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The Role of UAV-IoT Networks in Future Wildfire Detection

Osama M. Bushnaq, Anas Chaaban, Tareq Y. Al-Naffouri

arXiv:2007.14158v1cs.NIeess.SP

TL;DR

Wildfire detection must address increasing wildfire risk and the late or unreliable operation of satellite and remote camera methods. The paper models a UAV-IoT network with DTMC-based detection analysis and optimizes sensor density and UAV count under budget constraints. Numerical results indicate that the system can be cost-effective relative to satellite detection, especially when wildfire-related losses are high.

  • Problem

    Wildfires are increasing in severity and frequency, while existing detection methods can be late or unreliable; the paper examines UAV-IoT networks as an alternative for early detection.

  • Method

    The paper uses DTMC, geometry, and probability theory to analyze UAV-IoT detection and optimize IoT-device density and UAV count under time and budget constraints.

  • Results

    Increasing UAV numbers improves detection, whereas increasing IoT-device density does not necessarily do so; UAV-IoT deployment can be cost-effective for relatively high wildfire-related losses.

  • Takeaways & Limitations

    UAV-IoT networks provide design guidance for balancing UAV deployment and sensor density when improving wildfire detection under limited budgets.

Abstract

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The challenge of wildfire management and detection is recently gaining increased attention due to the increased severity and frequency of wildfires worldwide. Popular fire detection techniques such as satellite imaging and remote camera-based sensing suffer from late detection and low reliability while early wildfire detection is a key to prevent massive fires. In this paper, we propose a novel wildfire detection solution based on unmanned aerial vehicles assisted Internet of things (UAV-IoT) networks. The main objective is to (1) study the performance and reliability of the UAV-IoT networks for wildfire detection and (2) present a guideline to optimize the UAV-IoT network to improve fire detection probability under limited budgets. We focus on optimizing the IoT devices' density and number of UAVs covering the forest area such that a lower bound of the wildfires detection probability is maximized within a limited time and budget. At any time after the fire ignition, the IoT devices within a limited distance from the fire can detect it. These IoT devices can then report their measurements only when the UAV is nearby. Discrete-time Markov chain (DTMC) analysis is utilized to compute the fire detection probability at discrete time. Before declaring fire detection, a validation state is designed to account for IoT devices' practical limitations such as miss-detection and false alarm probabilities. Numerical results suggest that given enough system budget, the UAV-IoT based fire detection can offer a faster and more reliable wildfire detection solution than the state of the art satellite imaging techniques.

I. INTRODUCTION

Wildfire severity and frequency make early detection a major management challenge, while satellite, camera-based, and conventional IoT approaches face reliability, coverage, or infrastructure limits. The paper proposes UAV-IoT networks and studies how UAV numbers and IoT density can improve detection under constrained budgets.

  • Motivation: Wildfires are increasing in number, frequency, and severity, imposing substantial economic, ecological, and community impacts.Canadian forests alone burn an average of 2.5 million hectares annually, with estimated yearly costs of 370–740 million US dollars.
  • Motivation: Early detection is essential because satellite imaging and infrared cameras are unreliable, particularly under cloudy weather conditions.IoT networks offer a potential alternative through numerous cheap, self-powered sensors.
  • Motivation: Conventional IoT networks cannot readily aggregate forest measurements because remote areas lack infrastructure and sensors have limited power and complexity.The paper notes that supporting massive sensor densities does not eliminate these deployment and aggregation constraints.
  • Motivation: UAVs provide flexible, lower-cost access to dangerous and remote areas while supporting communication reliability for disaster management.This motivates UAV-IoT networks for natural-disaster applications.
  • Contributions: The paper proposes UAV-IoT wildfire detection and optimizes IoT-device density and UAV count to maximize a lower detection-probability bound under a limited budget.The system uses discrete-time Markov analysis and a validation state to model detection decisions and practical sensing limitations.
  • Related approaches: Existing wildfire detection methods include satellite imaging, remote sensing, and wireless sensor networks, each with distinct coverage, weather, energy, or infrastructure limitations.Satellite detection may miss early-stage fires, camera-based sensing is weather- and battery-sensitive, and WSNs face long-range transmission constraints.

