Large-scale wireless sensor network using electrical impedance spectroscopy for evaluating wildfire risk

Abstract

Methods and systems for monitoring dead and live fuel moisture in wildland environments, comprise a plurality of wireless sensors, each of the plurality of wireless sensors comprising: a housing, at least two corrosion-resistant electrodes, and a communication module configured to transmit data wirelessly from the sensor via low-power communication protocols, wherein each of the plurality of sensors is configured to measure electrical impedance, and a cloud platform for receiving, processing, and visualizing moisture data in real time.

Description

LARGE-SCALE WIRELESS SENSOR NETWORK USING ELECTRICAL IMPEDANCE SPECTROSCOPY FOR EVALUATING WILDFIRE RISKCROSS-REFERENCE TO RELATED PATENT APPLICATIONS

[0001] This application claims the priority and benefit under 35 U.S.C. §119(e) of U.S. Provisional Patent Application Serial No. 63 / 776,658 filed March 24, 2025, entitled “LARGE-SCALE WIRELESS SENSOR NETWORK USING ELECTRICAL IMPEDANCE SPECTROSCOPY.” U.S. Provisional Patent Application Serial Number 63 / 776,658 is herein incorporated by reference in its entirety.TECHNICAL FIELD

[0002] Embodiments are generally related to systems and methods for large-scale wireless sensor networks using electrical impedance spectroscopy for monitoring dead and live fuel moisture in situ in wildland environments.BACKGROUND

[0003] Wildfires continue to intensify in frequency and severity, partly due to prolonged droughts and climate change-induced shifts in vegetation and weather patterns. An essential factor influencing fire behavior is the moisture content of dead and live wildland fuels — such as fallen branches, twigs, and logs, as well as live trees, shrubs, and grasses. These fuels, when extremely dry, can rapidly ignite and sustain the spread of wildfires.

[0004] Currently, dead and live fuel moisture estimates often rely on the National Fire Danger Rating System (NFDRS) and the Fire Environment Mapping System (FEMS). The NFDRS uses weather data from approximately 2,800 Remote Automated Weather Stations (RAWS) to cover roughly 821 million wildland acres in the U.S. This averages to one RAWS station per 293,000 acres, resulting in large-scale assessments that overlook critical microclimatic variations (e.g., humidity, temperature, topography, vegetation cover, and wind speed) influencing fuel moisture at a localized level.

[0005] FEMS aggregates data from multiple sources, including RAWS stations and manually collected fuel samples, to generate fuel environment assessments for fire management operations. While FEMS provides a broader data integration framework, it remains dependent on the same sparse RAWS network and labor-intensive manual sampling methods for its underlying fuel moisture inputs. Consequently, the NFDRS, FEMS, and similar large-scale models frequently produce discrepancies between estimated and actual fuel moisture. These inaccuracies limit their effectiveness for real-time decision-making, such as planning prescribed burns or optimizing fuel load management.

[0006] Alternative modeling techniques, including meteorological and satellite-based remote sensing, have attempted to improve granularity in fuel moisture estimation. However, studies show that these methods still struggle to provide the accuracy needed for effective wildfire risk management. Similarly, a recent semi-mechanistic model using vapor pressure deficit (VPD) for global-scale estimation of 1 -hour dead and live fuel moisture offered promise but faltered in accurately predicting moisture levels below 10% — a critical moisture threshold that heavily influences fire behavior.

[0007] Direct measurements of dead and live fuel moisture currently rely on labor-intensive methods. Oven drying is the “gold standard” for accuracy. This technique involves the collection of fuel samples which are dried at 60°C to 100°C for at least 24 hours. While precise, oven drying is time-consuming, cannot provide real-time measurements, and is impractical for large-scale or remote deployments. Other methods, such as moisture scales and handheld moisture meters, are not easily scalable to broad landscapes and generally fail to deliver continuous or wireless data streams. Moreover, handheld devices like the Delmhorst J-2000 and indirect reference stick methods (e.g., 10-hour fuel sticks and scales) lack the ruggedness, accuracy, and connectivity required for real-time, in situ monitoring.

[0008] The Campbell CS506 sensor currently represents a state-of-the-art remote dead fuel moisture measurement system. It uses a 10-hour ponderosa pine reference dowel to estimate fuel moisture via its electrical waveguide properties. While more convenient than oven drying, this approach still depends on a reference material that does not directly represent actual wildland fuels. The dowels degrade over time, requiring manual replacement and leading to inaccuracies and maintenance challenges. Furthermore, for live fuel moisture,there are no reliable automated devices that can capture moisture content effectively, leaving manual sampling as the only viable option. Additionally, the CS506 requires wired connections to a datalogger, limiting its usefulness in remote, large-scale deployments. Although the CS506 can serve as a benchmark for dead fuel, shortcomings and the lack of live fuel solutions make it unsuitable for many applications.

[0009] As such, a need exists for field-deployable, and scalable systems capable of measuring dead and live fuel moisture directly from naturally occurring fuels, without reliance on reference materials or manual sample collection as detailed in the embodiments presented herein.SUMMARY

[0010] The following summary is provided to facilitate an understanding of some of the innovative features unique to the embodiments disclosed and is not intended to be a full description. A full appreciation of the various aspects of the embodiments can be gained by taking the entire specification, claims, drawings, and abstract as a whole.

[0011] It is, therefore, one aspect of the disclosed embodiments to provide improved methods and systems for monitoring moisture content of dead and live fuels.

[0012] It is another aspect of the disclosed embodiments to provide a method, system, and apparatus for scalable fuel moisture content monitoring.

[0013] It is another aspect of the disclosed embodiments to provide a method, system, and apparatus for electrical impedance spectroscopy for monitoring dead and live fuel moisture in situ in wildland environments.

[0014] Aspects of the invention are detailed in the specification, drawings, and claims provided herein. For example, in an embodiment, a system for monitoring fuel moisture comprises a plurality of wireless sensors, each of the plurality of wireless sensors comprising a housing, at least two electrodes configured to be inserted into organic matter, an impedance measurement circuit configured to measure electrical impedance at one or more frequencies, a communication module configured to transmit data wirelessly from each of the plurality of wireless sensors, and a cloud platform configured for receiving impedance data from the plurality of wireless sensors, processing the impedance data to determine moisture content data, and visualizing the moisture content data. In an embodiment, the plurality of wireless sensors measure impedance at a range of frequencies optimized through calibration. In an embodiment at least one of the plurality of wireless sensors further comprises a local meteorological sensor for measuring at least one of wind speed and environmental conditions. In an embodiment, the system for monitoring fuel moisture further comprises at least one gateway acting as an intermediary between the plurality of wireless sensors and the cloud platform, the at least one gateway configured to receive data packets from the plurality of wireless sensors and forward the data packets to the cloud platform. In an embodiment, the communication module is configured to transmit data using at least oneprotocol selected from the group comprising LoRaWAN, Non-Terrestrial Networks (NTN), DECT NR+, Cellular loT protocols, and Bluetooth Low Energy (BLE) mesh networking. In an embodiment, at least one of the plurality of wireless sensors further comprises an energy harvesting module. In an embodiment, each of the plurality of wireless sensors is configured with periodic wake cycles that perform impedance measurements at predefined intervals. In an embodiment, the cloud platform further comprises an analytics tool configured to generate alerts when moisture levels cross critical thresholds and to integrate the moisture data into predictive wildfire risk models by fusing local sensor data and meteorological data.

