## 5 Performance Evaluation
For a further investigation, it is possible to evaluate the standard deviation (averaging values by sectors) of the energy consumption by increasing the number of sectors for all of the configurations:  
Table 2 collects standard deviation values by varying the number of sectors for all of the considered approaches; the use of the SAS slightly enhances the omnidirectional case; however, observe how ABT-RRMAC helps to dramatically decrease values when the number of sectors improves with respect to other considered configurations. This trend confirms that our proposed approach seeks to efficiently distribute the energy consumption among sectors as already noted in plots of Figure 9. Therefore, energy consumption is very related to packet delivery ratio (PDR) and could represent a helpful indication of the overall network performance; for this purpose, the next figure illustrates PDR comparison extracted by _rcxdPkts_ and _sentPkts_ statistics collected by Omnet++. Figure 9: Energy consumption per sectors vs. number of sectors. (**a**) Energy consumption comparison, \(N_{s}=3\). (**b**) Energy consumption comparison, \(N_{s}=4\). (**c**) Energy consumption comparison, \(N_{s}=6\). (**d**) Energy consumption comparison, \(N_{s}=8\). In Figure 10, the average (averaged with respect to nodes) PDR is plotted both as a function of the number of sectors and the queue size. Figure 10a simply reflects the situation verified in Figure 9, in fact, the lower the energy consumption is, the higher the PDR; therefore, when the number of sectors is high, the ABT-RRMAC is able to provide a PDR of about 0.92 against 0.85 registered in the RR-MAC configuration. The same statistic can be evaluated by increasing the size of the queues; in Figure 10b (values are averaged with respect to number of sectors), it seems clear that PDR performance are quite sensitive to queue size, indeed, moderately. low values of PDR are obtained when omnidirectional antennas are used; SASs help to increase the minimum PDR from 0.62 to 0.72; however the minimum PDR value, when our proposed approach is used, is 0.84; this value slightly improves together with the queue size and tends to become almost uniform for the highest queue size values. More specifically, especially for critical queue size cases (from 3 to 5), the difference between RR-MAC and ABT-RRMAC is very significant; this result suggests that our proposed algorithm provides for a great robustness, even when the queue size is small, enabling an acceptable performance in the case of limited resource allocation. The latest set of simulations is accomplished with the aim to test our proposal under the condition of mobility of nodes; for this purpose, we evaluate the energy consumption of nodes by increasing the node mobility speed from 1 to 10 mps; in this case, results are obtained by averaging energy consumption values with respect to the number of sectors. In addition, we compared the worst queue size case (queue size = 3) and the best queue size case (queue size = 10). Figure 11 displays comparison curves related to the energy consumption of nodes in the function of the mobility speed by considering the worst and the best registered case in the function of the size of the queues. Note that continued-line curves depict the worst case plots, while dashed-line curves are related to the best case plots. As regards the omnidirectional case, the overall consumption remains tolerant until 7 mps and then whereupon increases widely; this is mainly due to link failures occurring for high mobility speed values, which results in the enhancement of protocol overhead. However, this rapid increase is limited in the best omnidirectional case. When using an SAS in cooperation with round-robin, disruptive impacts due to the growth of node mobility speed are quite mitigated with respect to the omnidirectional case, especially for high mobility values. Therefore, the effects of