II. SYSTEM MODEL

The system distributes low-cost IoT sensors across a forest and uses UAVs to collect nearby sensor flags, while modeling fire growth and sensor detection geometrically. The analysis adopts tractable worst-case assumptions, including random UAV motion and circular fire spread.

  • IoT and UAV deployment: IoT sensors are randomly distributed over forest area A with density λ_s = N_s/A and detect fires within distance d_s.Each sensor produces a binary fire or no-fire flag.
  • IoT and UAV deployment: UAVs collect sensor flags from nearby devices and move to new locations instead of relying on prohibited long-range sensor transmission to fixed access points.Random UAV movement is assumed for tractability, with optimized paths expected to improve performance.
  • Fire spread model: The fire-spread model uses a grid-based Markov process in which neighboring blocks ignite with probabilities determined by environmental parameters.The paper simplifies this model by ignoring wind and assuming identical environmental properties across blocks.
  • Fire spread model: Assuming uniform conditions and no wind produces a circular fire that represents a worst-case detection geometry because it has the smallest perimeter for a fixed fire size.The resulting fire radius is R_f[k] = vT_k, where v is the fire rate of spread.
  • Sensor detection model: Sensors detect fires through environmental variation δ(d) = δ_n + δ_f(d), combining nature-induced and distance-dependent fire-induced components.The fire-induced component decreases with distance from the fire front-line, whereas the nature-induced component is independent of distance.
  • Sensor detection model: The sensor detection ring contains points outside the fire front-line where sensors detect the fire with probability one, while sensors at larger distances are conservatively assumed not to detect it.Sensor errors are represented by ϵ_s, and the ring is bounded by the fire radius and R_s[k] = R_f[k] + d_s.

C. IoT devices and UAVs Setup

The setup models randomly deployed IoT sensors and UAV coverage, with detection depending on overlap between the sensor detection ring and UAV coverage. UAV height and coverage radius are optimized under target SNR and transmission-error constraints.

  • Network setup: IoT devices follow a Poisson point process with density λs, while Nu UAVs divide the forest into equal search areas.Each UAV hovers at locations to collect observations within its circular coverage region Bhov.
  • Detection geometry: Fire detection is possible only in Bin = Bs ∩ Bhov, where Bs is the sensor detection ring and Bhov is the UAV coverage region.The remainder of the UAV coverage region is Bout = Bhov \ Bin.
  • Model assumptions: Random, temporally independent UAV locations are assumed to keep trajectory analysis tractable, with optimized trajectories expected to improve performance.The model also assumes UAVs remain sufficiently far apart.
  • Coverage trade-offs: Larger Bhov increases overlap probability but also increases sensor-collection time and degrades the sensors-UAV channel quality.Reducing coverage area can decrease transmission error, whereas larger coverage collects more measurements despite allowing some transmission error.
  • Radio design: For a target edge SNR, UAV height is optimized to maximize Rhov, while transmission bit error rates are evaluated under BPSK and repetition coding.The reported BPSK bit error rates at target SNRs of 0, 5, and 10 dB are 79 × 10^-2, 6 × 10^-3, and 3.9 × 10^-6, respectively.

E. Medium access control

The medium-access design uses slotted ALOHA so UAVs can wake, synchronize, and collect sensor observations before moving between coverage locations. The section also formulates budgeted detection-probability optimization over sensing and UAV deployment choices.

  • Medium access control: Slotted ALOHA limits control overhead by allowing covered sensors to transmit observations probabilistically across multiple slots.The UAV first broadcasts a wake-up/synchronization signal, then collects transmissions before moving.
  • Collection cycle: Each UAV spends T = Thover + Ttravel seconds collecting data at one region and traveling to the next.Thover and Ttravel denote average hovering and traveling times.
  • Observation collection: During hovering, the UAV collects an average of N = βλsπRhov^2 sensor observations, where β is the collected-to-covered sensor ratio.The hovering time is designed so that β is approximately 1.
  • Timing model: Slotted ALOHA efficiency is η = 36.8%, and Tobs = γηTsym represents the time needed to collect one observation.Here γ is the number of bit repetitions and Tsym is the time required to transmit one symbol.
  • Optimization: The first optimization maximizes detection probability within a limited time and budget by selecting M, Ns, and Nu.The budget constraint includes sensor and UAV costs, while reducing β has an effect similar to reducing λs but may be less practical for cost reduction.

