Performance Evaluation of Wireless Sensor Networks inRealistic Wildfire Simulation Scenarios

Sinan Isik∗

NETLAB†

Bogazici University, Turkeyisiks@boun.edu.tr

M. Yunus Donmez‡

NETLABBogazici University, Turkey

donmezme@boun.edu.tr

Can TuncaNETLAB

Bogazici University, Turkeycan.tunca@boun.edu.tr

Cem ErsoyNETLAB

Bogazici University, Turkeyersoy@boun.edu.tr

ABSTRACTForest fires lead to high amount of environmental and eco-nomic loss all over the world. Prevention and early detectionefforts aim to eliminate or minimize the damage that will becaused by a fire incident. Current surveillance systems forforest fires do not provide dense real-time monitoring andhence they lack prevention or early detection of a fire threat.Wireless sensor networks (WSNs), on the other hand, cancollect real-time information such as temperature and hu-midity from almost all points of a forest and can providefresh and accurate data for the fire-fighting managementcenter quickly. In this work, we aim to evaluate the reportingperformance of a WSN under realistic workload. Since firesare destructive and burning a deployed WSN is not feasible,simulation is the appropriate way to assess the reporting ca-pability of a WSN during a forest fire. We integrate WSNsimulator with a realistic fire propagation simulator whichis modified to provide time based temperature field infor-mation while the fire propagates through the deploymentarea. Temperature information is used for the generationof realistic workloads and the determination of sensor de-struction times that affects the routing decisions in WSNsimulations. We present the effects of WSN related factors;such as reporting rate, number of the sinks, and the sinklocations together with the effects of environmental factorssuch as the wind speed and the number of ignition points in

∗Dr. Isik is also with the Department of Mathematics,Bogazici University.†NETLAB, Computer Networks Research Laboratory, De-partment of Computer Engineering, Bogazici University, Be-bek, Istanbul 34342, Turkey.‡Dr. Donmez is also with R&D, Information Technologies,NETAS, Istanbul, Turkey.

Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full cita-tion on the first page. Copyrights for components of this work owned by others thanACM must be honored. Abstracting with credit is permitted. To copy otherwise, or re-publish, to post on servers or to redistribute to lists, requires prior specific permissionand/or a fee. Request permissions from permissions@acm.org.MSWiM’13, November 3–8, 2013, Barcelona, Spain.Copyright 2013 ACM 978-1-4503-2353-6/13/11 ...$15.00.http://dx.doi.org/10.1145/2507924.2507941.

terms of temperature reporting performance and freshnessof temperature map.

Categories and Subject DescriptorsC.2 [Computer-Communication Networks]; C.2.2 [NetworkProtocols]; C.2.3 [Network Operations]

KeywordsWildfire; fire propagation estimation; wireless sensor net-works; simulation; performance evaluation

1. INTRODUCTIONTogether with fire prevention precautions, early warning

and fire spread monitoring systems for fires are very valu-able for improving the efficiency of fire fighting activities.Although there are satellite remote sensing [1] and camerabased early warning systems available [2, 3], these systemscannot function efficiently during all types of weather condi-tions. Their success depends on the time of the day, the exis-tence of line of sight and other visibility constraints. Hence,if these systems can be supported by a wireless network ofsensors such as temperature, humidity, smoke sensors, theresulting multimodal fire surveillance system can be moresuccessful.

The most important goals in fire surveillance are quickand reliable detection and accurate localization of the fire.While the dimensions of the fire are still small, and the lo-cation of the fire is known, it will be much easier to suppressthe fire without allowing much time for spreading. In thisrespect, besides timely detection of the fire, wireless sensornetworks [4] can help in providing information about the lo-cation of the fire, about the extent of spread of the fire, andabout temperature or smoke conditions at various locationswhich are highly valuable for firefighting management. Withthat information at hand, firefighting staff can be guided to-wards the target to block the fire or to suppress it quickly byutilizing the required firefighting instruments and vehicles.

