# Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
## 3 Proposed Methodology
### Network Model  
Consider a sensor field, which is made up of a collection of sensors that are randomly distributed throughout a specific area. The sensing activities and data reporting in this network are done on a regular basis [30; 31]. The method is predicated on the following characteristics of the model of a sensor network:  
* [noitemsep,topsep=0 pt]
* Sensor nodes are densely distributed and homogeneous in their distribution.
* Sensor nodes are mostly similar in terms of their sensing, processing, and communication capabilities.
* Each sensor node has a unique ID.  
Using the hello ER paradigm, sensor nodes broadcast data to the cluster head that is immediately adjacent to them.  
* The base station (BS) is stationary and situated a long distance away from the sensors.
* Each node can communicate with the BS on a one-to-one basis  
Initially, all nodes have the same amount of energy, and the network is homogenous in its energy distribution.  
The sink is in charge of clustering and routing operations. As a result, the technique that has been presented is centralized.  
* All nodes are energy restricted and execute tasks that are comparable to one another.  
The details of the EE-SS algorithm for homogenous sensor networks are stated below in Algorithm 1 and the flow of the approach can be seen in Figure 3.  
Figure 3: EE-SS proposed algorithm flowchart.  
```
Input:A set of sensor nodes, each with the Residual energy RES, degree difference Di, mobility speed Mi, its individual residual energy, total energy as Eri, Distance between Base station to each sensor node Dist, and Ti as the trust value for a node are the five coefficients for the weighted function (fitness function). Step 1: Find and compute Residual energy, Trust value, degree difference, total energy consumption and the distance between the nodes Step 2: Computer the mobility speed of every node Step 3: Calculate the combined weight with the help of Equation(7) by adding weight from W1 to W5 W1 for trust value = 0.5, w2 for residual energy = 0.3, w3 for degree difference = 0.1, w4 total energy consumption = 0.2, w5 coefficient for distance between SNs and BS = 0.4 Step 4: The node with the lowest Wi should be chosen to serve as the cluster head node Step 5: Consider the nodes that are within the transmission range to be member nodes of the cluster for investigation. Step 6: First cluster formation takes place as seen in Figure 4. Step 7: Remove the cluster head and its neighbor from the original set of sensor nodes after cluster formulation. Step 8 Repeat the process Step 1 to Step 7 until all nodes are assigned to a cluster
```  
**Sep 9:** if the left-over energy of CH is less the 25 % of its total energy, the CH selection process is again called. Step 11: Before sending data to base state an compression techniques is being used by CH known as SPHIT. Step 10: for sending data to the destination Semi Random Circular Movement model is being implemented for better getting probability of success. Step 11: On the Base Station a basic XOR operation is operated to remove the redundant data received by different CHs ```  
**Algorithm 1****Our proposed clustering approach EE-SS  
When it comes to power-constrained WSNs, implementing SPHIT [16] is an excellent choice since it delivers a better compression ratio while requiring less computing complexity, less power consumption, and a more straightforward implementation than other well-known compression algorithms [32; 33; 34]. Therefore, in our proposed algorithm, we implement SPHIT before sending data to a base station by al CHs. By avoiding the need for an entropy encoder, SPHIT generates a highly compact output bitstream, allowing for greater efficiency in terms of computing complexity and a reduction in the quantity of data that must be transmitted [20; 47; 48].  
Figure 4: Clustering of SNs.