2) Wildfire losses minimization:

The wildfire-losses problem minimizes total expected losses by combining UAV-IoT system cost, time-dependent fire damage and firefighting cost, and the cost of delayed or missed detection.

  • Objective: The objective minimizes fire damage, firefighting cost, and UAV-IoT system cost caused by a probable wildfire.Fire damage and firefighting costs increase with time because fire area and firefighting expense grow as detection is delayed.
  • Detection timing: The loss formulation weights detection at each time step by the probability ρD[k] of detection exactly at that step.The formulation also accounts for time steps before detection by other methods.
  • Cost components: The total-loss expression separates UAV-IoT network cost, detection costs within TD, and the cost of not detecting the fire by TD.These terms represent system deployment, timely detection, and residual loss after the decision horizon.
  • Solution approach: The loss-minimization problem follows derivation of ρD[k] and πD[k] and is solved using simple search algorithms.The probabilities are expressed in terms of the required positive flags, sensor count, and UAV count.

A. Intersection between UAV coverage region and IoT sensors detection ring:

The analysis characterizes when a UAV’s circular coverage intersects the IoT sensor detection ring and derives the probability of that intersection under uniformly random UAV locations. A UAV can detect the fire only when this overlap exists, although sensing and transmission errors can still produce false alarms.

  • Intersection condition: A fire is detectable at the UAV only when Bin[k] = Bs[k] ∩ Bhov[k] is nonempty.Sensors in Bs detect a circular fire with probability 1 − ϵs, while Bin is the overlap region containing potentially detecting sensors.
  • UAV detection ring: The UAV detection ring Bu is the set of UAV locations whose coverage region intersects the sensor detection ring.Its inner and outer radial boundaries are determined by the fire, sensor, and UAV coverage radii.
  • Intersection probability: For uniformly random UAV locations, Proposition 1 gives the probability that Bin[k] is nonempty from the area of Bu[k] relative to the forest area A.The probability is therefore governed by the spatial area in which coverage intersects the sensor ring.
  • Alarm validation: At least M false-positive flags can trigger a false alarm when Bin[k] is empty, while an alarm with Bin[k] nonempty is treated as a correct detection.The validation logic accounts for both faulty sensors in Bout[k] and sensors in Bin[k].

B. Markov Representation

The wildfire detection process is modeled as a time-inhomogeneous DTMC with no-fire, verification, and absorbing fire-detection states. State probabilities evolve through time-dependent transitions driven by detection and false-alarm probabilities.

  • The DTMC uses states N, V, and D for no fire, verification, and fire detection, respectively.
  • Transition probabilities vary with time, making the wildfire detection chain time-inhomogeneous.
  • The system initially occupies the no-fire state, represented by π[0] = [1 0 0].
  • Because D is absorbing, πD[K] gives the probability of fire detection by time step K.
  • The transition probabilities from verification to no fire, verification, or detection depend on the average verification time Tvrf and detection and false-alarm probabilities.

C. Detection and False Alarm Probabilities

Detection and false-alarm probabilities are determined by whether the UAV coverage intersects the fire-sensor ring and by the number and reliability of collected sensor flags. A threshold M balances missed detections against false alarms.

  • The UAV declares a fire possibility after receiving at least M positive flags from N observations.
  • Increasing M decreases both false-alarm and detection probabilities, requiring a threshold trade-off.
  • False alarms occur without an intersecting fire-sensor ring when at least M faulty positive flags are received.
  • A fire is detectable at the UAV only when its coverage region intersects the IoT sensor detection ring.
  • The conditional detection probability combines positive flags from nin sensors inside Bin and N − nin sensors outside it using a Poisson binomial distribution.

D. The probability of having an average of nin sensors inside Bin, Pnin|int[k]

The average number of sensors inside the detection intersection is computed from sensor density and intersection area, then integrated numerically over UAV-fire geometry. Approximation is used because the area-distance relationship lacks a closed form.