During the last few years, significant advances in embed-ded hardware and software technologies are driving downthe cost of wireless sensors. There is a variety of wirelesssensor nodes that have become commercially available [5,6, 7]. Although their prices are not yet very suitable to be

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Visualization Control Center

Fire Propagation

Estimation

(EFP)

Data Fusion

Video-Based Fire

Detection

Weather Data

Processing

Wireless Sensor

Network

IR Data

Processing

Fuel

Model

WSN

Performance

Evaluation

Sensor Polling

USER

· Queries

· Feedback

· Fire Propagation

· Response to Queries

· Alarms

· Temperature Map

· Visual Feeadback

Figure 1: FIRESENSE system architecture

deployed in thousands to very large areas, several hundredsensors can be deployed around critical or important areasand used for improving the efficiency of the fire monitoringsystems.The work in this paper is contained as a sub-block in the

block structure of a fire monitoring and detection system(Figure 1) named FIRESENSE [8] which is a Specific Tar-geted Research Project (STReP) of the European Union’s7th Framework Programme Environment. FIRESENSE aimsto develop an automatic early warning system to remotelymonitor areas of archaeological and cultural interest fromthe risk of fire and extreme weather conditions. The sys-tem is based on an integrated approach that uses innovativesystems for early warning. It takes the advantage of re-cent advances in multisensor surveillance technologies. Thekey idea is to place a Wireless Sensor Network (WSN), ca-pable of monitoring temperature, and optical and infraredcameras on the deployment site. The signals collected fromthese sensors are transmitted to a monitoring center, whichemploys intelligent computer vision and pattern recognitionalgorithms as well as data fusion techniques to automati-cally analyze sensor information. The system is capable ofgenerating automatic warning signals for local authoritieswhenever a dangerous situation arises. FIRESENSE pro-vide real-time information about the evolution of fire usingwireless sensor network data. Furthermore, it estimates thepropagation of the fire based on the fuel model of the areaand other important parameters such as wind speed, slopeand aspect of the ground surface. A 3-D Geographic Infor-mation Sytem (GIS) environment provides visualization ofthe predicted propagation.Since fires are destructive and burning a deployed wireless

sensor network is not feasible, simulation is the appropriateway for evaluating the fire detection performance of a WSNduring a forest fire. Here, we need an integrated simulationframework which contains both a realistic fire propagationsimulation component and a realistic WSN simulation com-ponent. In this framework, the fire propagation simulationcomponent should include a temperature field model suchthat it can evaluate and report the temperature values atspecific geographic coordinates as an incident of a wildfire

propagates in an environment with given ignition locationsand environmental conditions. On the other hand, the WSNsimulation component should include a sensor destructionmodel where the sensors deployed into an environment aredestructible due to high temperatures caused by a propagat-ing fire incident. Moreover, the WSN simulation componentshould include a distributed routing model which can main-tain its functionality with fast route restoration and minimaloverhead under such dynamic network topologies. In theintegrated simulation framework, the temperature map evo-lution output of the fire propagation component is fed intothe WSN simulation component as an input for the perfor-mance evaluation of WSNs in realistic forest fire simulationscenarios.

For the WSN simulation component, we used OPNETmodeler [9] and for the fire propagation simulation compo-nent we used EFP [10]. OPNET is a commercial networkdesign, analysis and research tool and it can be used bothprior to or after the deployment for assessing the reportingperformance of a WSN in a forest fire scenario. EFP is de-veloped in the FIRESENSE project and it aims to predictthe propagation of a real fire incident by considering theenvironmental models such as ground and fuel models andcurrent weather conditions as input. In the project varioustemperature field modeling algorithms are developed andembedded into EFP which allows modeling of the tempera-ture variation at specific pre-defined sensor node locations,as a wildfire spreads starting from specific locations and un-der specific environmental conditions. The temperature mapevolution is then used in OPNET for the sensor data gener-ation and for the determination of sensor destructions. Asthe routing algorithm, we used a recent multisink routingalgorithm, MLBRF [11], which is suitable for wildfire appli-cations, where sensors are dynamically destroyed and sensortopology changes as the fire propagates in the environment.

