# Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform
## 5 PEAR Solution: Hardware Integration and Results Evaluation
### The Multiple Profiles Test Series
Figure 10: Average field device energy consumption for all devices in the network differentiated by the energy profile when PEAR is enabled. The Figure 11 gives an overview of the PEAR impact on field device lifetime predictability. All field devices respect their energy profile while not aiming to further balance the energy after lifetime expectancy has been achieved. On the other hand, when the energy balancing support is not provided, as shown in Figure 10, one can observe that 18.5% (i.e., 13 devices) of the field devices do not respect the energy profile. Furthermore, three devices from the low cluster exceed not only their energy profile but also the high energy profile. This confirms that an energy balancing favorable deployment does not result in an intra- or intercluster energy balancing. The comparison between the PEAR and Non-PEAR variants of the H20L50 test is given on a cluster basis and considers metrics such as lifetime, average energy consumption and success, as illustrated in Table 4. The Non-PEAR profile clusters lifetime is 11.23 and 9.46 months representing 25.46 and 2.5 months less than the expected cluster lifetime, respectively. Contrastingly, the PEAR clusters lifetime is greater than the corresponding profile lifetime with 5.7 and 53.5 days, respectively. Furthermore, the PEAR low cluster lifetime is 3.2 times larger compared with the Non-PEAR variant. This demonstrates the PEAR ability to balance the energy consumption within the network in order to provide cluster lifetime predictability. The PEAR algorithm also reduces the average energy consumption with 23.6% for the lower cluster and with 25% for the higher cluster. This indicates that when multiple profiles are defined, PEAR further enhances the use of the network energy resources by lowering the overall energy consumption. The success rate metric shows that without the energy balancing support provided by PEAR only 84% of the low-profile devices and 75% of high-profile devices respect their intended life expectancy. Thus, the overconsuming field devices represent a significant percentage of the total field devices arguing that PEAR energy balancing is critical for assuring predictable field device lifetime within a dynamic large mesh network. The low cluster lifetime for all two-profile test configurations is given in Figure 12 including the H20L50 configuration that was detailed previously. For each test configuration, the lifetime of the PEAR and Non-PEAR variants is depicted together with an indication of the low-profile expected lifetime. The distribution of the field devices per profile clusters gives a measure of the intercluster energy balancing opportunities in terms of energy margin and topology choices. More specifically, larger high-consumption clusters give better opportunities for the PEAR algorithm due to a higher overall accepted energy consumption and a larger number of field devices that can reroute data packets without exceeding their energy profile. The PEAR low-profile lifetime is larger as the high-profile cluster is larger whereas the Non-PEAR results are not correlated with the high cluster size. These results indicate that PEAR dynamically capitalizes on different network resources and configurations to improve the cluster lifetime and predictability. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Profile Cluster** & **Energy Balancing** & **Expected Lifetime** & **Lifetime** & **Average Energy Consumption** & **Success Rate** \\  & & **[Months]** & **[Months]** & **[mWh/day]** & **(\%)** \\ \hline Low & PEAR & 36 & 36.69 & 32.6 & 100\% \\  & Non-PEAR & 36 & 11.23 & 11.23 & 84\% \\ \hline High & PEAR & 12 & 13.76 & 1.37 & 100\% \\  & Non-PEAR & 12 & 9.46 & 1.83 & 75\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The comparison between the PEAR and Non-PEAR per profile cluster for the H20L50 distribution. The test configuration considers corner cases where the high cluster size is as large as 50 devices and as small as five devices.