Early Wildfire Detection using RSSI

Research Project for CPSC-855∗

Adam HodgesClemson University

School of ComputingClemson, USA

hodges8@clemson.edu

Ravi MandliyaClemson University

School of ComputingClemson, USA

rmandli@clemson.edu

ABSTRACTEarly prediction and detection of wildfires can have an enor-mous impact on the rate at which they can be suppressedand extinguished. Current methods of detection include hu-man monitoring of satellite imagery and video feeds, hot-line reporting and wireless sensor networks (WSNs). TheseWSNs use temperature, humidity, pressure, and gas sen-sor data to detect the occurrence and spreading of wildfires.Currently only gas sensor data provides for the automatedearly detection of wildfires. In this paper we introduce anovel approach for the early detection of wildfires by moni-toring the RSSI fluctuations between nodes in a WSN. Thisnew technique improves the accuracy, speed, and range ofwildfire detection without the use of additional sensor hard-ware.

Categories and Subject DescriptorsC.3 [Special Purpose and Application-BasedSystems]: Real-time and Embedded Systems; D.3.4[Hardware]: Sensor Network and Deployment

General TermsSensor Networks, Experimentation

KeywordsMotes, Wildfires, Wireless Sensor Networks, RSSI, Radio

1. INTRODUCTIONWildfires are one of the most devastating environmental dis-asters that occur worldwide, inflicting loss of billions of dol-lars [1] and precious vegetation and wildlife. These disasterscan be prevented if they are detected and suppressed in theearly stages. There have been many attempts over the yearsto detect wildfires. The most primitive techniques includethe use of a lookout tower positioned at a high point andground patrols. Some of the more sophisticated techniquesthat have been employed include satellite imaging [17] andthe sensing of emissions by airborne craft. All of these tech-niques require human observers and are therefore subject tohuman error as well as high cost and low availability[19].

Wireless sensor networks (WSNs) have been introduced as away to autonomously detect wildfires. WSNs provide manybenefits including low cost, large coverage, and quicknessof detection. Currently there are three main categories ofwildfire detection used in WSNs:

∗Submitted as the final project of the course

• Gas sensing[18, 16]

• Environmental parameters sensing (tempera-ture/humidity/light)[9, 13, 21, 8]

• Video monitoring[12, 5]

Of the above categories, only the technique of gas sensingpossesses the innate ability to autonomously detect wildfiresnot in the immediate proximity of the sensor [19]. Thermalimaging using a network of infrared cameras is another tech-nique that provides for the early detection of wildfires, butthis technique requires post-processing of live video which isvery computationally expensive.

A bushfire plume consists of partially ionized gas, whichis considered to be conductive. The ionized electrons inthe plume cause high levels of dispersion and attenuation inelectromagnetic waves[6, 3]. In an attempt to further under-stand the impacts of fire on firefighter radio communications,the authors in [4] have investigated the effect of fire whenpresent in the signal propagation path of 802.15.4 wirelesscommunications. The researchers found that this physicalproperty of fire has a measurable effect on RSSI and LQIvalues of radio signals in 2.4GHz channels. It was observedthat the measurements of these two values are indeed lowerin the presence of fire. The fire that they experimented withwas 5-10cm in height, and it was noted that the size of thefire has an impact on the amount by which the signal de-grades. In this paper, we propose a novel technique for theearly detection of wildfires using received signal strength in-dication (RSSI) of the radio links in a WSN.

The remainder of the paper is organized as follows. In Sec-tion 2, we discuss existing methods used to monitor wildfiresusing WSNs. In Section 3 we will explain our experimentalsetup. In Section 4, we present our results. In Section 5, wediscuss the results of our experiment which explains impactof fire on RSSI values of radio communication. In Section 6,we evaluate the feasibility of using RSSI as a indicator forwildfire detection and investigate the use of a combinationof this new technique with current ones, improving the earlydetection of wildfires in WSNs. In Section 7 we conclude ourwork.

2. BACKGROUNDThe use of gas sensors is one of the most promising meth-ods of autonomous early wildfire detection in wireless sensor

networks. These sensors work by detecting gasses producedduring the early stages of pyrolysis. This can allow for de-tection of fires before combustion occurs, giving the earliestpossible warning of wildfires. The researchers in [18] aimedto address the issue of the high power consumption by thesegas sensors. They outlined an energy harvesting strategy,as well as a test deployment in which they confirmed theaccuracy of their sensing algorithm. Gas sensors provide areliable method of early fire detection but their usefulness inreal-world wildfire monitoring WSN deployments is limitedby their high power consumption [16].