[0015] In an embodiment, a method for measuring fuel moisture comprises deploying a network of wireless sensors configured to measure electrical impedance directly from naturally occurring dead wood or live vegetation, transmitting sensor data collected by the network of wireless sensors to a cloud platform, processing the data on the cloud platform to determine moisture content, applying at least one environmental adjustment to generate processed moisture data, the at least one environmental adjustment selected from a group comprising species-specific calibration curves, temperature compensation, wind speed corrections, humidity compensation, and fuel density adjustments, and visualizing the processed moisture data. In an embodiment, the method for measuring fuel moisture further comprises calibrating the network of wireless sensors by comparing impedance measurements against reference values for various fuel species. In an embodiment, measuring electrical impedance further comprises measuring impedance at multiple frequencies, wherein higher frequencies are sensitive to bound water in fuel and lower frequencies capture bulk moisture effects. In an embodiment, the method for measuring fuel moisture further comprises generating alerts when moisture levels cross critical thresholds. In an embodiment, the method for measuring fuel moisture further comprises integrating the processed moisture data with external wildfire forecasting tools. In an embodiment, the method for measuring fuel moisture further comprises generating a spatial moisture map associated with the network of wireless sensors. In an embodiment, the method for measuring fuel moisture further comprises fusing locally measured meteorological data comprising wind speed with external weather datasets.

[0016] In an embodiment, a sensor apparatus for measuring moisture content of fuel comprises a housing, at least two electrodes configured for insertion into naturally occurringdead wood or live vegetation, an impedance measurement circuit calibrated for one or more frequencies, a microcontroller, a wireless communication module, and a battery module. In an embodiment, each of the at least two electrodes comprise at least one of stainless steel and corrosion resistant alloys. In an embodiment, a relative position between each of the at least two electrodes is selected with spacing and depth for representative sampling of internal fuel moisture. In an embodiment, the sensor apparatus for measuring moisture content of fuel further comprises an energy harvesting module. In an embodiment, the sensor apparatus for measuring moisture content of fuel further comprises a local meteorological sensor for measuring wind speed and environmental conditions.

[0017] In an embodiment, a system for monitoring dead and live fuel moisture in wildland environments, comprises a plurality of wireless sensors, each of the plurality of wireless sensors comprising: a housing; at least two corrosion-resistant electrodes configured to be inserted into naturally occurring dead wood or live vegetation; an impedance measurement circuit configured to measure electrical impedance at one or more frequencies; a communication module configured to transmit data wirelessly from the sensor via low-power communication protocols; and a cloud platform configured to receive impedance data from the plurality of wireless sensors, process the impedance data to determine moisture content, and visualize moisture data in real time. In an embodiment, the plurality of wireless sensors measure impedance at a range of frequencies optimized through calibration, thereby enhancing the accuracy of moisture content determination. In an embodiment, at least one of the plurality of wireless sensors further comprises a local meteorological sensor for measuring wind speed and environmental conditions. In an embodiment, the system further comprises one or more gateways acting as intermediaries between the plurality of wireless sensors and the cloud platform, the one or more gateways configured to receive data packets from the plurality of wireless sensors and forward the data packets to the cloud platform. In an embodiment, the system further comprises a communication module configured to transmit data using at least one protocol selected from the group consisting of LoRaWAN, Non-Terrestrial Networks (NTN), DECT NR+, Cellular loT protocols (e.g., LTE-M, NB-loT), and Bluetooth Low Energy (BLE) mesh networking. In an embodiment, at least one of the plurality of wireless sensors further comprises a solar or kinetic energy harvesting module. In an embodiment, each of the plurality of wireless sensors is configured with periodic wake cycles that perform impedance measurements at predefined intervals. In an embodiment,the cloud platform comprises analytics tools configured to generate alerts when moisture levels cross critical thresholds and to integrate the moisture data into predictive wildfire risk models by fusing local sensor data and meteorological data.

[0018] In an embodiment, a method for measuring dead and live fuel moisture in wildland environments, comprises deploying a network of wireless sensors configured to measure electrical impedance directly from naturally occurring dead wood or live vegetation; transmitting sensor data to a cloud platform using low-power, long-range wireless communication protocols; processing the data on the cloud platform to determine moisture content, applying at least one environmental adjustment selected from the group consisting of species-specific calibration curves, temperature compensation, wind speed corrections, humidity compensation, and fuel density adjustments; and visualizing processed moisture data for stakeholders. In an embodiment, the method comprises calibrating the network of wireless sensors by comparing impedance measurements against oven-dry reference values for various fuel species. In an embodiment, measuring electrical impedance comprises measuring impedance at multiple frequencies, wherein higher frequencies are sensitive to bound water in fuel and lower frequencies capture bulk moisture effects. In an embodiment, the method comprises generating alerts when moisture levels cross critical thresholds. In an embodiment, the method comprises integrating the processed moisture data with external wildfire forecasting tools. In an embodiment, the method comprises generating spatial moisture maps across the network of wireless sensors. In an embodiment, the method comprises fusing locally measured meteorological data including wind speed with external weather datasets to enhance the accuracy of the moisture content determination.

[0019] In an embodiment, a sensor apparatus for measuring moisture content of dead or live fuel in a wildland environment, comprises a housing; at least two corrosion-resistant electrodes configured for insertion into naturally occurring dead wood or live vegetation; an impedance measurement circuit calibrated for one or more frequencies that optimize moisture detection; a low-power microcontroller; a wireless communication module; and a battery module. In an embodiment, the at least two corrosion-resistant electrodes comprise stainless steel or corrosion-resistant alloys, and wherein an electrode configuration defines spacing and depth for representative sampling of internal fuel moisture. In an embodiment, the sensor apparatus further comprises a solar or kinetic energy harvesting module. In anembodiment, the sensor apparatus further comprises a local meteorological sensor for measuring wind speed and environmental conditions.

[0020] In an embodiment, a computer-implemented method for determining fuel moisture content in wildland environments, comprises receiving, at a cloud platform, impedance data measured at one or more frequencies from a plurality of wireless sensors deployed on naturally occurring dead wood or live vegetation in a wildland environment; applying speciesspecific calibration curves to the impedance data; performing temperature compensation on the impedance data; fusing meteorological data from local sensor measurements with meteorological data obtained from external sources; and generating moisture content determinations based on the calibrated, compensated, and fused data.BRIEF DESCRIPTION OF THE FIGURES

[0021] The accompanying figures, in which like reference numerals refer to identical or functionally similar elements throughout the separate views and which are incorporated in and form a part of the specification, further illustrate the embodiments and, together with the detailed description, serve to explain the embodiments disclosed herein.

[0022] FIG. 1 depicts a block diagram of a system for monitoring dead and live fuel moisture in situ in wildland environments, in accordance with the disclosed embodiments;

[0023] FIG. 2 depicts a block diagram of a sensor associated with a system for monitoring dead and live fuel moisture in situ in wildland environments, in accordance with the disclosed embodiments;

[0024] FIG. 3 depicts steps associated with a method for monitoring dead and live fuel moisture in situ in wildland environments, in accordance with the disclosed embodiments;

[0025] FIG. 4 depicts a block diagram of a computer system which is implemented in accordance with the disclosed embodiments;

[0026] FIG. 5 depicts a graphical representation of a network of data-processing devices in which aspects of the present embodiments may be implemented;

[0027] FIG. 6 depicts a computer software system for directing the operation of the data-processing system depicted in FIG. 5, in accordance with an example embodiment;

[0028] FIG. 7 depicts a network topology diagram illustrating communication paths between sensors, gateways, a cloud platform, and client applications, in accordance with the disclosed embodiments;

[0029] FIG. 8 depicts a data flow diagram of a cloud data processing pipeline for transforming raw impedance data into moisture content determinations, in accordance with the disclosed embodiments;

[0030] FIG. 9 depicts a cross-sectional detail view of sensor electrodes inserted into a fuelsample, illustrating electrode spacing and penetration depth, in accordance with the disclosed embodiments; and

[0031] FIG. 10 depicts an exemplary dashboard visualization including a spatial moisture map and alert indicators, in accordance with the disclosed embodiments.DETAILED DESCRIPTION

[0032] The particular values and configurations discussed in the following non-limiting examples can be varied, and are cited merely to illustrate one or more embodiments and are not intended to limit the scope thereof.