In this paper, we aim evaluate the reporting performanceof WSNs under realistic fire scenarios by enabling tempera-ture based workload generation with dynamic topology changesdue to sensor destructions caused by very high temperatures.We present the effects of WSN related factors; such as thereporting rate, the number of sinks, and the sink locationstogether with the effects of environmental factors such as thewind speed and the number of ignition points in tempera-ture reporting performance and freshness of the temperaturemap.

The rest of the paper is organized as follows: An overviewof the related works on WSNs for forest fire detection ispresented in Section 2. A summary of MLBRF routing al-gorithm and its route restoration mechanism is given in Sec-tion 3. The simulation model and the performance evalua-tion of WSN in various scenarios are presented in Section 4.Section 5 concludes the paper.

2. FIRE DETECTION USING WSNThere has been a considerable amount of work carried out

for fire detection application of wireless sensor networks.In [12] real experiments through controlled fires are donewith a system of ten sensor nodes with GPS capability. Thesensor nodes are deployed with ranges up to one kilome-ter and they sense and forward temperature, humidity andbarometric pressure values to a base station. The systemwas implemented and real-world observations were gatheredfrom the field. However, because of the long distances be-

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tween sensor nodes, the data arriving to the sink is not valu-able enough to detect a fire quickly and forecast the spreaddirection of the fire. Also, with the growth of fire and burn-ing out some of the sensor nodes, the sensor network couldfail in delivering the data from all sensor nodes to the basestation.A wireless local area network (WLAN) together with sensor-

node technology for fire detection is used in [13]. The pro-posed system mixes multi-sensor nodes with IP-based cam-eras in a wireless mesh network setting in order to detectand verify a fire. When a fire is detected by a wireless multi-sensor node, the alarm generated by the node is propagatedthrough the wireless network to a central server on whicha software application runs for selecting the closest wire-less camera(s). Then, real time images from the zone arestreamed to the sink. Combining sensory data with imagesis the most important contribution of this study.A multi-tiered portable wireless system for monitoring en-

vironmental conditions, especially for forest fires is devel-oped and presented in [14]. Integrating web-enabled surveil-lance cameras with wireless sensor nodes, the system canprovide real-time weather data from a forest. Three dif-ferent sensor networks are deployed to different parts of aforest and the communication between the networks is en-abled by powerful wireless devices that can send data up toten kilometers range. The objective of the study is to deter-mine the behavior of forest fires rather than their detection.With a wireless sensor network around an active fire, theymeasure the weather conditions around the fire. Webcamsare also used to get visual data of the fire zone. Data gath-ered from the sensor nodes and the webcams are aggregatedat a base station which has the capability of providing longdistance communication using satellites. Periodically, thesensor nodes measure the temperature, relative humidity,wind speed and direction, and web-cams provide continuousvisual data to the base station.A forest fire surveillance system in South Korea is pro-

posed by [15] in which a dynamic minimum cost path for-warding protocol is applied. After gathering data, a sinknode makes several calculations regarding the relative hu-midity, precipitation and solar radiation, and produces aforest fire risk level. Additionally, [15] applies a minimumcost path forwarding method that causes some sensor nodes(especially the ones that are closer to the sink) to consumetheir energy much faster than the others.A method is presented in [16] which applies neural network

techniques for in-network data processing in environmen-tal sensing applications of wireless sensor networks. Severaldata fusion algorithms are presented in this study. Maxi-mum, minimum and average values of temperature and hu-midity data are calculated by the cluster-heads. Data arepropagated to the sink only if a certain threshold is exceeded.A general reliability-centric framework for event reporting

in wireless sensor networks which can also be used in forestfire detection systems is provided in [17]. They considerthe accuracy, importance and freshness of the reported datain environmental event detection systems. They present adata aggregation algorithm for filtering important data anda delay-aware data transmission protocol for rapidly carry-ing the data to the sink node.A proactive routing method for wireless sensor networks

is proposed in [18] to be used in disaster detection. The pro-tocol is developed to be aware of a node’s destruction threat

and it can adapt the routes in case of a sensor node’s death.The method can also adapt the routing state based on apossible failure threat indicated by a sensed phenomenon.