The infrared signature created by fires is also able to be de-tected by the motes used in most WSN deployments. In[12], this was used in conjunction with gas sensing to fire analarm upon the detection of fire. The concept of fire veri-fication was introduced to improve the accuracy of fire de-tection. Video cameras were deployed overlooking the WSNto verify the fire alarms. These techniques tend to rely online-of-sight, and may require a dense sensor deployment.Although the devices in this experiment were successfullypowered by a combination of solar panels and batteries, theuse of 802.11g networks and gas sensors resulted in a highlevel of power consumption that limits the scalability of thisapproach.

The experimenters in [11] utilized an artificial neural net-work to improve the accuracy of the detection of wildfires.Each mote in this neural network used a data fusion algo-rithm to process temperature, humidity, infrared and vis-ible light data. An alarm decision based on these criteriawas then reported to a base station. This system was im-plemented and tested using TelosB motes. The result wasan accurate self-learning alarm system. A limitation of thisdesign was that the detection accuracy was less than 40%when the motes were more than 30 cm from the fire. Theexperimenters made note that other fire indicators, such assensing the products of combustion with a gas sensor couldbe used with the neural network to improve the accuracyand provide for better early wildfire detection.

As far as we know, the radio link communication parameterslike RSSI and LQI have not been used to detect wildfires.

In this paper, we use the concept of a fresnel zone to ex-plain the relationship observed between the obstruction ofthe flame, link distance, and impact observed on RSSI. Afresnel zone is a long ellipsoid spanning between two an-tennas that can be used to describe the line-of-sight path inradio communications. The general rule of thumb is that forproper communication to take place, 60% of the first fresnelzone must be clear of obstructions [7].

The formula for fresnel zone radius at a point P depends on

• n the fresnel zone number (1)

• λ the wavelength of the signal

• The distances d1, d2 from the respective nodes to thepoint P

Figure 1: Scenario 2 Trial

The equation is the following:

Fn =

√nλd1d2d1 + d2

3. EXPERIMENTAL SETUPTo study the impact of fire on RSSI, we conducted exper-iments using Crossbow TelosB motes. In order to applythe findings in [4] to the area of early wildfire detection,we needed to conduct a similar experiment at a larger scale.Our experiment necessitated a larger fire as well as a greaterdistance between motes.

3.1 Preliminary TestingWe have conducted four experimental trials in three differentscenarios.

3.1.1 Scenario 1The first scenario was in an apartment complex with a publicfire place. The software used in this scenario was a modifiedversion of PacketParrot, a TinyOS 2.x tutorial app. Thisprogram received radio data, recorded RSSI, timestampedthe packet, and wrote the results to flash for later retrieval.Four motes were placed along a concrete surface in a lineperpendicular to the fire.

The results for the two trials we ran in this scenario werecorrupted due to human interference and poor managementof the flash memory. There was also the issue of inconsis-tent timestamps between motes. The fresnel zones for eachmote were obstructed since the motes were laid flat on theground. The fresnel zones were further obstructed by a brickchimney that was positioned behind the fire. Testing in anapartment complex setting also meant that there were many802.11 networks operating on the same frequency, causinginterference for our RSSI data.

Despite the corruption of the data, we saw visible drops inRSSI during the presence of fire, which motivated us to con-tinue experimentation in a tighter controlled environment.

3.1.2 Scenario 2The second scenario we tried was in a public campgroundwith a fire ring, shown in Figure 1. We redesigned our soft-ware from scratch so it no longer relied on flash memory.All results were relayed to an observer mote that forwarded

the results over serial to a laptop for storage. Interferencefrom cell-phones and laptops was eliminated by disablingthe radios on these devices for the duration of the exper-iment. Motes were placed approximately 4.5 inches abovethe ground, which unfortunately still obstructed part of thefirst fresnel zones. Despite this obstruction, we obtainedvaluable results that show a strong relationship between thepresence of fire and RSSI as seen in Figure 2.

Figure 2: Radio Link Communication between Node 1 and4

Unfortunately, this location was hard to secure due to thepopularity of camping in the spring season. Although thislocation was largely ideal due to reduced radio interference,it was inconvenient due to the requirement of reservationsand fees. The campsite also imposed size restrictions forfires and placement of motes. We decided to design anotherscenario in which the fire was larger and the motes werefurther apart.