[0033] Example embodiments will now be described more fully hereinafter, with reference to the accompanying drawings, in which illustrative embodiments are shown. The embodiments disclosed herein can be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the embodiments to those skilled in the art. Like numbers refer to like elements throughout.

[0034] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0035] Throughout the specification and claims, terms may have nuanced meanings suggested or implied in context beyond an explicitly stated meaning. Likewise, the phrase “in one embodiment” as used herein does not necessarily refer to the same embodiment and the phrase “in another embodiment” as used herein does not necessarily refer to a different embodiment. It is intended, for example, that claimed subject matter include combinations of example embodiments in whole or in part.

[0036] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unlessexpressly so defined herein.

[0037] It is contemplated that any embodiment discussed in this specification can be implemented with respect to any method, system, or apparatus of the invention, and vice versa. Furthermore, systems of the invention can be used to achieve methods of the invention.

[0038] It will be understood that particular embodiments described herein are shown by way of illustration and not as limitations of the invention. The principal features of this invention can be employed in various embodiments without departing from the scope of the invention. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, numerous equivalents to the specific procedures described herein. Such equivalents are considered to be within the scope of this invention and are covered by the claims.

[0039] The use of the word “a” or “an” when used in conjunction with the term “comprising” in the claims and / or the specification may mean “one,” but it is also consistent with the meaning of “one or more,” “at least one,” and “one or more than one.” The use of the term “or” in the claims is used to mean “and / or” unless explicitly indicated to refer to alternatives only or the alternatives are mutually exclusive, although the disclosure supports a definition that refers to only alternatives and “and / or.” Throughout this application, the term “about” is used to indicate that a value includes the inherent variation of error for the device, the method being employed to determine the value, or the variation that exists among the study subjects.

[0040] As used in this specification and claim(s), the words “comprising” (and any form of comprising, such as “comprise” and “comprises”), “having” (and any form of having, such as “have” and “has”), “including” (and any form of including, such as “includes” and “include”) or “containing” (and any form of containing, such as “contains” and “contain”) are inclusive or open-ended and do not exclude additional, unrecited elements or method steps.

[0041] The term “or combinations thereof” as used herein refers to all permutations and combinations of the listed items preceding the term. For example, “A, B, C, or combinations thereof” is intended to include at least one of: A, B, C, AB, AC, BC, or ABC, and if order is important in a particular context, also BA, CA, CB, CBA, BCA, ACB, BAC, or CAB. Continuingwith this example, expressly included are combinations that contain repeats of one or more item or term, such as BB, AAA, AB, BBC, AAABCCCC, CBBAAA, CABABB, and so forth. The skilled artisan will understand that typically there is no limit on the number of items or terms in any combination, unless otherwise apparent from the context.

[0042] All of the systems and / or methods disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. While the systems and methods of this invention have been described in terms of preferred embodiments, it will be apparent to those of skill in the art that variations may be applied to the systems and / or methods and in the steps or in the sequence of steps of the method described herein without departing from the concept, spirit, and scope of the invention. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope and concept of the invention as defined by the appended claims.

[0043] Embodiments disclosed herein are directed to large-scale wireless sensor networks using electrical impedance spectroscopy for monitoring dead and live fuel moisture in situ in wildland environments. Embodiments introduce a large-scale, wireless sensor network that employs Electrical Impedance Spectroscopy (EIS) to directly measure the moisture content of dead woody fuels and live vegetation in situ. Unlike existing reference-dowel methods or indirect modeling approaches, this technology eliminates the need for artificial proxies or labor-intensive sample preparation. By integrating low-power wireless communication (e.g., LoRaWAN), cloud-based data processing, and robust sensor designs, the systems can be deployed across wide wildland areas, delivering continuous, high-resolution moisture data. This enables stakeholders (such as land managers, firefighters, researchers, electric utilities, private landowners, and military bases) to make informed, time-sensitive decisions for wildfire risk reduction and fuel management.

[0044] FIG. 1 illustrates aspects of a system 100 comprising hardware and associated computer processes which can be implemented with software, in accordance with the disclosed embodiments. The system 100, can include wireless (or optionally wired) fuel moisture sensor(s) 105 disposed in an environment 110. In certain embodiments, the environment can comprise a wildland area, wildland-urban interface, or other environment with fire and / or wildfire risk.

[0045] The sensors 105 can measure conditions associated with dead-fuel (e.g. wood) as well as live vegetation (e.g., trees, shrubs, grasses, and duff), including but not limited to moisture content of the wood, live fuel moisture, and plant water potential. In addition, the sensors 105 can measure certain environmental conditions, including but not limited to, ambient temperature, humidity, barometric pressure, light intensity, and light type. It should be understood that multiple sensors 105 can be used in the environment 110.

[0046] The system 100 further includes client application 120, and a server application 125. In certain embodiments one or both of these applications can be provided as part of a cloud 130 application.

[0047] Components of the system 100 can be connected with bi-directional or unidirectional wireless communication. For example, the wireless communication can connect sensors 105 to the server application 125. The client application 120 can communicate with the server application 125 via an application programming interface (API) 135. The sensors can communicate with the cloud architecture via low-power wireless communication protocols, including but not limited to, LoRaWAN, Non-Terrestrial Networks (NTN), DECT NR+, Cellular loT protocols (e.g., LTE-M, NB-loT), or Bluetooth Low Energy (BLE) mesh networking.

[0048] Across all wireless communication implementations, the sensors 105 can be battery-powered devices that collect and transmit data. Battery life is extended through power-saving modes, such as periodic wake cycles that perform measurements at predefined intervals, and embodiments can further incorporate solar or kinetic energy harvesting modules. The specific wireless protocol determines whether intermediate network infrastructure, such as gateways, is required between the sensors 105 and the cloud server 125. The server 125 can forward data to the client 120. The client 120 can process the data received from the server 125 and make the data available to an end user.

[0049] In certain embodiments, LoRaWAN can provide the communication backbone of the system 100. In LoRaWAN implementations, gateways act as intermediaries between the sensors 105 and the cloud 130 server application 125, forwarding data packets. The LoRaWAN configuration provides long-range, low-power, and low-bandwidth data transmission, making it well suited for dense deployments in rugged, forested terrains wheremany sensors 105 may be distributed across large areas (on the scale of miles).

[0050] In certain embodiments, Non-Terrestrial Network (NTN) can provide the communication backbone of the system 100. In NTN implementations, the sensors 105 communicate directly with orbiting satellites without the need for terrestrial gateways or intermediate ground infrastructure. This direct-to-satellite communication enables sensor 105 deployment in extremely remote or inaccessible environments where installing and maintaining ground-based gateways is impractical. The NTN configuration provides global coverage independent of terrestrial network availability.

