Forest Fire Detection using Wireless Sensor Networks

Опубликовано: 25 Февраль 2025
на канале: MatlabSimulation. Com
316
5

Title: Early detection of Forest fires using wireless sensors network and Data mining technique
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s/w req:
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1)NS-3.26

2)ubuntu-14.04 LTS(32 bit os)


Execution Steps: ---------------------


Open the ternimal (ctrl + alt + t)

change the directory on terminal.

First execute the files

change the image locations in the main files.

command to execute :

./waf --run filename --vis

Note:
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1) use our module else you get error.

2)Refer howtoaddour model.txt to addmodule

Implementation Plan:
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Step 1: We create a Network, it consists of 100- Sensor nodes,1-Sink Node and 1 - Server Node.

Step 2: Initially, assign the highest energy level to the all sensor nodes.

Step 3: Next, perform the sample information broadcasts and based on the information perform the clustering and select the Cluster head by using a hierarchical clustering algorithm based on spectral classification (HCA-SC).

[The process based your reference paper :- A Hierarchical Clustering Algorithm based on Spectral Classification for Wireless Sensor Networks]

Step 4: Next, All sensor nodes measure environmental temperature, relative humidity, smoke and light. based on it each sensor node detects fire by itself.

[The process based your reference paper :- Data Mining Techniques Applied to Wireless Sensor Networks for Early Forest Fire Detection ]

Step 5: Next, Perform the route discovery message transmission between the nodes and based on it select the route between the sensors and server via sink

Step 6: Next, perform the fire detection data transmission.

[The process based your reference paper :- Intelligent Data Mining Techniques for Emergency Detection in Wireless Sensor Networks ]

Step 7: Finally,we plot the results graph for Accuracy, Throughput, Packet Transmission Rate and Energy Consumption .


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S/W Req:
----------------

1) ns-3.26
2) ubuntu 14.04 [32 bit]

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Note:-
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1) We perform the simulation based process only, not use any real time datasets and sensor datas.