3.1.3 Scenario 3Our final scenario location was in a backyard of a residentialarea. Because this yard was private property, the area pro-vided much more flexibility for our testing. We used largestakes to hold our motes above the ground, and a barrel toraise the fire up. This ensured that the fresnel zones of themotes were obstructed only by the fire. We used twice asmuch wood in this experiment, and doubled the distance be-tween motes. This scenario also allowed for video recordingof fire height. The remainder of this paper will pertain toresults obtained from this experimental scenario.

3.2 Experiment DesignFor our experiment, we used off the shelf TelosB motes.These motes have Texas Instruments CC2420 2.4GHz ra-dio chips that use the IEEE 802.15.4 standard. Interferencefrom 802.11 networks was negated by our use of channel 26(2480 MHZ), which does not overlap with any of the 802.11channels [20]. We positioned these motes in a straight lineperpendicular to the fire ring at even distances. The fire ringwas 16 inches in diameter. The innermost motes were posi-tioned 4 feet from the fire ring, while the outer motes were8 feet from the fire ring. Each of these motes were placedapproximately 4 feet above the ground to provide adequatefirst fresnel zone clearance [10]. This experimental designis illustrated by Figure 3. Additionally, there was a fifthmote attached to a Linux laptop that was used to observethe results from these four motes during the experiment.

3.3 SoftwareEach mote was programmed using TinyOS 2.1.2. Our soft-ware was written in nesC and consists of two major pro-grams. The first is the Monitor program, which broadcastsan empty 802.15.4 ActiveMessage packet every 500ms. Thisprogram also listens for any packet of the same type. Uponreceiving a packet, it measures the RSSI for the packet andbroadcasts this information to an Observer. The Observerprogram simply listens for any of these broadcasts, times-tamps them upon reception, and forwards them over serialto a listening Java program.

3.4 MethodologyVarious measures were taken in order to conduct the ex-periment with a minimum impact from external sources ofRSSI interference. The only human interference during theexperiment occurred during the lighting and extinguishingof the fire. These times were recorded using timestamps dis-played by the Java program and are shown clearly in ourgraphs. The event times were verified through the use of avideo recording of events. All motes in the experiment weresecured and completely stationary.

A log cabin fire structure was used in our experiment toprovide a reproducible long-lasting flame that would pro-duce sufficient results for analysis. Approximately 1.5 cu ofdry firewood was used to build the fire structure. Data wascollected for 5 minutes before lighting the fire, allowing foran experimental control. The times at which the fire was litwas accurately recorded to ensure the isolation of all possi-ble causes of interference in our data. The flame height wasrecorded throughout the duration of the experiment throughthe use of our video recording. The fire was allowed to burnuntil it died down to a flame of 1 foot, at which time it wasextinguished.

4. RESULTSThe following results show the impact of fire on radio linksduring our third trial. This data was filtered using a centeredmoving average with a window size of 121, which amountsto a window of approximately 1-2 minutes of data. A time-line of events is provided in Table 1. We have provided athorough analysis of these results in the discussion section.

4.1 Link 1-2In Figure 4, the RSSI values show a negative trend in thepresence of a fire. We can see a clear change in RSSI valuesduring all significant events of our time-line on these graphs.Also notice the upward trend in RSSI again when fire wasstifled.

Figure 4: Radio Link Communication between Node 1 and2

Figure 3: Experiment Design

Table 1: Timeline of events

Time Event0 Started observer program

343 seconds Human interference (to light fire)364 seconds Fire was lit397 seconds Flame size 1 feet445 seconds Flame size 3 feet500 seconds Flame size 3.5 feet530 seconds Flame size 4 feet550 seconds Flame size 4 feet565 seconds Flame size 3.5 feet580 seconds Flame size 3 feet632 seconds Flame size 2.5 feet703 seconds Flame size 2 feet840 seconds Known human interference904 seconds Flame size 1 feet1010 seconds Human interference (extinguished)1033 seconds No flame, high smoke1190 seconds Stopped observer program

4.2 Link 1-3In Figure 5, the link between 3 and 4 showed a downwardtrend in RSSI in the presence of fire. This chart reflectsa small level of interference that was observed at approxi-mately 840 seconds due to a known human interference.

Figure 5: Radio Link Communication between Node 1 and3

4.3 Link 1-4In Figure 6, the link between nodes 1 and 4 does not showany significant change in strength in the presence of fire.This particular link was not impacted by the presence offire.

Figure 6: Radio Link Communication between Node 1 and4

4.4 Link 2-3In Figure 7, the link between nodes 2 and 3 was relativelyconsistent in its strength. There was a small drop in averageRSSI values after the ignition of fire, but it is not statisticallysignificant. This particular link was not impacted by thepresence of fire.