[0051] In certain embodiments, DECT NR+ can provide the communication backbone of the system 100. In DECT NR+ implementations, the sensors 105 form a self-organizing mesh network that routes data through neighboring sensor nodes to reach a base station or gateway connected to the cloud server 125. DECT NR+ provides low-latency, reliable communication with support for high device density, making it suitable for environments requiring dense sensor coverage with autonomous network formation.

[0052] In certain embodiments, Cellular loT can provide the communication backbone of the system 100. In Cellular loT implementations, including LTE-M and NB-loT protocols, the sensors 105 communicate directly with existing cellular base stations without requiring dedicated gateways. LTE-M provides higher data throughput and supports mobility, while NB-loT is optimized for stationary deployments with very low data rates and deep penetration in challenging terrain. Both protocols leverage existing cellular infrastructure, simplifying deployment in areas with cellular coverage.

[0053] In certain embodiments, Bluetooth Low Energy (BLE) can provide the communication backbone of the system 100. In Bluetooth Low Energy (BLE) mesh networking implementations, the sensors 105 form a mesh network where data is relayed between sensor nodes to reach a BLE-enabled gateway or edge device connected to the cloud server 125. BLE mesh networking is well suited for localized, high-density sensor deployments where sensors are within relatively close proximity to one another.

[0054] It should be appreciated that system 100 can employ a combination of the above wireless communication protocols within a single deployment, with different sensors 105 orgroups of sensors 105 using different protocols based on factors such as terrain, proximity to infrastructure, power constraints, and data transmission requirements.

[0055] In operation, the sensors 105 report data they collect, and manage configuration via the selected wireless communication protocol to the cloud 130 from the environment 110. Client applications 120 communicate with the cloud 130 via the API 135.

[0056] The client application 120 can comprise a downloadable software application or “app”, or can comprise a software application running on a computer system. It is possible for the client application 120 to be distributed on multiple devices to allow various users to access the client application 120. In certain embodiments, the client applications 120 are used to monitor, analyze, modify, and otherwise provide a given sensor 105 data from all the sensors 105 in the environment 110.

[0057] FIG. 2 Illustrates a block diagram showing aspects of the sensors 105. The sensors 105 can operate via direct Electrochemical Impedance Spectroscopy (EIS) measurement at one or more frequencies. The sensors 105 can comprise electrodes 205 that are configured to be inserted into naturally occurring dead wood or live vegetation. By measuring electrical impedance at one or more frequencies, the sensor system 100 can identify and report the relationship between impedance and moisture content.

[0058] Each sensor 105 can include a housing 225 with electrodes 205. The electrodes 205 can extend out of the housing 225, and can be formed of stainless steel and / or other corrosion-resistant alloys. The electrodes 205 that are configured to be inserted into naturally occurring dead wood or live vegetation an impedance measurement circuit 210 calibrated for frequencies that optimize moisture detection. The electrode configuration (e.g., spacing and depth) ensures stable contact and representative sampling of the fuel’s internal moisture.

[0059] Each sensor 105 can also include a microcontroller 215, which can comprise a low-power microcontroller for data process. The sensors 105 can also include a communication module 220, which can comprise a wireless communication module configured to operate using one or more of LoRaWAN, NTN, DECT NR+, LTE-M, NB-loT, and BLE mesh.

[0060] Each of the sensors 105 can further include local meteorological sensors 230. Inaddition to measuring moisture, selected sensor nodes 230 incorporate local wind speed sensors (e.g., ultrasonic or cup anemometers) to capture on-site wind data, as well as temperature sensors, external moisture, sensors, barometric pressure sensors, and the like. This localized wind information is useful for understanding microclimatic effects on fuel drying and fire spread.

[0061] Each of the sensors 105 can also include a battery module 235. Battery life is extended through power-saving modes, such as periodic wake cycles that perform impedance measurements at predefined intervals. Embodiments can further incorporate solar or kinetic energy harvesting modules 240 embodied as photovoltaic panels, kinetic energy harvesting units, or other such energy capture systems. The sensors are configured to use low-power, long-range communication protocols that enable data transmission to centralized gateways and cloud platforms, even in rugged, forested terrains.

[0062] An aspect of the disclosed embodiments is the deployment of many (e.g., thousands) of sensors 105 to capture the spatial heterogeneity of wildland fuels. This granular approach addresses the gap between coarse regional models and localized fire behavior assessments.

[0063] Sensors 105 can be configured with rugged materials, corrosion-resistant electrodes, and power management strategies (e.g., duty cycling and low-power modes) to ensure long-term, low-maintenance operation.

[0064] Embodiments incorporate advanced data processing, meteorological data fusion, and analytics. The cloud-based platform applies environmental corrections (e.g., temperature compensation) and fuses local sensor data with meteorological data, including wind speed measurements, to transform raw impedance data into accurate, actionable moisture readings. Predictive analytics and integration with wildfire risk models further enhance the system’s utility.

[0065] The system’s cloud infrastructure aggregates data from multiple sensors. In certain embodiments, calibration curves and accounts for species-specific and environmental variations are applied but also fuses meteorological data obtained from external sources (e.g., local weather stations) with locally measured parameters such as temperature andwind speed. This data fusion enhances real-time analytics and improves the predictive accuracy of wildfire risk models.

[0066] FIG. 3 illustrates steps associated with a method 300 for leveraging the large-scale wireless sensor network system 100, using electrical impedance spectroscopy for monitoring dead and live fuel moisture in situ in wildland environments in accordance with the disclosed embodiments. The method starts at 305. It should be appreciated that the order of steps illustrated in FIG. 3 is exemplary. In other embodiments, steps can be completed in other orders, or simultaneously, without departing from the scope disclosed herein.

[0067] At step 310 sensors are deployed on wildfire fuel, such as dead wood or live vegetation, in an environment. In certain embodiments, this step can include inserting the electrodes 205 of the sensors 105 into naturally occurring dead wood or live vegetation.

[0068] Initial field calibration can be performed at step 315. Field calibrations can comprise comparing impedance readings against oven-dry reference measurements for various fuel species and diameters. The calibration curves are stored in the cloud platform to adjust subsequent sensor readings dynamically.

[0069] At step 320, the sensors measure impedance across a range of frequencies, identified through initial calibration trials. An optimal frequency range may be identified as a part of this step. The measurements can be taken intermittently by each sensor in the system. In certain embodiments, the measurement duration and frequency can be set using the server app 125 or client app 120.

[0070] The system 100 uses the raw data from the sensors 105 to generate moisture content estimates at step 325. Higher frequencies may be more sensitive to bound water in fuel, while lower frequencies capture bulk moisture effects. Temperature, wind speed, and environmental compensation factors can also be collected with the sensors 105 considered. The system 100 can apply correction factors based on temperature and other environmental parameters (e.g., humidity, wind speed, fuel density). By integrating both local meteorological measurements and cloud-fused data, the system 100 can provide accurate data, even under rapidly changing conditions.

[0071] At step 330 measurements are transmitted to a gateway and / or to the sensor application 125 associated with the cloud 130 using low-power long-range protocols. The sensors 105 (or Gateways) upload data to the cloud for processing at step 335. This step can include advanced error-checking and encryption which are incorporated to ensure data integrity and security.

[0072] At step 340, the system 100 then generates reports which can include raw data, as well as metrics indicative of fire risk. The metrics can be localized and can provide area specific risk data. The method ends at 345.

