## 4 Results and Discussion
### Power Consumption Evaluation of the Three DKF Algorithms: Scenario 1
#### 4.1.2 Results from Laboratory Experiments  
For each of the DKF implementation, we measured the current and voltage of the nodes. Figure 6 represents the power profile of sensor S2 in the case of the EDKF, ICF, and SGG-ICF implementations. The average power consumed by the sensor node S2 for a period 1 h was found to be 135.616 mW for ICF and SGG-ICF, while it was 140.064 mW for EDKF. From these results, we can deduce that the lifespan of sensor node S2 when powered by the 2000 mAh capacity Li-Po battery will be approximately 54 h for ICF and SGG-ICF, and approximately 52 h for EDKF. Thus, the lifespan for the sensor node is approximately 2 days for all the DKF implementations. We also observed that the difference in power consumption for EDKF when compared to ICF and SGG-ICF was not as significant as what we had obtained from simulations. However, the results of the laboratory experiments validated the high communication burden of the ICF and SGG-ICF algorithms compared to the EDKF algorithm. As shown in Figure 6, the wavelets that occur every 1 s period on the power profile of ICF and SGG-ICF represent the five consensus/ gossip communication iterations that these algorithms have to undertake to achieve an accurate estimate. This shows that the communication burden of these algorithms is high and so is their power consumption. The EDKF has only a single spike in its power profile after every 1 s because it requires just a single communication with the neighbouring node to fuse the estimates. We also observed something unexpected from the power measurement results obtained from the physical experiments. As can be seen from Figure 6, the power consumption of EDKF is higher than that of ICF and SGG-ICF. This is inconsistent with the results obtained from simulations and also from a theoretical perspective. For verification, we powered the sensor node for a period of 1 day using the Li-Po battery and monitored the rate of discharge of the battery's supply voltage. Figure 7 presents an experimental validation of the state of the sensor node battery's voltage over a period of 1 day for each of the DKF implementation. Figure 7 demonstrates the accurate monitoring of the State of Charge (SOC) of the battery experimentally by observing the evolution of the battery's voltage over time. From observation, the battery discharge profiles for all three DKF implementations are found to be approximately the same at the end of one day, as indicated in Figure 7. The SOC of the battery is computed from the battery voltage measurements using Equation (5). Calculating the difference between the SOC at the beginning and end of the one-day experimental period yields the percentage of battery usage over that period. The results of these calculations are presented in Table 5.  
Figure 6: Power profile of the three DKF algorithms. Table 5 shows the battery energy consumption for the three selected DKF algorithms. As can be seen, EDKF has the lowest energy consumption (40.71%), followed by SGG-ICF (41.34%), and lastly ICF (44.53%). These results are consistent with those obtained from simulations. The results also show that there is no significant difference in the energy consumption of ICF and SGG-ICF, which is also consistent with the result obtained from the simulation. However, the difference between the energy consumption of EDKF and ICF is not as significant as was revealed by the simulation results. From the simulation results presented in Figure 5, the energy consumption of the ICF was 8 times higher than that of the EDKF, whereas the results from the laboratory experiments revealed that the energy consumption of the ICF is less than 2 times the energy consumption of the EDKF. One reason for this great difference can be explained by the fact that the CupCabon simulator only models the energy consumption of the communication unit and does not model the energy consumption of the microcontroller and the sensor.