Figure 7: Radio Link Communication between Node 2 and3

4.5 Link 2-4Figure 8 represents the link quality between nodes 2 and4 throughout the duration of the experiment. We can ob-serve a drop in measured RSSI as soon as fire reached 4.5feet high. The RSSI curve shows a decreasing trend until680-700 seconds when flame height was 2 feet. As soon asfire flames started to die down the RSSI curve showed anincreasing trend which continued to increase until the firewas completely extinguished.

Table 2: Radius of first fresnel zone for various radio links

Link Fresnel zone radius (ft)Link 2:3 0.97Link 2:4 1.12Link 1:4 1.33

Figure 8: Radio Link Communication between Node 2 and4

4.6 Link 3-4In Figure 9, the presence of fire did not have a significantimpact on the RSSI of this link. This link had a small level ofinterference that was observed at approximately 840 secondsbecause of a known interference.

Figure 9: Radio Link Communication between Node 3 and4

5. DISCUSSIONAn illustration of the first fresnel zones for our experimentalsetup is given in Figure 10. The sizes of these fresnel zoneswere calculated and are included in Table 2. Link 1:4 wasnot impacted by RSSI because the first fresnel zone of thislink had a much larger radius than that of the other links.Increasing the first fresnel zone size while maintaining thesize of the flame results in a lower percentage of obstructionby the fire and thus better reception on this link.

The large temperature gradients and gasses produced byfire affect the refractive index of air. This can cause radioenergy to bend away from the ground, causing a loss of signalstrength [2]. Heating and the turbulent mixing of air, as wellas particles swept up into the air by the fire can introducescattering of the radio signal [15, 4]. As seen in Figure 11,the wind was blowing towards nodes 1 and 2 throughout theduration of the experiment. This resulted in much of theheat and smoke being blown in that direction, effectivelyreducing the radius of the first fresnel zone in links to node2. Therefore we expect to see the fire impacting node 2 morethan node 3. This is reflected in the results we recorded forlinks 1:2 and 2:4. This explains why the variations in RSSI

Figure 11: Photograph illustrating the wind blowing thesmoke towards node 2

Figure 12: The flame as it reached a peak of 4 feet at ap-proximately 540 seconds

measured for links 1:2 and 2:4 were greater than that in link1:3 and 3:4.

As the link between nodes 2 and 3 had the smallest firstfresnel zone, it was expected to be the one most negativelyimpacted by the fire. The graph of link 2:3 in Figure 7 showsthat the fire had an impact on the RSSI, but it was nowherenear as significant as expected. This is due to the fact thatcommunicating motes transmitting at a high power in closeproximity are not as susceptible to interference.

The highest flame recorded was approximately 4 feet as seenin Figure 12. The links that were impacted most by the pres-ence of fire (links 1:2, 2:4, 2:3, 1:3) all showed a strong neg-ative trend that corresponded with the instant in which weobserved the maximum flame height. This negative trendwas consistent across all links, and did not completely re-verse until the flame was extinguished. The flame as seen atits lowest point was recorded in Figure 13.

The packet reception rates calculated for these links were ar-tificially low due to our software design. The effective packetreception rate was approximately 60% for all links, as shownin Table 3. The observer received packets in bursts, someof which were dropped while the mote was busy forwardingthe previous packet over serial. This flaw could be solved byimplementing a queue of received packets at the observer.

Figure 10: We illustrate some of the fresnel zones that exist in our experiment. We have omitted some of the overlappingzones for clarity.

Figure 13: The fire as it appeared at 1100 seconds, immedi-ately prior to extinguishing

Table 3: Packet reception rate for all links

Period Packet Reception RateControl 55.60

During fire 57.71Overall 55.68

Although we suspect a strong correlation, our high packetloss in the control portion of our experiment prevents usfrom making any statement on how the presence of fire in-fluences packet loss.

It was observed in all our experiments that the presence ofhuman interference resulted in a large fluctuation in RSSIvalues. Although we negated this impact from our resultsby recording the time periods of human intervention, thisinterference could be eliminated by the remote ignition offire in future experiments.

In order to analyze the consistency of our data, we performlocal correlation between the data for two links, link 3:1and 2:4. Local correlation is a statistical analysis techniqueused to generate similarity scores to describe two time seriesdata sets [14]. There are two methods of computing localcorrelation scores; sliding window and exponential window.

