Wireless Sensor Networks for Early Detection of Forest Fires

1. Wireless Sensor Networks for Early Detection of Forest Fires V. Krishna Kanth M. Tech, ACS III semester EC084210

2. Introduction • Forests are considered as one of the most important resources & protector of earth’s ecological balance. • Forest fire is considered as one of the severest disaster which destroyed forest resources and threatened human living environment. • Common causes of forest fires include ; • lightning • human carelessness • exposure of fuel to extreme heat, etc. • Apart from preventive measures, early detection and suppression of fires is the only way to minimize the damage due to forest fire.

3. EXAMPLE: In the period from January 1 to December 31, 2007 there were 8925 fires registered in Croatia of which 5455 were forest fires that burnt an area of 67,992 hectares

4. Forest fires between 1937-2006 in Turkey

5. Forest fire detection systems based on WSN • At present, traditional forest fire prevention measures are; • Ground patrolling • Watching tower • Long distance video detection • Satellite monitoring and so on Disadvantage of satellite based monitoring; • Long scan period • Low resolution • Cannot forecast forest fires before the fire is spread uncontrollable • Based on the deficiencies of conventional forest fire detection on real time and monitoring accuracy, the WSN techniques for forest fire detection was introduced along with the traditional preventive measures.

6. Parameters of interest • For forest fire detection, the motes collects the dynamic changing fire information such as; • Temperature • Humidity • Atmospheric pressure • Wind speed • Wind direction

7. Forest fire detection systems based on WSN • The university of California at Berkeley in collaboration with the United States National Science Foundation (USNSF), took the lead research on WSN for the application of monitoring forest fires . • The sensor nodes (motes) are generally deployed randomly in the forests or nearby area, and organize themselves into self configuring network (removing overhead of manual setup)

8. Structure of a WSN for forest fire detection

9. Forest fire detection systems based on WSN • FWI system • Mobile Biological Sensor System(MBS)

10. FWI system • FWI system is one of the most popular forest rating system in North America {Developed by Canadian Forest Service(CFS)} • FWI system is comprised of six components: • Three fuel codes(FFMC,DMC,DC) They represent the moisture content of the organic soil layers of forest floor. • Three fire indices (ISI,BUI,FWI) They describe the behavior of fire.

11. Structure of FWI system

12. Fuel Moisture Codes • Fine Fuel Moisture Code(FFMC) • FFMC represents the moisture content of litter and fine fuels (1-2cm deep). • It can be used to indicate the ignition probability. • Duff Moisture Code(DMC) • DMC determines the probability of fire ignition due to lightening and also shows the rate of fuel consumption in moderate depth layers (5-10 cm deep). • Drought Code(DC) • DC is an indicator of moisture content of the deep layer of compacted organic matter (10-20cms deep).

13. Fire Behavior Indexes • ISI(Initial Spread Index) • Indicates the rate of fire spread immediately after ignition. • BUI(Build Up Index) • It is the weighted combination of DMC and DC codes and it indicates the total amount of fuel available for combustion. • FWI(Fire Weather Index) • FWI indicates fire intensity by combining the rate of fire spread with the amount of fuel being consumed. • ISI and BUI are used to compute the FWI index. • FFMC code is used to provide early warning of the potential forest fire. • FWI index is used to estimate the scale and intensity of the forest fire.

14. FFMC and FWI are computed from the basic weather conditions: temperature, relative humidity, wind speed.

15. Mobile Biological Sensor System(MBS) • To utilize animals with sensors as Mobile Biological Sensors(MBS). • Sensors should posses GPS feature so that the location of the sensor is known. • This system offers two detection methods(depending on type of sensors used); (1) Animal Behavior Classification (ABC) ~To classify sudden changes in animals behavior. (2) Thermal Detection(TD) ~To measure sudden changes in animal behavior. Advantage of MBS: • More coverage with less number of motes.

16. Thermal Detection(TD) • Access points gets the temperature values from the MBS devices and check for any instant changes in the temperature. Animals escaping the forest fire

17. An example simulation Animals : 3 Loria Forest snakes, 2 Egyptian tortoises Sensors : 5 SR-TP11-25 (Temperature and Pressure sensor produced by Lotek Corp) Area : 1 acre

18. ABC(Animal Behavior Classification) • Access points continuously receive data about animals’ location using GPS and see if there is a sudden movement(panic) of the animal groups .

20. Animals as MBS • Animals that can be used as MBS will vary according to the territory’s specification • The most important issue in selection of sensors is that they must have GPS feature.

21. Sample sensors that can be used in the subsystem A simple mote

23. Categories of Forest Fires • Ground Fires(GF) • They occur in the humus and peaty layers under the litter of compound material on the forest floor • Produce intense heat but practically no flame • Slow moving animals such as reptiles turtles are considered • Surface Fires(SF) • Fires occurring on the ground in the litter • Spread of fire is regular and usually depends on wind speed • Selected animals should be fast and live in groups • Crown Fires(CF): • Occurs in crowns of trees

24. Infrastructure

25. SENSOR NODES (Motes) : • For the nodes in the senor network we chose the Mica2 motes • manufactured by Crossbow. • The Mica2s utilize AA batteries. • The Mica2 is controlled by an Atmel ATMega128 8-bit processor running at • 7.37 Mhz. • For communications, the Mica2 uses the Chipcon CC1000 radio operating at • 900Mhz. • To allow for different sensing packages, the Mica2 contains an external 52-pin • connector. • Motes run on tiny OS and executing programs written in NesC.

26. A typical Berkeley Mote

27. PROTECTION FOR THE NODES :

28. Firesensorsocks • To thermally protect the sensors to prevent their destruction during fire event and extending their lifetime beyond the fire event. • The Firesensorsock consists in several layers of thermal insulation materials: (1)A layer of simple Zetex fiber (2)A layer of ceramic wool and (3)A final layer of aluminized Zetex fiber These layers are fixed to one another with Kevlar thread and thus achieve a prolonged fire contact and also wireless communications without any disturbance.

29. CONCLUSION • Compared to traditional method of wildfire prevention, WSN technology has greater advantage . The design of a WSN for early detection of forest fires has been presented which is based on the Fire Weather Index(FWI).FWI system can be used to meet the two goals of a WSN designed for forest fires; (1)Provide early warning of a potential forest fire. (2)Estimate the scale and intensity of the fire. • The TD and ABC methods fire detection based on MBS can be usefully adapted for current forest fire detections.

30. REFERENCES : [1] Jungo ZHANG and Xiaolin,“Forest fire detection system based on wireless sensor networks“, IEEE Communications magazine, V 9, July 2007,P 518-523. [2]YasarGuneriSahin,” Animals as Mobile Biological Sensors for Forest Fire Detection”, Sensors magazine 2007, 4 December2007, V 7, P 3084-3099. [3]I. Akyildiz, S. Weilian, Y. Sankarasubramaniam and E. Cayirci”A survey on sensor networks”, IEEE Communications Magazine,August 2002, P 102-114. [4]Zhang J G,LI W B, K J M,” Forest fire detection system based on Zigbee wireless sensor network “, Journal of Beijing Forestry University, v 29,July 2007, p 41-45. [5] United States Forest Service RAWS http://www.fs.fed.us/raws/ [6] Crossbow Inc. Web Page. http://www.xbow.com/

31. Thank You