A wireless sensor network for forest fire detection is de-veloped in [19] based on Fire Weather Index (FWI) systemwhich is one of the most comprehensive forest fire danger rat-ing systems in USA. The system determines the spread riskof a fire according to several index parameters. It collectsweather data via the sensor nodes, and the data collectedis analyzed at a center according to FWI. A distributed al-gorithm is used to minimize the error estimation for spreaddirection of a forest fire.

A simulation environment is presented in [20] that cancreate a model for a fire by analyzing the data reported bysensor nodes and by using some geographical informationabout the area. The use of topography of the environmentdistinguishes the study from some other solutions. The esti-mation of the spread of a fire is sent to hand-held devices offire fighters to help them in fighting against the fire in field.

A framework for the use of wireless sensor networks for for-est fire detection and monitoring is proposed in [21]. Frame-work includes proposals for the wireless sensor network ar-chitecture, sensor deployment scheme, and clustering andcommunication protocols. The aim of the framework is todetect a fire threat as early as possible and yet consider theenergy consumption of the sensor nodes and the environ-mental conditions that may affect the required activity levelof the network. Proposed framework is validated and evalu-ated via simulations to show that the framework can providefast reaction to forest fires while also consuming energy ef-ficiently.

3. SUITABILITY OF MLBRF FOR WILD-FIRE APPLICATIONS

3.1 An overview of MLBRFDeployment of multiple sinks is a candidate solution for

the congestion problem in WSNs which also provides extrabenefits in terms of energy-efficiency and reliability. Ex-tra sinks in the environment relieves the burden around anysink. In addition, the average path length, consequently, thenumber of hops that a frame has to travel between a sensorand a sink decreases due to shorter geographic distances.Traveling shorter hops results with less energy consumptionon the whole network due to the degradation in the numberof sensors employed as a relay node. Moreover, usage of ex-tra sinks relieves the unbalanced energy consumption amongthe sensors and improves the lifetime of a deployed sensornetwork. A multi-sink sensor network also becomes more ro-bust against the inaccessibility of a sink node due to singlepoint of failures such as node failures (energy exhaustion),node destructions (wildfire) or communication destructions(jamming).

In order to gain the maximum benefit from the deploy-ment of multiple sinks, it is essential to distribute the loadamong the sinks evenly. MLBRF (Multi-Sink Load Bal-anced Reliable Forwarding) [11] is a cross layer geographicforwarding scheme which aims to provide reliable and en-ergy efficient data delivery in a multi-sinked WSN for targettracking by maintaining load balancing among the deployedsinks.

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1

2

3

3

MATLAB Network

Visualization Tool

EFP

OPNET

Figure 2: Block Diagram of OPNET and EFP

MLBRF uses a sink selection mechanism based on fuzzylogic. In order to evaluate the traffic density in the directionof a sink, it uses a fuzzy inference system to combine twodynamic criteria which are the number of contenders andthe buffer occupancy levels in the neighborhood with a staticcriterion which is the distance of the candidate relay sensorto the sink. The result of the fuzzy engine is the currentmembership value of the sensor for a sink. Subsequent to theevaluation of the current membership values of the sensor foreach sink, the destination sink for a frame is determined asthe one with the greatest membership value.

