## 5 Analyses and Simulation Results
### Analyzing Computational Overhead and Energy Efficiency in High Resolution Image Generation  
In this subsection, the computational overhead of stitching low-resolution images, the energy efficiency of presented methods and quality of generated high resolution images are simulated and analyzed. It is assumed that after the first query and sending required information to the related cluster head, Sink sends 10 queries to cluster head and obtain high-resolution images in response to these queries. Also, as mentioned in subsection 5.1, related cluster head is located three hops far from Sink and transmission schemes in EQV-Architecture [49] are used to transmit stitched image to Sink through the upper-tier. As mentioned before, having the transformation matrices and blending maps, cluster head can stitch its camera sensor images with low computational overhead before sending them to Sink. In order to do so, every pixel of each selected low-resolution image must be transferred to its position in the high-resolution image by related transformation matrix and its opacity level in blending map be  
Figure 10: Comparing FCNS and MCNS algorithms.  
applied to them. Despite the result of simplified stitching method is not as good as the original one containing a multiband blending, it is worth utilizing because its low computational overhead lets the stitching be performed by cluster heads. For example, in this simulation, image stitching in cluster head by available transformation matrices requires approximately 21% computation of whole image stitching process from the beginning by comparing average run time of both algorithms in 10 run. On the other hand, reusing transformation matrices causes 79% computational performance.  
Moreover, using the Coverage Data Structure, cluster head is able to select proper active subset of sensor nodes. Figure 11 shows the stitched image of 8 th query in the related cluster head. This high-resolution image is created using low-resolution images that generated with camera IDs 24, 21, 23, 19, 15, 17, 13, 8, 10, 9, 2, and 4.  
As mentioned before, the grid cells covered by only one camera node are omitted from Coverage Data structure. Therefore, the stitched image in cluster head created by proper subset of camera sensor images has some small difference from stitched image created by all camera sensor images. However, based on simulation results shown in Figure 11, the stitched image in cluster head is acceptable for many monitoring sensor network applications.  
Stitching in the cluster head presented in this literature omits transmitting redundant data to Sink and let save more energy in the network. To show this, it is assumed that 10 queries are sent to the desired area in the third hop far from Sink. In order to perform this simulation, energy consumption of each query is computed and compared with energy consumed in case stitching is done in Sink. Figure 12 which shows the simulation results of these computations says that the stitching in cluster head has less energy consumption compared to Sink stitching. Inserting these results into the Equation (9) shows that the optimization of the energy consumption using stitching in the cluster head is approximately 26%. In this equation, \(Q\) is the query numbers that arrived from Sink.  
Figure 11: The stitched image in cluster head for the response to 8 th query.