## 5 Evaluation Results: Influence of Each Layer to the Overall Performance
### NWK Layer
The BeeStack and PLF metrics use a coarse granularity mapping that converts LQI values into link costs, being three steps in the BeeStack metric and seven steps in the PLF metric. On the other hand, the Path-DR metric defines 61 steps when transforming the LQI values into expected LDR values. Metrics that use finer granularity mappings are able to detect a wider spectrum of link qualities and identify high quality links with higher certainty. However, the route length and the link qualities of a route alone do not decide how well a transmission over this route performs. An important factor that is not considered by any of the metrics is found to be network congestion. A higher amount of traffic increases the degree of contention among nodes inside the network, leading to interference and collisions. This directly affects the average number of retries used by the IEEE 802.15.4 MAC layer when transmitting unicast data frames. Table 4 demonstrates that the number of tries observed for each metric increases with the traffic, which is an indicator of congestion. Experiments have shown that LQI values measured with and without internal network interference are almost the same. One reason could be that only the link qualities from the correctly received packets are considered by the link quality-aware metrics, while the packets with the errors that result from collisions or interference are not considered. This leads to the conclusion that the measured LQI values do not serve as indicators for network congestion. Because of this phenomenon, the link quality-aware metrics are not able to identify congested links. If a metric tends to choose longer routes, like the Path-DR metric does, the fact that additional transmissions are necessary with each hop can lead to further congestion and to a lower end-to-end performance. Furthermore, the experiments have shown that the link quality-aware metrics will more likely overuse the links with high LQI values, leading to local congestion. This issue especially affects the Path-DR metric. Figure 14 depicts an example where the Path-DR metric only finds one best route, while the  
Figure 13: Average lengths of routes chosen by different routing metrics and traffic conditions. evaluated ZigBee metrics are able to find three best routes of equal costs over different links. The restriction of Path-DR to routes of very high quality, due to its fine link and route cost granularity, increases the amount of transmission attempts over them and is an important source of congestion. As a consequence, the Path-DR metric performs poorly at medium and high traffic scales, when compared with the ZigBee metrics (including the HC metric), as shown in Figures 10-12. In contrast, the link and path cost granularity of the BeeStack and the PLF metrics is significantly coarser than that of Path-DR. This increases the probability of determining the same path cost for routes that have slightly different link qualities when the BeeStack and the PLF metrics are used. Therefore, the BeeStack and PLF metrics provide greater path diversity, which naturally tends to reduce network congestion. The PLF metric chooses links with high LQI values, but in contrast to the Path-DR metric, it does not restrict its set of available routes to a small subset of optimal routes, as illustrated in Figure 14. The combination of the greater path diversity and sufficient link quality-awareness leads to a better performance of the PLF metric for all traffic scales. On the other hand, the BeeStack metric performs worse than the HC and PLF metrics. The coarse link and path cost granularity is one reason. Figure 2 shows that 70% of all LQI values are mapped to the same link cost, which means that the BeeStack metric assumes that all of the links in this large interval are of similar, if not equal, qualities. The second reason for the inferior performance may originate from the fact that the LQI value is obtained from RSSI measurements of the radio transceiver in the BeeStack implementation, instead of relying on correlation values.