[0073] Aspects of the embodiments disclosed herein include direct in situ measurement. The system measures the moisture of actual wildland fuels, eliminating errors introduced by standardized, aging, or degraded reference materials. In addition, the disclosed embodiments can provide continuous or frequent measurements which allow for near-realtime updates on moisture levels, critical for rapidly evolving fire conditions and immediate fire management responses.

[0074] The embodiments are configured to provide a scalable network architecture. The design supports dense sensor networks, enabling a fine-resolution moisture map of the forest floor.

[0075] Embodiments further include integrated meteorological data fusion by combining locally measured meteorological data (including wind speed) with broader weather datasets in the cloud, the system enhances situational awareness and model accuracy for wildfire risk assessments.

[0076] The disclosed embodiments are further designed for long-term reliability. Sensors can include ruggedized electrode coatings, protective housings, and remote, wireless operation minimize maintenance overhead and labor costs.

[0077] The disclosed embodiments provide enhanced accuracy. By correlating impedance measurements directly with moisture content in a variety of fuel species and fusing meteorological data, the system delivers more representative readings than indirect models. Likewise, the disclosed systems enjoy low operation cost. The elimination of consumablereferences and labor-intensive measurements lowers long-term expenses. Finally, the embodiments provide versatility across environmental gradients. The flexible architecture adapts to different ecosystem types, climates, and topographies, supporting broad applicability in wildfire management and wildfire research.

[0078] FIG. 4 provides a block diagram a network topology 400 for the system 100 in greater detail, in accordance with an embodiment. In the depicted topology 400, a plurality of sensors 105 are deployed across a wildland environment 110. Each sensor 105 communicates wirelessly with one or more gateways, illustrated as gateway 405 and gateway 410, via low-power, long-range wireless links 415. As illustrated, the gateway 405 is configured for LoRaWAN, DECT NR+, or other such wireless protocols. Gateway 410 is configured for Cellular loT protocols such as LTE-M or NB-loT, BLE mesh, or the like. The gateway 405 and the gateway 410 act as intermediaries, receiving data packets from the sensors 105 and forwarding them to the cloud platform 130 over a wide area network or internet connection 415.

[0079] The cloud platform 130 processes the aggregated sensor data and makes it available to one or more client applications 120 via an API 135. This topology enables scalable, large-area deployments in which thousands of sensors can transmit data through a smaller number of strategically placed gateways, providing coverage across rugged, forested terrains where direct sensor-to-cloud communication may not be feasible.

[0080] It should be appreciated that, in other embodiments, the sensors 105 can communicate directly with satellite 420 or satellite constellations, cellular base stations, or other Non-Terrestrial Network (NTN) infrastructure without the use of intermediate gateway 405 or gateway 410. In such embodiments, each sensor 105 includes a communication module 220 configured to transmit data directly to a satellite or cellular endpoint (e.g., via Cellular loT protocols such as LTE-M or NB-loT, or via NTN links), which in turn relays the data to the cloud platform 130. This gateway-less architecture is particularly advantageous in extremely remote or inaccessible wildland areas where the installation and maintenance of terrestrial gateways is impractical. It should be appreciated that the system 100 can employ a combination of gateway-based and direct-to-infrastructure communication paths across a single deployment, with different sensor nodes 105 configured for different communication modes depending on location, terrain, and available network coverage.

[0081] FIG. 5 illustrates a cloud data processing pipeline 500, in accordance with the disclosed embodiments. Raw impedance data 505, received from the plurality of sensors 105, is processed by a calibration module 510 that applies species-specific calibration curves derived from initial field calibration against oven-dry reference measurements.

[0082] A temperature compensation module 515 then adjusts the calibrated data based on locally measured temperature and other environmental parameters (e.g., humidity and fuel density).

[0083] A meteorological fusion module 520 integrates locally measured meteorological data 522 (e.g., wind speed from on-site sensors 230) along with external meteorological data 524 obtained from weather stations and other external sources, to refine the moisture estimate.

[0084] The pipeline produces moisture content determinations 530 that are passed to an alert engine 535, which generates notifications when moisture levels cross user-defined critical thresholds, and to a spatial map generator 540, which produces fine-resolution moisture maps across the sensor deployment area. The processed data and outputs can be further integrated into predictive wildfire risk models 545 and made available to stakeholders through the client application 120.

[0085] FIG. 6 illustrates a cross-sectional detail view 600 of an exemplary sensor 105, with the sensor electrodes 205 deployed on a fuel sample 605, in accordance with the disclosed embodiments. The electrodes 205, which extend from the sensor housing 225, are inserted into a naturally occurring piece of dead wood or live vegetation, which are examples of a fuel sample 605. The electrode 205 configuration defines an electrode spacing 610 between the at least two electrodes 205, and a penetration depth 615 into the fuel sample 605. The spacing 610 and depth 615 are selected to ensure stable contact with the fuel's internal structure 620 and representative sampling of the fuel's internal moisture content. By inserting the electrodes 205 directly into naturally occurring fuels — rather than into a reference dowel or proxy material — the impedance measurements reflect the actual moisture state of wildland fuels 605 in situ.

[0086] FIG. 7 illustrates an exemplary dashboard visualization 700 which can be displayed by the client application 120, in accordance with the disclosed embodiments. The dashboard 700 includes a spatial moisture map 710 depicting the geographic distribution of sensor 105 locations across a deployment area, with each sensor 105 location rendered with a color-coded indicator reflecting its most recent moisture content reading. Regions with moisture levels below a critical threshold are visually highlighted with alert indicators 720.

[0087] The dashboard 700 can further include temporal trend displays 730 showing moisture changes over time for selected sensors 105 or regions, enabling stakeholders to observe drying trends and anticipate elevated fire risk conditions

[0088] The dashboard 700 can also provide integration points 740 for external wildfire forecasting tools, allowing the fused sensor and meteorological data to be exported or visualized alongside broader wildfire risk models.

[0089] In an embodiment, a system for monitoring dead and live fuel moisture in wildland environments, comprises a plurality of wireless sensors, each of the plurality of wireless sensors comprising a housing, at least two corrosion-resistant electrodes, and a communication module configured to transmit data wirelessly from the sensor via low-power communication protocols, wherein each of the plurality of sensors is configured to measure electrical impedance, and a cloud platform for receiving, processing, and visualizing moisture data in real time. As used herein, "real-time" refers to intervals sufficient to capture relevant environmental changes, such as, but not limited to, every 15 minutes, hourly, or other periodic intervals, rather than strictly sub-second latency.

[0090] In an embodiment, a system for monitoring dead and live fuel moisture in wildland environments, comprises a plurality of wireless sensors, each configured with corrosionresistant electrodes for insertion into naturally occurring dead wood or live vegetation, the sensors measuring electrical impedance at multiple frequencies to determine moisture content, a cloud platform for receiving, processing, and visualizing said moisture data in real time, a communication module configured to transmit data wirelessly from sensors to the cloud platform over long-range, low-power communication protocols; temperature compensation and environmental calibration algorithms integrated into the system to adjust for variables such as ambient temperature, fuel species, fuel density, and local wind speed.In an embodiment, each sensor comprises an impedance measurement unit, a low-power microcontroller, a wireless transceiver, an energy management subsystem designed for extended battery life, and a local meteorological sensor for wind speed and environmental conditions. In an embodiment, the sensors measure impedance at a range of frequencies optimized through calibration, thereby enhancing the accuracy of moisture content determination. In an embodiment, the cloud platform includes analytics tools for visualizing temporal and spatial moisture trends, generating alerts when moisture levels cross critical thresholds, and integrating data into predictive wildfire risk models by fusing local sensor and meteorological data. As used herein, "real-time" refers to intervals sufficient to capture relevant environmental changes, such as, but not limited to, every 15 minutes, hourly, or other periodic intervals, rather than strictly sub-second latency.