3.2 Route restoration mechanism of MLBRFMLBRF uses a special solution to route the packets around

dead-ends. For this purpose, it introduces routing-classes inorder to discriminate the forwarding capabilities of the sen-sors.The own routing-class of a sensor is determined according

to the gathered routing-class information of the neighborswhich are acquired by extracting the piggy-backed informa-tion from each received MAC layer packet and stored inneighbor information table. In case of any change in therouting-class information from a neighbor, a sensor checksand updates its own routing-class accordingly. In that way,these changes iteratively diffuse in the network over time andthe routing-classes in the network reach to a steady state.The routing-class of a node is updated either in the ini-

tialization phase or in case of a topological change in thenetwork such as energy depletion, sensor destruction or along-term communication disruption. If for a sufficientlylong time no packet is received from a neighbor, than therouting-class of that sensor is set to be unreachable and arenot involved in any forwarding decision. A sensor deter-mines its neighbors that belong to this class at the time ofeach frame delivery by controlling the last packet receptiontimes in neighbor information table and re-evaluates its ownrouting-class. If any subsequent packet is received from anunreachable neighbor, the routing-class of the neighbor andthe node itself is updated accordingly.

4. PERFORMANCE EVALUATION OF A WSNFOR FIRE DETECTION

The structure of the integrated WSN performance eval-uation system is presented in Figure 2. In the integratedsystem, initially, the WSN topology (area dimensions, sen-sor locations, grid size, and ignition point(s)) that will beused in OPNET simulations is generated and fed to the EFPmodule (1). Various additional parameters are then speci-fied by the user or are automatically obtained based on var-ious information sources, e.g.: slope/aspect (estimated from100m STRM files for the area of interest), fuel maps (con-verted from CORINE maps corresponding to the area ofinterest),weather parameters such as temperature, humid-ity, precipitation, wind speed and wind direction (currentor forecasted, obtained by FIRESENSE weather stations orsupported external (Internet) weather services). EFP esti-mates and outputs the temperature variation with respectto time for each sensor location for the given topology. Eachcell of the grid may either be ignited or not, as the fire ispropagated, and the temperature at each predefined sensorlocation can be modeled based on the locations and fire-related parameters (e.g. flame length) of the ignited cellsclosest to the sensor. The temperature variation for eachsensor is estimated for the whole time-span of the WSN sim-ulation and used by OPNET to determine the temperaturereporting rate and the time instant of sensor destructions.The overall network and fire propagation behaviour is ob-served using the Network Visualization Tool implemented inMATLAB (3).

In our simulation model temperature sensors are randomlydeployed into the forest. Initially, all the sensors are aliveand start sensing and communication as a regular WSN.Sensors generate periodic data packets that contain the tem-perature values reported by the EFP software and deliverthem in a multi-hop manner to the sinks using a routingprotocol. These report packets are used to create a temper-ature map of the environment. The sensor data generationmodel supports two modes of operation, which are normaland alarm modes. In these operation modes, the sensors re-port temperature readings with different periods where the

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(a)

(b)

Figure 3: WSN topology change due to sensor de-structions

period is longer in the normal mode and shorter in the alarmmode. Related with the operation modes of the sensors,there are two temperature thresholds defined in the model.The first temperature threshold value is the one that deter-mines the sensors to switch from the normal operation modeto alarm operation mode. The second temperature thresh-old value is the one that defines the temperature value overwhich the sensors are destructed. The destructed sensorswill cease both sensing and relaying activities, and alterna-tive routes to sinks are considered (Figure 3).We designed experiments to to explore the effects of WSN

related factors; such as reporting rate, number and locationof the sinks together with the effects of environmental factorssuch as the wind speed and the number of ignition points interms of temperature reporting performance and freshnessof temperature map.

4.1 Simulation Setup and ParametersIn the default simulation scenarios, 200 sensors are de-

ployed into an environment of 400m x 400m. The environ-ment is assumed to be in the ancient city of Rhodiapolisin the Antalya region in Turkey which is one of the FIRE-SENSE pilot sites [22] and the environmental parameterssuch as fuel maps and slope/aspect are configured accord-ingly. The location of the sinks are varied in order to evalu-ate the effect of sink locations on the reporting performanceof the WSN. In Figure 4, the simulated fire scenario and thesimulated WSN are presented. The alert mode temperature

(a)

(b)

Figure 4: Simulated fire scenario and the WSN

threshold of the sensors are set to 60 ◦C which is 20 ◦C abovethe ambient temperature assumed to be 40 ◦C in a summerday which is of a high risk season. The destruction temper-ature threshold of the sensors are set to 120 ◦C where thesensors are assumed to be in a protective casing. The rout-ing protocol used is MLBRF [11] which is modified to dealwith sensor destructions for finding alternative routes. Inthe MAC layer, we used the SMAC [23] protocol with 1%duty cycle. Table 1 presents other simulation parameters.

