## 3 Experiments, Comparative Investigations and Results
By contrast, other existing techniques such as E1, E7 and E8 hold the sensor nodes in an active condition for a more limited period of time than the expected case (20 to 25% of limited downtime). These existing techniques mainly concentrate on the WMAC-based energy efficiency than the node's overall behaviors (routing, advertising, discovery and listening). Figure 8: AECR and number of sensor nodes. From the observations, this article classifies the cross-layered energy-efficient strategies from other techniques, as given in Figure 10. The performance of each cross-layered technique is evaluated using SDDR for multiple data sessions. As denoted, the data communication sessions are populated (20 to 220) and executed for multiple test cycles. Let us assume the number of sessions increase as the number of sensor nodes increases to handle the increasing rate of throughput. In this comparison, the integrated principles of WMAC, routing assistance and scheduler policies of E3 ensure a better SDDR than other works. In any event, E4 manages the SDDR at a higher rate than E3 (94.7%) during initial sessions. The priority calculation and QoS management tasks of E4 give optimal results in SDDR around the network. In contrast, the stability in data delivery needs proper organization of routing strategies at any cost. Accordingly, E3 attains optimal SDDR during the moments of more populated sessions than other works. During this moment, the performance of E5 starts at the lowest SDDR (94.4%) during initial sessions and manages with the average performance between E3 and E4 as it is modelling both balanced power assumptions and balanced scheduling possibilities. Finally, E5 achieves the SDDR of 93.5% against densely populated sessions. Consequently, the changes in the number of retransmissions are crucially noted for the existing techniques such as E3, E4 and E5. The number of retransmissions is closely connected with SDDR and the node's liveliness rate. The SDDR is indirectly proportional to the retransmission rate and directly proportional to the liveliness rate. According to that, E3 produces the minimal number of data retransmissions from 45 to 95 (number of packets) as the increasing number of sessions at each test cycle. In the same manner, the retransmission rate of E4 reaches a higher point for the maximum number of sessions (116). Similarly, E5 produces a moderate load in data retransmission compared to other works (between 55 and 105). The implications of data retransmission for varying number of transmission sessions are given in Figure 11. In the same manner, E6 shows notable contributions in both routing and WMAC policies. E6 achieves balanced routing and medium management policies in WSN. In this case, the observations of E6 performance using AECR, liveliness rate and SDDR are  
Figure 9: Average liveliness rate of sensor nodes. denoted in Figures 8 and 9. The effort of E6 on these metrics is crucial after the implications of E3, E4 and E5. With this in mind, Figure 12 shows the comparative cases of E3, E6, E7 and E8 with respect to computation overhead. In this association, the assumption on the computation  
Figure 11: Total number of retransmissions. Figure 10: SDDR during the sessions. overhead of the existing techniques E1, E2, E4 and E5 are directly mapped with the number of retransmissions taken for each session. In any event, the comparison given in Figure 12 relates the existing techniques E3, E6, E7 and E8 in terms of routing behaviors. These techniques commonly achieve energy-optimized routing solutions in WSNs. The efficient and energy-optimized routing models need to produce minimal computation overhead irrespective of the node's location. Additionally, the processes involved in each sensor node vary depends upon the mobility of sensor node. The challenges taken under mobile sensor nodes lead us to solve new problems such as link breaks, node failures, data retransmission, frequent route updates and energy loss.