# Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform
## 5 PEAR Solution: Hardware Integration and Results Evaluation
### The One Profile Test Series  
The first scenario employs 70 field devices, one profile cluster and multiple energy profiles. The battery capacity is considered 25 A equivalated to 90 Wh of stored energy. Considering all one profile cluster tests, the average number of Tx and Rx packets is 110 and 134 DPDUs/min, respectively which represents 49.8 uWh/min. The average consumption would correspond to an energy profile of 118 DPDUs/min. The performance and impact of the PEAR algorithm are given in comparison with a similar test where PEAR is not enabled labeled as Non-PEAR. Moreover, the PEAR performance evolution will be analyzed at intermediary stages marked by the number of topology changes. The test scenario aims to (1) demonstrate the PEAR capability to add energy awareness within a profile cluster and (2) to give an empirical baseline of the overall network energy needs to assure the WSN performance requirements. The network energy baseline will be referenced when defining the test scenarios with two and three profile clusters. The Table 1 below shows the PEAR success rate as the total field devices respecting the energy constraints for a series of energy profiles. More specifically, it represents the rate of the field devices having the average number of Tx/Rx packets per minute lower than the corresponding energy profile. This metric is evaluated for multiple energy profiles ranging from 100 DPDUs/min to 450 DPDUs/min. When all 70 field devices have the energy profile set to 450 DPDUs/min, PEAR achieves a 100% success rate. However, when exceeding the 200 DPDUs/min profile, an increase of the energy profile has a low impact on the number of devices respecting the energy constraints. A 250 DPDUs/min increase in the energy profile determines only a 10% improvement of the success rate. This behavior appears when PEAR cannot lower the energy consumption of a sink field device because it does not have a better topology alternative without causing network instability. On the other hand, for profiles lower than 200 DPDU/min, a small increase in the energy profile generates a high increase in the success rate. This demonstrates that PEAR can rapidly and efficiently balance the consumption when the energy margin is positive. The Figure 8 depicts the average daily energy overconsumption of a field device considering different energy profiles. It can be observed that PEAR can significantly improve energy prediction and balancing when the energy profile is set above the average consumption of 110 Tx and 134 Rx DPDUs/min. An increase in the energy profile from 100 to 200 DPDUs/min results in a three times overconsumption decrease whereas an increase of the energy profile from 200 to 300 DPDUs/min results in less than two times overconsumption decrease. The nonlinear overconsumption improvement is determined by the PEAR ability to balance the energy within the profile cluster. In the case of an energy profile set to 250 DPDUs/min the overconsumption of the Tx communications increases when compared with the 200 DPDUs/min profile setting. This result reflects a dynamic network resources allocation mechanism where a few devices have a large energy consumption that PEAR cannot adjust without affecting the network stability and performance. A more comprehensive view of the PEAR workings and performance is given in Figure 9 by evaluating the average field device overconsumption for one profile cluster with 70 devices and a 200 DPDUs/min (i.e., 24 months lifetime) energy profile. The energy overconsumption is given at several intermediary stages concerning the number of topology changes triggered by PEAR. Moreover, the performance is assessed in comparison with a similar test where the PEAR support is not enabled.