Table 1: Simulation parameters

Normal Period 10 minutes

Alarm Period 0.25, 0.5,1,2,4 minutes

Alarm & Destruction Thresh. 60 oC & 120 oC

Data Packet & Buffer Size 100 bits & 10 packets

Channel Rate 250 Kbps

MAC Layer SMAC

Duty Cycle 1%

Transmission Range 60 m

Initial Energy 200 J

TX Power 81 mW

RX Power 30 mW

IDLE Power 30 mW

SLEEP Power 0.003 mW

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(a)

(b)

Figure 5: Report delivery ratio and report delay

The radio power consumption parameters are set as in [24],which are typical values for Mica2 Mote sensors.

4.2 Single Sink ScenariosIn the first set of experiments, the sink is located at the

center of the right edge and the wind speed is set as 3 mphin EFP. During fire propagation, 33 sensors are destructedwhich are shown in red (dark colors) in Figure 4.b. In theseexperiments, we varied the alarm period of the sensors andobserved the delivery performance of the WSN in terms ofthe report delivery ratio (Figure 5.a), the reporting delay(Figure 5.b), the freshness of the temperature map (Fig-ure 6.a) and the energy expenditure per successful reportdelivery (Figure 6.b). Freshness of temperature reports froma sensor is measured in seconds and it corresponds to the av-erage inter-arrival time among the report packets. Likewise,freshness of the temperature map provided by a WSN is theaverage freshness of all sensors in the network.When the alarm period is at the lowest value, we observe

that the report delivery ratio is low and the mean reportdelay is very high due to high level of congestion created bythe excessive number of report packets. For the alarm periodvalue of 0.5 minutes, the congestion level is still quite highwhich results with a delivery ratio below 0.8 and mean delay

(a)

(b)

Figure 6: Report freshness and energy expenditureper report

above 50 seconds. When the alarm period is 1 minute andabove, the congestion is relieved and the WSN performanceis acceptable in terms of both metrics,i.e., the delivery ratiois above 0.9 and the mean delay is below 10 seconds.

On the other hand, the average freshness of the tempera-ture map results in Figure 6.a suggest that the alarm periodshould be set as 1 minute where we get the best value amongthe measured ones. Freshness is worse for the lower alarmperiod values due to high level of congestion. As we increasethe alarm period beyond 1 minute, although the congestionlevel decreases, average freshness increases due to the largerinter-arrival times of the report packets. For the choice of 1minute alarm period, delivery ratio and report delay figuresalso provide quite acceptable results.

Figure 6.b presents the effectiveness of the WSN in termsof energy expenditure per successful report delivery. As thealarm period increases, the number of report packets createdby the network decreases but the energy expenditure of theWSN per report increases. This result is due to the MACprotocol which operates with a static duty cycle and suggestsa MAC protocol that has a dynamic duty cycle management.

In the second set of experiments, the alarm period is setas 1 minute and the location of the sinks are varied to be

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Table 2: Performance results for different sink loca-tions

R L T B

Delivery Ratio 0.97 0.95 0.91 0.9

Delay (sec) 9.26 20.23 11.71 21.58

Av. Temp. Map Freshness (sec) 40 56 52 65

L -R -B

0.72 0.77 0.78

66.81 44.54 46.16

Av. Temp. Map Freshness (sec) 111 85 86

Table 3: Performance results for two sinks caseTable 11. Performance results for 2 sinks case

L L-R T-B

Delivery Ratio 0.72 0.77 0.78

Delay (sec) 66.81 44.54 46.16

Av. Temp. Map Freshness (sec) 111 85 86

in the centers of Top (T), Bottom (B) and Left (L) edges ofthe environment. Table 2 presents the performance resultsfor each sink location including Right (R) edge. From theresults, it can be observed that when the sink is located inthe right edge, which is the fire propagation direction, weget better results in each performance metric.

