# Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks
## 5 Performance Evaluation of HEEPS  
To supervise the functionalities of HEEPS, various simulations and measurements are performed. Several parameters are taken into account to measure the power consumption of the CPU.  
Figure 7 shows the Gantt diagrams of the execution of the different tasks. To validate the results of HEEPS and the GEDF scheduler, we check the time parameters through the STORM simulator. Time is displayed in units of 50 ms (\(X\)-axis). Each task is represented by a different color. They are activated simultaneously at \(t=0\). Tasks are migrated between the different queues of the ready tasks in order to have always \(m\) CPU and the \(m\) earliest task deadlines ready to be executed on these \(m\) processors.  
Figure 8 shows that if the CPU frequency changes with DVFS from \(f_{max}=8\) MHz to 6 MHz, the energy is 0.86 J (5.5 V) and 0.63 J (4.05 V), respectively. The energy gain is then 26%. For this purpose, the lower the frequency, the greater the gain, when the 'ET' is increased, the idle time is reduced, which can lead to a delay in execution justified by numerous instructions against a limited CPU processing capacity. The decrease of the frequency induces a nonlinear minimization of the energy dissipation but it achieves an energy saving, as shown in the Figure 8, between 26% and 52%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline F (MHz) & 8 & 6 & 4 & 2 & 1 \\ V (V) & 5.5 & 4.05 & 3.6 & 3.15 & 2.7 \\ \(DVFS\_factor\) & 1 & 0.75 & 0.5 & 0.25 & 0.125 \\ Energy (J) & 0.86 & 0.63 & 0.41 & 0.23 & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Frequency/Voltage Couples Supported by ATmega128L.  
Figure 7: Gantt Diagrams of Simulation Results.  
Figure 9 describes how the number of active processors evolves during the simulation period (500 ms). This number varies between a minimum equal to 0 and a maximum, which is the number of theoretical processors satisfying the feasibility test of GEDF (equal to 5 in our case). It should be noted that this variation follows the workload in the system. By adding more processors, the CPU time and last scheduled task timeout are both shortened. The more we adjust the frequencies, the more the energy gain is important according to the simulation, globally.  
The variation in the number of tasks has a considerable impact on energy consumption. To do this, we started several simulations by modifying the number of tasks. The primary factor in energy consumption variations is the actual workload. As more tasks are performed, more dynamic latencies are generated and therefore more opportunities for DVFS to regain energy. HEEPS may even result in energy savings more than any stand-alone policy in some cases. Every time the tasks number rises, the power consumption rises since the processors in this case are more solicited. To evaluate the reliability of the process, many tasks are deployed first with an AET equal to the WCET and then with a random value.  
Figure 10 states a gain of the order of 11.82% with the same assumptions against 19.5% gain when the value of the AET is random. The use of the WCET value guarantees us an energy gain, but it is obtained at the expense of performance.  
Figure 8: Effect of Reducing Frequency on Consumption.  
Figure 9: Evolution of the Number of Active Processors.