to fight.

Subject of Research: A hybrid Zigbee-LoRa IoT sensor network with integrated machine learning for ignition-stage wildfire risk monitoring

Article Title: A hybrid Zigbee–LoRa sensor network with integrated machine learning for ignition-stage wildfire risk monitoring

Article References: Ferrufino, F., Aighobahi, A., & Rahman, M. (2026). A hybrid Zigbee–LoRa sensor network with integrated machine learning for ignition-stage wildfire risk monitoring. Frontiers in Communications and Networks, 7, Article 1783392. https://doi.org/10.3389/frcmn.2026.1783392

Image Credits: AI Generated

DOI: 10.3389/frcmn.2026.1783392

Keywords: wildfire monitoring, Zigbee, LoRa, Internet of Things, machine learning, Random Forest, sensor networks, early warning systems, ignition detection, low-power communication, forest fires, edge inference