# An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors
## 5 Experimental Results
\[P_{Wind}=\frac{1}{2}\eta\ A\ \rho c_{p}\hat{\sigma}_{AVG}^{3} \tag{8}\]  
where \(A\) is the swept area of the blades, \(\rho\) is the density of air, \(\eta\) is the efficiency of the rectifier and the DC-DC converter, \(v_{avg}\) is the measured average wind speed and \(c_{p}\) is the power coefficient, which is a function of the tip-speed ratio (\(TSR\)) of the wind turbine [34]. \(c_{p}\) is determined using both analytical formulas and measurements in the lab with controlled wind-speed and direction. In general it is less than 0.1 for small size turbines. Because the power factor is a function of the \(TSR\), it is indirectly a function of the wind speed. In that case the \(c_{p}\) factor cannot have fixed value and must be corrected for different values of the wind speed. The manufacturers of wind turbines usually give a \(TSR\)_vs._\(c_{p}\) curve. This curve was not available for this work and a characterization of the turbine had to be performed. The characterization was conducted experimentally by measuring the \(RPM\) and the power produced at different wind speeds; whereas the power available in the wind, and the power factor were calculated using Equations (8) and (9). Figure 4 describes the curve that was generated from the experiment. To be able to use this in the simulation, an interpolation of the measured data was performed. The formula derived with the interpolation was used to correct the \(c_{p}\) factor Equation (10). \[c_{p}=\frac{P_{out}}{P_{Wind}} \tag{9}\]  
\[c_{P}=0.102v_{avg}^{-1.15} \tag{10}\]  
When calculating the power extractable from the wind, the wind turbine cut in speed has to be taken into account. For the selected wind turbine the cut in speed was measured to be 3 m/s. Figure 4: Average wind speed _vs._ power factor \(c_{p}\). Because the wind speed in deployment (I) was an average wind speed over a period of 10 s, the harvested energy calculated in the simulation for this case must also be for intervals of 10 s. Hence, the energy consumed (\(E_{consumed}\)) in the simulation for every cycle was calculated over a period \(T\) of 10 s using Equation (11). Finally the total energy available in the system at each time (\(t\)) is defined by Equation (7) where \(E_{LEVEL}\) is the available energy in the battery after every cycle  
\[E_{consumed}\ =\ E_{ACTIVE}\left(T\ \ f_{EASA}\right)\,+\,P_{SLEEP}\left(T\ \ - \left(T\ \ f_{EASA}\right)\right) \tag{11}\]  
Figure 5 shows the estimated power in the wind calculated using Equation (8) with the measured average wind speed data from deployment (I) and Equation (10) for calculating of the power factor. The values of the rest of the parameters were as follows: efficiency was set to \(\eta\) = 0.56, which reflects the efficiency of the DC-DC conversion and the AC-DC rectifier inside the wind turbine. The density of the air was taken to be \(\rho\) = 1.2 kg/m\({}^{3}\) (at \(T\) = 15 \({}^{\circ}\)C). To be able to precisely replicate the energy profile of the Waspmote in deployment (II), an analysis of the energy consumption of the system in various operational states was conducted. A state machine representation of the simplified operation was developed and implemented (Figure 6) replicating the firmware used in deployment (II). Figure 5: Harvested power from the wind during the 35 days of experiment—deployment (I). Figure 6: State machine for platform used in deployment (II). Table 2 summarizes the results of the power consumption analysis of deployment (II). It shows the average current and energy consumption of the Waspmote in different states of its operation, together with the duration of each of the states in deployment (II). The current profile is obtained using an Agilent N6705B DC Power Analyzer configured to sample the current consumption of the node every 5 ms. The figures for the \(I_{AVG}\) in each state are obtained by averaging the measured current over the duration of the individual states. In deployment (II), the time \(t_{\text{ACTIVE}}\) is equal to the sum of the duration of each of the states.