4.3 Multiple Sinks ScenariosIn the first set of experiments, the number of sinks in

the environment is increased to two. The alarm period isalso set as 0.5 minute to increase the load of the networkfor this and the other set of experiments. The locationsof the sinks in one setup are the centers of the left andthe right edges of the environment. In the other setup, thelocations are the centers of the top and the bottom edges.The two setup results are compared to the case where thereis a single sink in the environment located in the center ofthe left edge. Table 3 presents the performance results. Theperformance of the WSN for both setup with two sinks aresimilar. Introduction of a second sink reduces the congestionin the network especially around the sinks compared to thesingle sink case and improves the performance of the WSNin terms of each metric.In two other set of experiments, we increased the num-

ber of sinks in the environment to three and four sinks, theresults of which are presented in Table 4 and Table 5 respec-

Table 4: Performance results for three sinks caseTable 12. Performance results for 3 sinks case

L-R-T L-R-B L-T-B R-T-B

Delivery Ratio 0.87 0.84 0.84 0.87

Delay (sec) 28.62 34.99 34.09 24.59

Av. Temp. Map Freshness (sec) 55 68 63 51

dge Centers Corners

Av. Temp. Map Freshness (sec)

1 mph 2 mph 3 mph 4 mph

1 0.99 0.95 0.96

2.36 4.29 17.94 16.09

Av. Temp. Map Freshness (sec) 24 21 38 34

2 6 33 70

Table 5: Performance results for four sinks caseTable 13. Performance results for 4 sinks case

Edge Centers Corners

Delivery Ratio 0.92 0.95

Delay (sec) 19.24 17.94

Av. Temp. Map Freshness (sec) 42 38

1 mph 2 mph 3 mph 4 mph

1 0.99 0.95 0.96

2.36 4.29 17.94 16.09

Av. Temp. Map Freshness (sec) 24 21 38 34

2 6 33 70

Table 6: Performance results for different windspeed for four sinks case

Table 14. Performance results for different wind speed for 4 sinks case

Wind speed 1 mph 2 mph 3 mph 4 mph

Delivery Ratio 1 0.99 0.95 0.96

Delay (sec) 2.36 4.29 17.94 16.09

Av. Temp. Map Freshness (sec) 24 21 38 34

Destructed 2 6 33 70

tively. From each set of experiments, we observe that as thenumber of sinks in the environment increases, the degree ofcongestion decreases and the performance of the WSN im-proves in terms of each metric. In addition, the results showthat the location of the sinks is an important factor thatprovides a significant differentiation in the performance ofthe WSN. The location of ignition and the direction of firepropagation make the location of the sinks an influentialparameter.

4.4 Effect of the Wind SpeedIn this set of experiments, we look at the effect of wind

speed in the delivery performance of the WSN. For this pur-pose, keeping the ignition point as it is in the previous ex-periments and the number of sinks as four located in thecorners, we varied the wind speed. As can be seen in Ta-ble 6, as the wind speed increases, the number of destructedsensors increases up to 70. The increase in the destructedsensors affects the operation of the routing algorithm bycreating routing holes and topology changes. However, weobserve that the routing algorithm finds alternative routesand achieves fairly good results in each performance metricdespite the increased number of sensor destructions. We ob-serve that the report delivery ratio increases and the reportdelay decreases as the wind speed changes from 3 mph to4 mph, which is also related with the increased number ofsensor destructions that leads to decreased level of the cre-ated load in the network and hence the decreased level ofcongestion.

