## 6 Experimental Results
### OTV Algorithm: Power Consumption on Embedded Devices  
Here, we evaluate the performance of the setup configurations previously outlined in terms of power consumption and execution time.  
Figure 7 shows the execution time of the standalone OTV algorithm with the three computing platforms, for the two resolutions F and H, and with color and monochrome images. Not surprisingly, the Raspberry 4 is always significantly faster than the other two devices; on the other hand, the Raspberry Pi 3B is always the slowest one, although not far from model 3B+. We can also notice the same behavior when processing monochrome images but with a much shorter execution time, halved in most cases. Taking advantage of the four cores of the integrated CPU and the SIMD instruction set allows always for achieving the best performance enabling a notable speed-up compared to the baseline. In the optimal SIMD/Multicore configuration, processing a 20-s video stream takes a maximum of about 150 s (model 3B, full resolution colored images), or a minimum of time comparable to the processed video length (model 4, half resolution monochrome images). Interestingly, processing colored images with model 4 with the most efficient SIMD/Multicore configuration  
Figure 6: Outcome of the OTV algorithm for color and monochrome video streams at full (F), half (H), and quarter (Q) resolutions.  
at both resolutions takes the same time needed for processing monochrome images with model 3B.  
In Figure 8, it is shown that power consumption is not related to image resolution or SIMD; on the other hand, it conspicuously increases using all four CPU cores. Finally, from the power consumption point of view, using gray-scale images does not really matter even if it can be mildly beneficial using multicore. In the SIMD/Multicore configurations, the power required by model 3B is less than three watts, while, for model 4, it is higher than four watts in most cases.  
Figure 7: OTV algorithm: execution time at (**Top**) full resolution and (**Bottom**) half resolution.  
By combining execution time and power consumption, we can obtain the most relevant figure of merit: the energy required to carry out a single measurement with the OTV algorithm. Before doing so, we recall that energy \(E\) consumed by a device powered with voltage \(V(t)\) and draining current \(I(t)\) in the interval [\(T_{1}\), \(T_{2}\)] is the integral of power consumption \(P(t)\) over time:  
\[E=\int_{T_{1}}^{T_{2}}P(t)dt=\int_{T_{1}}^{T_{2}}V(t)I(t)dt\]  
Observing Figure 9, it can be seen that processing gray-scale images at lower resolution is always beneficial in terms of energy consumption to perform a single measure. Specifically, monochrome images always allow for at least halving the energy drained by all devices. Image resolution also plays a beneficial yet less evident effect. An interesting outcome of the evaluation reported in Figure 9 concerns the fact that the Raspberry 3B+ turns out to be the most energy-consuming model, thus the worst choice when facing self-powered systems. Concerning the other two devices, although there is not an overall winner for each configuration, the device requiring less energy is always the Raspberry 4 configured to take advantage of SIMD and multicore capabilities.  
Figure 8: OTV algorithm: power consumption at **(Top)** full resolution and **(Bottom)** half resolution.