Sliding Window Correlation

Γ̂t(X,w,m) :=

t∑τ=t−m+1

xτ,w ⊗ xτ,w

Table 4: Local Correlation Parameters

Symbol DescriptionΓt Local autocorrelation matrix esti-

matew Window sizext Time series, t ∈ N

xτ,w Window starting at t, xτ,w ∈ Rwm No. of windows (typically m=w)β Exponential decay weight

Exponential Window Correlation

Γ̂t(X,w, β) :=

t∑τ=1

βt−τxτ,w ⊗ xτ,w

The sliding window correlation technique uses m prior win-dows to calculate correlation between the two data sets.Exponential window correlation uses all prior windows inthis calculation, but weights them exponentially so that thecloser windows are weighted more.

Since our time value in the data was timestamped by the ob-server mote as soon as it received a packet, we had randomtimestamps for each time series. In order to apply correla-tion, we needed discrete time samples. To correct the for-matting of our data, we averaged every packet’s RSSI valuewithin time-frames of 2000 milliseconds (approximately 4measurements). This gave us a time series with consistenttime frames of 2000 milliseconds. We chose a window of 10for our correlation calculation, as window w essentially cor-responds to time scale. In our experiment, 10 window sizecorresponds to 20 seconds, which is enough time frames fora sensitive correlation as the fire lasted for approximately14 minutes. We chose β as 0.8 for the exponential localcorrelation window.

The results of our local correlation are shown in Figure 14.The local correlation score between these two links is alwaysabove .9998, which shows that the two links are highly cor-related. There is a loss of correlation at 345 seconds. Thisis a reflection of the human interference during the light-ing of the fire. There is another spike at around 840 thatrepresents other known human interference. The links areotherwise shown to be highly correlated, which proves thatour results were accurately captured by multiple observers.

Figure 14: Local correlation for links 1:3 and 2:4

6. FUTURE WORKAdditional trials of our experiment must be conducted tofurther prove statistical significance. Not only did we havea limitation in the time we had to conduct trials, we hadtrouble finding places that allowed large fires. Public firesare banned in the city of Clemson, and the only parks withfire rings required a paid reservation. This was a huge obsta-cle in our obtaining of data. By the time our methodologyhad matured enough to produce meaningful data, we had notime left to run more trials. Once additional identical tri-als have been run, the technique of local correlation shouldbe used to statistically correlate the results between trials.This will allow us to determine whether or not our resultsare repeatable and statistically significant.

Temperature, humidity, and light data should also be col-lected in order to explore the correlation between the differ-ent wildfire sensing parameters over time. This will provideinsight to the level of ”earliness” that is achieved by wildfiredetection using RSSI as an indicator.

Power consumption was not taken into account during ourexperiment. Sustainability of systems is a requirement ofwireless sensor network deployments. A real-world deploy-ment of this technology will require an energy harvestingstrategy. The sampling rate of RSSI will have a significantimpact on the power consumption of a wildfire detectionWSN deployment using this technique. The trade-off be-tween sampling rate, accuracy, and power consumption mustbe investigated for this technique to be confirmed as feasi-ble. One of the benefits of this technique is that it canbe implemented by measuring the RSSI of any packet withany payload. Depending on the protocols used in the wild-fire monitoring WSN, this technique could be implementedwithout any additional energy cost.

To further explore the feasibility of using RSSI as a param-eter for wildfire detection, larger scale experiments shouldbe conducted with larger fires such as controlled wildfires.This should provide us with a better understanding of the

relationship between flame height, distance, and the trendin RSSI values in the presence of fire.

If this technique proves to be feasible in those respects, itcould be used in conjunction with other wildfire detectionparameters such as temperature, humidity and light in asimilar manner to that discussed in [11].

7. CONCLUSIONIn this paper, we explored the possibility of using RSSI forearly wildfire detection. Our experiments showed that theintroduction of fire in radio link communication does impactRSSI, and the change is more significant with increasingintensity and size of fire. The rate of change of RSSI inthe presence of fire also depends on the distance from thefire. As a fire grows in size the drop in RSSI should beable to be observed at a larger distance. The feasibility ofusing RSSI as an indicator for wildfire detection cannot bedetermined until more experiments are conducted and at alarger scale. We should also explore the possibility of usingRSSI in conjunction with other parameters, which mightgive us a better method of early wildfire detection.

8. ACKNOWLEDGMENTWe would like to thank Dr. Jason Hallstrom and Dr. JacobSorber for providing us with the background, equipment andfeedback that was necessary to carry out this experiment.We would also like to thank Ian Taylor for providing usinsight on local correlation.

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