4.5 Effect of the Number of Ignition PointsIn the first set of experiments, we set the alarm period

as 0.5 minutes and the wind speed as 3 mph. In Figure 7,the simulated fire scenario and the simulated WSN are pre-sented. During the fire propagation, 86 sensors are destruc-ted (Figure 7.b) which are shown in red (dark colors). Wevaried the number of sinks in the environment. Table 7presents the performance results. We repeated the same setof experiments for the case where the wind speed is set as4 mph. In Figure 8, the simulated fire scenario and the

Table 7: Performance results for two ignition points(Wind speed=3 mph)

(Alarm period=0.5, Wind speed=3 mph)

T T-B L-T-B L-R-T-B

Delivery Ratio 0.58 0.83 0.91 0.95

Delay (sec) 49.79 47.15 28.96 24.97

Av. Temp. Map Freshness (sec) 130 83 62 50

We repeated the same set of experiments for the case where the wind speed is set

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(a)

(b)

Figure 7: Two ignition points fire scenario and theresulting WSN simulation (Wind speed = 3mph)

simulated WSN are presented. During the fire propagation,119 sensors are destructed (Figure 8.b). The performanceresults for this case are presented in Table 8.As expected, the increase in the number of ignition points

in the environment increases the number of destructed sen-sors which causes to frequent topology changes. The resultsshow that with multiple sinks in the environment the rout-ing algorithm successfully deals with the frequent topologychanges and finds alternative routes for delivering the tem-perature reports to the sink nodes, where the delivery ratioincreases up to 95%.

5. CONCLUSIONIn this work, we evaluated the performance of a WSN un-

der realistic fire scenarios using the OPNET modeler, whichis integrated with a realistic fire propagation simulator thatprovides time based temperature information from the de-

Table 8: Performance results for two ignition points(Wind speed=4 mph)

(Alarm period=0.5, Wind speed=4 mph)

T T-B L-T-B L-R-T-B

Delivery Ratio 0.58 0.85 0.94 0.96

Delay (sec) 51.98 36.73 19.21 11.06

Av. Temp. Map Freshness (sec) 115 64 48 34

(a)

(b)

Figure 8: Two ignition points fire scenario and theresulting WSN simulation (Wind speed = 4mph)

ployment area. We presented the performance of WSN un-der various load conditions determined by the reporting ratein terms of the report delivery ratio, the reporting delay, thefreshness of temperature map and the energy expenditure.The results show that, for the cases studied, 1 report perminute gives the best result in terms of freshness and quiteacceptable results in terms of the delivery ratio and the de-lay. In addition, energy expenditure results suggest a MACprotocol that adjusts its duty cycle dynamically accordingto the level of the load in the network for the effective useof scarce energy. The results also show that the location ofthe sinks is an important factor that affects the performanceof the WSN. Hence they have to be arranged according tothe main wind directions in the monitored area. We alsoevaluated the reporting performance of WSNs in scenarioswith multiple sinks where we observed that the increase inthe number of the sinks provides a significant gain in termsof each performance metric. The increase in the the windspeed and the number of ignition points in the environmentcauses higher number of sensors to be destructed which leadsto dynamic and frequent topology changes. The results showthat the routing algorithm is successful in coping with thefrequent topology changes and finding alternative routes fordelivering the temperature reports to the sink nodes.

As the next step, we want to design and implement aload aware MAC protocol that is tailored for the needs ofWSNs for forest fire monitoring. Then, we can compare theperformance of WSN using the novel MAC with the resultsobtained in this work. We also want to design additional

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experiments to further investigate the effects of the envi-ronmental parameters such as vegetation and aspect/slopecharacteristics of an environment on the detection and deliv-ery performance of WSNs. In addition, we want to improvethe prediction capability of the fire propagation estimationsoftware in a real fire scenario by enabling the temperaturemap information to flow from the WSN to the fire propa-gation software. In that case, we can compare the accuracyof the WSN-integrated fire propagation estimation with theoriginal EFP software.

6. ACKNOWLEDGMENTSThis work is supported by the European Community’s

Seventh Framework Programme (FP7-ENV-2009-1) underthe grant agreement FP7-ENV-244088 FIRESENSE.

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