[0091] In an embodiment, a method for measuring dead and live fuel moisture in wildland environments, comprises deploying a network of wireless sensors configured to measure electrical impedance directly from naturally occurring dead wood or live vegetation, transmitting sensor data to a cloud platform using low-power, long-range wireless communication protocols, processing the data on the cloud platform to determine moisture content, applying species-specific calibration curves, temperature compensation, wind speed corrections, and other environmental adjustments, and visualizing processed moisture data for stakeholders, providing real-time alerts, trend analyses, and integration with external wildfire forecasting tools. In an embodiment, the method further comprises calibrating the sensors by comparing impedance measurements against oven-dry reference values for various fuel species to improve accuracy and reliability.

[0092] FIGS. 8-10 are provided as exemplary diagrams of data-processing environments in which embodiments of the present invention may be implemented. It should be appreciated that FIGS. 8-10 are only exemplary and are not intended to assert or imply any limitation with regard to the environments in which aspects or embodiments of the disclosed embodiments may be implemented. Many modifications to the depicted environments may be made without departing from the spirit and scope of the disclosed embodiments.

[0093] A block diagram of a computer system 800 that executes programming for implementing parts of the methods and systems disclosed herein is shown in FIG. 8. Acomputing device in the form of a computer 810 configured to interface with sensors, peripheral devices, and other elements disclosed herein may include one or more processing units 802, memory 804, removable storage 812, and non-removable storage 814. Memory 804 may include volatile memory 806 and non-volatile memory 808. Computer 810 may include, or have access to, a computing environment that includes a variety of transitory and non-transitory computer-readable media such as volatile memory 806 and non-volatile memory 808, removable storage 812 and non-removable storage 814. Computer storage includes, for example, random access memory (RAM), read only memory (ROM), erasable programmable read-only memory (EPROM) and electrically erasable programmable readonly memory (EEPROM), flash memory or other memory technologies, compact disc readonly memory (CD ROM), Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage, or other magnetic storage devices, or any other medium capable of storing computer-readable instructions as well as data including image data.

[0094] Computer 810 may include or have access to a computing environment that includes input 816, output 818, and a communication connection 820. The computer may operate in a networked environment using a communication connection 820 to connect to one or more remote computers, remote sensors, detection devices, hand-held devices, multifunction devices (MFDs), mobile devices, tablet devices, mobile phones, Smartphones, or other such devices. The remote computer may also include a personal computer (PC), server, router, network PC, RFID enabled device, a peer device or other common network node, or the like. The communication connection may include a Local Area Network (LAN), a Wide Area Network (WAN), Bluetooth connection, or other networks. This functionality is described more fully in the description associated with FIG. 9 below.

[0095] Output 818 is most commonly provided as a computer monitor, but may include any output device. Output 818 and / or input 816 may include a data collection apparatus associated with computer system 800. In addition, input 816, which commonly includes a computer keyboard and / or pointing device such as a computer mouse, computer track pad, or the like, allows a user to select and instruct computer system 800. A user interface can be provided using output 818 and input 816. Output 818 may function as a display for displaying data and information for a user, and for interactively displaying a graphical user interface(GUI) 830.

[0096] Note that the term “GUI” generally refers to a type of environment that represents programs, files, options, and so forth by means of graphically displayed icons, menus, and dialog boxes on a computer monitor screen. A user can interact with the GUI to select and activate such options by directly touching the screen and / or pointing and clicking with a user input device 816 such as, for example, a pointing device such as a mouse and / or with a keyboard. A particular item can function in the same manner to the user in all applications because the GUI provides standard software routines (e.g., module 825) to handle these elements and report the user’s actions.

[0097] Computer-readable instructions, for example, program module or node 825, which can be representative of other modules or nodes described herein, are stored on a computer-readable medium and are executable by the processing unit 802 of computer 810. Program module or node 825 may include a computer application. A hard drive, CD-ROM, RAM, Flash Memory, and a USB drive are just some examples of articles including a computer-readable medium.

[0098] FIG. 9 depicts a graphical representation of a network of data-processing systems 900 in which aspects of the present invention may be implemented. Network data-processing system 900 is a network of computers or other such devices such as mobile phones, smartphones, sensors, detection devices, and the like in which embodiments of the present invention may be implemented. Note that the system 900 can be implemented in the context of a software module such as program module 825. The system 900 includes a network 902 in communication with one or more clients 910, 912, and 914, and external device 905. Network 902 may also be in communication with one or more external devices, including but not limited to RFID and / or GPS enabled devices or sensors 904, servers 906, and storage 908. Network 902 is a medium that can be used to provide communications links between various devices and computers connected together within a networked data processing system such as computer system 800. Network 902 may include connections such as wired communication links, wireless communication links of various types, fiber optic cables, quantum, or quantum encryption, or quantum teleportation networks, etc. Network 902 can communicate with one or more servers 906, one or more external devices such as RFID and / or GPS enabled device 904, and a memory storage unit such as, for example, memoryor database 908. It should be understood that external device 904 may be embodied as a mobile device, cell phone, tablet device, monitoring device, detector device, sensor microcontroller, controller, receiver, transceiver, or other such device.

[0099] In the depicted example, external device 904, server 906, and clients 910, 912, and 914 connect to network 902 along with storage unit 908. Clients 910, 912, and 914 may be, for example, personal computers or network computers, handheld devices, mobile devices, tablet devices, smartphones, personal digital assistants, microcontrollers, recording devices, MFDs, etc. Computer system 800 depicted in FIG. 8 can be, for example, a client such as client 910 and / or 912.

[0100] Computer system 800 can also be implemented as a server such as server 906, depending upon design considerations. In the depicted example, server 906 provides data such as boot files, operating system images, applications, and application updates to clients 910, 912, and / or 914. Clients 910, 912, and 914 and external device 904 are clients to server 906 in this example. Network data-processing system 900 may include additional servers, clients, and other devices not shown. Specifically, clients may connect to any member of a network of servers, which provide equivalent content.

[0101] In the depicted example, network data-processing system 900 is the Internet with network 902 representing a worldwide collection of networks and gateways that use the Transmission Control Protocol / lnternet Protocol (TCP / IP) suite of protocols to communicate with one another. At the heart of the Internet is a backbone of high-speed data communication lines between major nodes or host computers consisting of thousands of commercial, government, educational, and other computer systems that route data and messages. Of course, network data-processing system 900 may also be implemented as a number of different types of networks such as, for example, an intranet, a local area network (LAN), or a wide area network (WAN). FIGS. 8 and 9 are intended as examples and not as architectural limitations for different embodiments of the present invention.

[0102] FIG. 10 illustrates a software system 1000, which may be employed for directing the operation of the data-processing systems such as computer system 800 depicted in FIG.8. Software application 1005, may be stored in memory 804, on removable storage 812, or on non-removable storage 814 shown in FIG. 8, and generally includes and / or is associatedwith a kernel or operating system 1010 and a shell or interface 1015. One or more application programs, such as module(s) or node(s) 825, may be “loaded” (i.e., transferred from removable storage 814 into the memory 804) for execution by the data-processing system 800. The data-processing system 800 can receive user commands and data through user interface 1015, which can include input 816 and output 818, accessible by a user 1020. These inputs may then be acted upon by the computer system 800 in accordance with instructions from operating system 1010 and / or software application 1005 and any software module(s) 825 thereof.

