## 5 Evaluation Results: Influence of Each Layer to the Overall Performance
### APL Layer  
At the APL layer, we evaluate the impact on the end-to-end network performance of the five RTO algorithms that have been introduced in Section 3.2 in terms of overall end-to-end PDR and delay, as well as the energy efficiency of the RFDs. Using an adequate RTO value for APL layer data transmissions is important for the end-to-end performance, because of two main reasons. First, the probability of producing spurious retransmissions and, thus, contributing to network congestion increases if the RTO value is set too low. Second, the probability of detecting a loss incurring a large delay increases if the RTO value is set too high. For the low traffic scale, all RTO algorithms show a similar performance, as the number of APL layer retransmissions needed is small. The similarities in the performances apply to the PDR, delay and energy efficiency metrics, as shown in Figures 16-18, respectively. In the low traffic case, most of the end-to-end data transmissions are successful, and for most of them, the average RTT lies below the initial RTO values. As a result, the impact of the RTO algorithms on the end-to-end performance is rather small for the low traffic scale. The higher delay and lower energy efficiency observed when using the CoAP algorithm is an exception, as even a few packet losses cause the average delay and the energy consumption to increase noticeably due to the high initial RTO value used by this algorithm. In the medium and high traffic scales, end-to-end delays get larger due to congestion, and accordingly, the average RTT is augmented. Together with a higher packet loss ratio, the frequency of RTO expirations increases noticeably. For example, when using the default stack configuration with the ZigBee RTO algorithm, compared to the low traffic case, the number of RTO expirations is augmented by 330% at medium traffic and by 813% at high traffic. As RTO expirations become more frequent, the impact of the RTO algorithms on the overall network performance becomes greater. The default algorithm is outperformed by all other algorithms in terms of PDR for medium and high traffic scales, as seen in Figure 16.  
With the medium traffic scale, the average delay obtained with the default ZigBee RTO algorithm lies in between the delay values observed when using the other RTO algorithms. The CoAP algorithm delivers the best PDR performance, but suffers from the highest delays. Similarly, the ZigBee RTO  
Figure 16: PDR results for different RTO algorithms. algorithm with BEB has a larger delay than that of the default RTO algorithm. The PDR and delays of the TCP RTO algorithm and the CL RTO algorithm are very similar, performing better than the default algorithm. The energy efficiency of the different algorithms follows a pattern that resembles qualitatively the curve observed for the end-to-end delay. For the high traffic scale, the CoAP RTO algorithm achieves the highest PDR at the cost of the highest average delay. The other alternative algorithms achieve a similar PDR, lying between the PDR of ZigBee and CoAP RTO algorithms. We observe that like the CoAP algorithm, the RTO expirations of the ZigBee RTO backoff grow exponentially and lead to a higher amount of polls when packets are lost and to a higher energy consumption. The RFC 6298 RTO algorithm performs slightly better, explainable by the lower average delay. The CL RTO algorithm has a low delay and a higher PDR than the default RTO algorithm. As a consequence, the energy efficiency of this algorithm is the highest one. Figure 17: Average end-to-end delays with 95% confidence intervals for different RTO algorithms. Figure 18: Energy efficiency for different RTO algorithms. When the default ZigBee RTO algorithm is used, under medium and high traffic load, the RTT frequently surpasses the 1.5 s of the initial RTO value. Since no RTO backoff is applied, two main effects are observable.