  • The average sensor count inside Bin is nin = λsAin, where λs is sensor density and Ain is the intersection area.
  • The distribution of Ain is obtained from the distance R between the fire center and UAV position and the corresponding circle-intersection geometry.
  • The Poisson-binomial model uses success rate 1 − ϵ for sensors inside Bin and ϵ for the remaining sensors.
  • The complex relation between R and Ain prevents expressing R as a function of Ain or deriving its PDF by a closed-form change of variables.
  • The method approximates the resulting integral with accuracy parameter I, and the approximation converges as I →∞.
  • The computed detection and false-alarm probabilities are injected into the DTMC at every time step through Algorithm 1.

V. DESIGN AND PERFORMANCE INSIGHTS

UAV-IoT design requires balancing coverage, sensing density, error rates, verification thresholds, exploration time, and cost. More UAVs consistently improve detection, whereas greater sensor density can trade diagnostic accuracy for slower area exploration.

  • Increasing the number of UAVs strictly improves detection by reducing the forest area covered by each UAV, but increases deployment cost.
  • Higher IoT density improves detection and false-alarm probabilities locally but can reduce the number of regions explored during a fixed mission time.
  • Larger UAV coverage raises intersection probability but also increases transmission error, hovering time, and the cost of exploring new regions.
  • The threshold M should balance false alarms and missed detections, with error probability ϵ and verification time Tvrf affecting its optimum.
  • For M = 1, detection and false-alarm probabilities are maximized in opposite directions, while computational complexity simplifies to O(IK).
  • The design optimization is NP-hard, so the paper searches over UAV count, sensor density, and flag threshold under a system budget.

VI. NUMERICAL RESULTS

Numerical analysis and Monte Carlo validation show how UAV-IoT design parameters affect wildfire detection and losses under constrained budgets. Detection improves with time and UAV count, but sensor density has an interior optimum because higher density increases collection time.

  • Validation: Monte Carlo simulations independently validate the mathematical wildfire-detection analysis.IoT devices and fire locations are randomized, while UAVs collect measurements from covered sensors at each time step.
  • Device density and UAV deployment: Detection probability increases with IoT device density up to an optimum, then decreases as collection time reduces UAV visits within the 30-minute critical period.Higher density can improve reliability against damaged or uncharged devices, but collecting a fixed percentage of sensors increases hovering time and cost.
  • Device density and UAV deployment: Increasing the number of UAVs monotonically increases coverage probability, while increasing the required positive flags shifts the optimal sensor density upward.The optimal number of positive flags rises approximately linearly with sensor density, with slope dependent on sensing and transmission error probability.
  • Verification and error effects: Higher sensing and transmission error probability requires larger positive-flag thresholds to balance miss-detection and false-alarm risks.When error probability is high, larger M improves detection probability; at high error probability, values converge toward πD = 0.6.
  • Verification and error effects: Higher verification time favors larger positive-flag thresholds, whereas larger IoT detection ranges permit larger thresholds while maintaining detection performance.Verification time is wasted after false alarms, while larger detection ranges reduce miss-detection probability.
  • Time-dependent detection: The cumulative detection probability approaches one over time, while detection exactly at a given time eventually declines as the probability of fire survival decreases.Fire growth initially increases detection at later time steps, but survival probability eventually becomes limiting.
  • Budget optimization: Minimum-loss budgets increase with fire-loss cost, reaching 3.6 × 10^5, 5 × 10^5, and 7 × 10^5 for ωd ∈{500, 1000, 2000}, respectively.Beyond the indicated budget points, additional investment is not cost effective because detection probability saturates.

VII. CONCLUSION

The paper analyzes UAV-IoT wildfire detection using probabilistic modeling and simulation. Results identify how UAV count, sensor density, and budget affect performance and support UAV-IoT as a potentially cost-efficient alternative to satellite imaging when fire-related losses are high.

  • Analysis and validation: The study proposes and analyzes a UAV-IoT wildfire-detection system using DTMC, geometry, and probability theory, validated against independent Monte Carlo simulations.The analysis covers system-design optimization and detection probability.
  • Design findings: Increasing the number of UAVs strictly improves fire-detection performance, whereas increasing IoT-device density does not necessarily improve detection probability.The numerical results distinguish the monotonic benefit of UAV count from the non-monotonic effect of sensor density.
  • Practical implication: UAV-IoT systems can be a cost-efficient alternative to satellite imaging for wildfire detection, especially when fire-related losses are high.This conclusion is stated within the paper’s numerical evaluation of detection and wildfire-loss objectives.
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