[0103] Generally, program modules (e.g., module 825) can include, but are not limited to, routines, subroutines, software applications, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types and instructions. Moreover, those skilled in the art will appreciate that elements of the disclosed methods and systems may be practiced with other computer system configurations such as, for example, hand-held devices, mobile phones, smart phones, tablet devices, multiprocessor systems, printers, copiers, fax machines, multi-function devices, data networks, microprocessor-based or programmable consumer electronics, networked personal computers, minicomputers, mainframe computers, servers, and the like.

[0104] Note that the term module or node as utilized herein may refer to a collection of routines and data structures that perform a particular task or implements a particular abstract data type. Modules may be composed of two parts: an interface, which lists the constants, data types, variables, and routines that can be accessed by other modules or routines; and an implementation, which is typically private (accessible only to that module), and which includes source code that actually implements the routines in the module. The term module may also simply refer to an application such as a computer program designed to assist in the performance of a specific task such as word processing, accounting, inventory management, etc., or a hardware component designed to equivalently assist in the performance of a task.

[0105] The interface 1015 (e.g., a graphical user interface 830) can serve to display results, whereupon a user 1020 may supply additional inputs or terminate a particular session. In some embodiments, operating system 1010 and GUI 830 can be implemented in the context of a “windows” system. It can be appreciated, of course, that other types of systems are possible. For example, rather than a traditional “windows” system, otheroperation systems such as, for example, a real time operating system (RTOS) more commonly employed in wireless systems may also be employed with respect to operating system 1010 and interface 1015. The software application 1005 can include, for example, module(s) 825, which can include instructions for carrying out steps or logical operations such as those shown and described herein.

[0106] The following description is presented with respect to embodiments of the present invention, which can be embodied in the context of, or require the use of a data-processing system such as computer system 800, in conjunction with program module 825, and data-processing system 900 and network 902 depicted in FIGS. 8-10. The present invention, however, is not limited to any particular application or any particular environment. Instead, those skilled in the art will find that the systems and methods of the present invention may be advantageously applied to a variety of system and application software including database management systems, word processors, and the like. Moreover, the present invention may be embodied on a variety of different platforms including Windows, Macintosh, UNIX, LINUX, Android, Arduino and the like. Therefore, the descriptions of the exemplary embodiments, which follow, are for purposes of illustration and not considered a limitation.

[0107] Based on the foregoing, it can be appreciated that a number of embodiments, preferred and alternative, are disclosed herein. In an embodiment, a system for monitoring fuel moisture comprises a plurality of wireless sensors, each of the plurality of wireless sensors comprising a housing, at least two electrodes configured to be inserted into organic matter, an impedance measurement circuit configured to measure electrical impedance at one or more frequencies, a communication module configured to transmit data wirelessly from each of the plurality of wireless sensors, and a cloud platform configured for receiving impedance data from the plurality of wireless sensors, processing the impedance data to determine moisture content data, and visualizing the moisture content data. In an embodiment, the plurality of wireless sensors measure impedance at a range of frequencies optimized through calibration. In an embodiment at least one of the plurality of wireless sensors further comprises a local meteorological sensor for measuring at least one of wind speed and environmental conditions. In an embodiment, the system for monitoring fuel moisture further comprises at least one gateway acting as an intermediary between the plurality of wireless sensors and the cloud platform, the at least one gateway configured to2Q / 37receive data packets from the plurality of wireless sensors and forward the data packets to the cloud platform. In an embodiment, the communication module is configured to transmit data using at least one protocol selected from the group comprising LoRaWAN, NonTerrestrial Networks (NTN), DECT NR+, Cellular loT protocols, and Bluetooth Low Energy (BLE) mesh networking. In an embodiment, at least one of the plurality of wireless sensors further comprises an energy harvesting module. In an embodiment, each of the plurality of wireless sensors is configured with periodic wake cycles that perform impedance measurements at predefined intervals. In an embodiment, the cloud platform further comprises an analytics tool configured to generate alerts when moisture levels cross critical thresholds and to integrate the moisture data into predictive wildfire risk models by fusing local sensor data and meteorological data.

[0108] In an embodiment, a method for measuring fuel moisture comprises deploying a network of wireless sensors configured to measure electrical impedance directly from naturally occurring dead wood or live vegetation, transmitting sensor data collected by the network of wireless sensors to a cloud platform, processing the data on the cloud platform to determine moisture content, applying at least one environmental adjustment to generate processed moisture data, the at least one environmental adjustment selected from a group comprising species-specific calibration curves, temperature compensation, wind speed corrections, humidity compensation, and fuel density adjustments, and visualizing the processed moisture data. In an embodiment, the method for measuring fuel moisture further comprises calibrating the network of wireless sensors by comparing impedance measurements against reference values for various fuel species. In an embodiment, measuring electrical impedance further comprises measuring impedance at multiple frequencies, wherein higher frequencies are sensitive to bound water in fuel and lower frequencies capture bulk moisture effects. In an embodiment, the method for measuring fuel moisture further comprises generating alerts when moisture levels cross critical thresholds. In an embodiment, the method for measuring fuel moisture further comprises integrating the processed moisture data with external wildfire forecasting tools. In an embodiment, the method for measuring fuel moisture further comprises generating a spatial moisture map associated with the network of wireless sensors. In an embodiment, the method for measuring fuel moisture further comprises fusing locally measured meteorological data comprising wind speed with external weather datasets.

[0109] In an embodiment, a sensor apparatus for measuring moisture content of fuel comprises a housing, at least two electrodes configured for insertion into naturally occurring dead wood or live vegetation, an impedance measurement circuit calibrated for one or more frequencies, a microcontroller, a wireless communication module, and a battery module. In an embodiment, each of the at least two electrodes comprise at least one of stainless steel and corrosion resistant alloys. In an embodiment, a relative position between each of the at least two electrodes is selected with spacing and depth for representative sampling of internal fuel moisture. In an embodiment, the sensor apparatus for measuring moisture content of fuel further comprises an energy harvesting module. In an embodiment, the sensor apparatus for measuring moisture content of fuel further comprises a local meteorological sensor for measuring wind speed and environmental conditions.

[0110] In an embodiment, a system for monitoring dead and live fuel moisture in wildland environments, comprises a plurality of wireless sensors, each of the plurality of wireless sensors comprising: a housing; at least two corrosion-resistant electrodes configured to be inserted into naturally occurring dead wood or live vegetation; an impedance measurement circuit configured to measure electrical impedance at one or more frequencies; a communication module configured to transmit data wirelessly from the sensor via low-power communication protocols; and a cloud platform configured to receive impedance data from the plurality of wireless sensors, process the impedance data to determine moisture content, and visualize moisture data in real time. In an embodiment, the plurality of wireless sensors measure impedance at a range of frequencies optimized through calibration, thereby enhancing the accuracy of moisture content determination. In an embodiment, at least one of the plurality of wireless sensors further comprises a local meteorological sensor for measuring wind speed and environmental conditions. In an embodiment, the system further comprises one or more gateways acting as intermediaries between the plurality of wireless sensors and the cloud platform, the one or more gateways configured to receive data packets from the plurality of wireless sensors and forward the data packets to the cloud platform. In an embodiment, the system further comprises a communication module configured to transmit data using at least one protocol selected from the group consisting of LoRaWAN, Non-Terrestrial Networks (NTN), DECT NR+, Cellular loT protocols (e.g., LTE-M, NB-loT), and Bluetooth Low Energy (BLE) mesh networking. In an embodiment, at least one of theplurality of wireless sensors further comprises a solar or kinetic energy harvesting module. In an embodiment, each of the plurality of wireless sensors is configured with periodic wake cycles that perform impedance measurements at predefined intervals. In an embodiment, the cloud platform comprises analytics tools configured to generate alerts when moisture levels cross critical thresholds and to integrate the moisture data into predictive wildfire risk models by fusing local sensor data and meteorological data.

[0111] In an embodiment, a method for measuring dead and live fuel moisture in wildland environments, comprises deploying a network of wireless sensors configured to measure electrical impedance directly from naturally occurring dead wood or live vegetation; transmitting sensor data to a cloud platform using low-power, long-range wireless communication protocols; processing the data on the cloud platform to determine moisture content, applying at least one environmental adjustment selected from the group consisting of species-specific calibration curves, temperature compensation, wind speed corrections, humidity compensation, and fuel density adjustments; and visualizing processed moisture data for stakeholders. In an embodiment, the method comprises calibrating the network of wireless sensors by comparing impedance measurements against oven-dry reference values for various fuel species. In an embodiment, measuring electrical impedance comprises measuring impedance at multiple frequencies, wherein higher frequencies are sensitive to bound water in fuel and lower frequencies capture bulk moisture effects. In an embodiment, the method comprises generating alerts when moisture levels cross critical thresholds. In an embodiment, the method comprises integrating the processed moisture data with external wildfire forecasting tools. In an embodiment, the method comprises generating spatial moisture maps across the network of wireless sensors. In an embodiment, the method comprises fusing locally measured meteorological data including wind speed with external weather datasets to enhance the accuracy of the moisture content determination.

[0112] In an embodiment, a sensor apparatus for measuring moisture content of dead or live fuel in a wildland environment, comprises a housing; at least two corrosion-resistant electrodes configured for insertion into naturally occurring dead wood or live vegetation; an impedance measurement circuit calibrated for one or more frequencies that optimize moisture detection; a low-power microcontroller; a wireless communication module; and a battery module. In an embodiment, the at least two corrosion-resistant electrodes comprisestainless steel or corrosion-resistant alloys, and wherein an electrode configuration defines spacing and depth for representative sampling of internal fuel moisture. In an embodiment, the sensor apparatus further comprises a solar or kinetic energy harvesting module. In an embodiment, the sensor apparatus further comprises a local meteorological sensor for measuring wind speed and environmental conditions.

[0113] In an embodiment, a computer-implemented method for determining fuel moisture content in wildland environments, comprises receiving, at a cloud platform, impedance data measured at one or more frequencies from a plurality of wireless sensors deployed on naturally occurring dead wood or live vegetation in a wildland environment; applying speciesspecific calibration curves to the impedance data; performing temperature compensation on the impedance data; fusing meteorological data from local sensor measurements with meteorological data obtained from external sources; and generating moisture content determinations based on the calibrated, compensated, and fused data.

[0114] It should be appreciated that variations of the above-disclosed and other features and functions, or alternatives thereof, may be desirably combined into many other different systems or applications. It should be understood that various presently unforeseen or unanticipated alternatives, modifications, variations, or improvements therein may be subsequently made by those skilled in the art which are also intended to be encompassed by the following claims.

Claims

CLAIMSWhat is claimed is:

1. A system for monitoring fuel moisture, comprising:a plurality of wireless sensors, each of the plurality of wireless sensors comprising:a housing;at least two electrodes configured to be inserted into organic matter;an impedance measurement circuit configured to measure electrical impedance at one or more frequencies;a communication module configured to transmit data wirelessly from each of the plurality of wireless sensors; anda cloud platform configured for:receiving impedance data from the plurality of wireless sensors; processing the impedance data to determine moisture content data; and visualizing the moisture content data.

2. The system for monitoring fuel moisture of claim 1 , wherein the plurality of wireless sensors measure impedance at a range of frequencies optimized through calibration.

3. The system for monitoring fuel moisture of claim 1 , wherein at least one of the plurality of wireless sensors further comprises:a local meteorological sensor for measuring at least one of:wind speed; andenvironmental conditions.

4. The system for monitoring fuel moisture of claim 1 further comprising:at least one gateway acting as an intermediary between the plurality of wireless sensors and the cloud platform, the at least one gateway configured to receive data packets from the plurality of wireless sensors and forward the data packets to the cloud platform.

5. The system for monitoring fuel moisture of claim 1 wherein the communication module is configured to transmit data using at least one protocol selected from a group comprising:LoRaWAN;Non-Terrestrial Networks (NTN);DECT NR+;Cellular loT protocols; andBluetooth Low Energy (BLE) mesh networking.

6. The system for monitoring fuel moisture of claim 1 wherein at least one of the plurality of wireless sensors further comprises an energy harvesting module.

7. The system for monitoring fuel moisture of claim 1 , wherein each of the plurality of wireless sensors is configured with periodic wake cycles that perform impedance measurements at predefined intervals.

8. The system for monitoring fuel moisture of claim 1, wherein the cloud platform further comprises:an analytics tool configured to generate alerts when moisture levels cross critical thresholds and to integrate moisture data into predictive wildfire risk models by fusing local sensor data and meteorological data.

9. A method for measuring fuel moisture, comprising:deploying a network of wireless sensors configured to measure electrical impedance directly from naturally occurring dead wood or live vegetation;transmitting sensor data collected by the network of wireless sensors to a cloud platform;processing the sensor data on the cloud platform to determine moisture content; applying at least one environmental adjustment to generate processed moisture data, the at least one environmental adjustment selected from a group comprising:species-specific calibration curves;temperature compensation;wind speed corrections;humidity compensation; andfuel density adjustments; andvisualizing the processed moisture data.

10. The method for measuring fuel moisture of claim 9 further comprising:calibrating the network of wireless sensors by comparing impedance measurements against reference values for various fuel species.

11. The method for measuring fuel moisture of claim 9, wherein measuring electrical impedance further comprises:measuring impedance at multiple frequencies, wherein higher frequencies are sensitive to bound water in fuel and lower frequencies capture bulk moisture effects.

12. The method for measuring fuel moisture of claim 9, further comprising:generating alerts when moisture levels cross critical thresholds.

13. The method for measuring fuel moisture of claim 9 further comprising:integrating the processed moisture data with external wildfire forecasting tools.

14. The method for measuring fuel moisture of claim 9, further comprising:generating a spatial moisture map associated with the network of wireless sensors.

15. The method for measuring fuel moisture of claim 9 further comprising:fusing locally measured meteorological data comprising wind speed with external weather datasets.

16. A sensor apparatus for measuring moisture content of fuel comprising:a housing;at least two electrodes configured for insertion into naturally occurring dead wood or live vegetation;an impedance measurement circuit calibrated for one or more frequencies;a microcontroller;a wireless communication module; anda battery module.

17. The sensor apparatus for measuring moisture content of fuel of claim 16, wherein each of the at least two electrodes comprise at least one of:stainless steel, andcorrosion resistant alloys.

18. The sensor apparatus for measuring moisture content of fuel of claim 16, wherein a relative position between each of the at least two electrodes is selected with spacing and depth for representative sampling of internal fuel moisture.

19. The sensor apparatus for measuring moisture content of fuel of claim 16, further comprising:an energy harvesting module.

20. The sensor apparatus for measuring moisture content of fuel of claim 16, further comprising:a local meteorological sensor for measuring wind speed and environmental conditions.

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