# Spiral Mobility Based on Optimized Clustering for Optimal Data Extraction in WSNs
## 6 Experiments and Simulation Results
### Spiral Mobility vs. Random and Grid Mobility
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Figures 10 and 11 show the packet drop and delivery ratio, respectively. The obtained results show that the proposed M-SMOC protocol attains a high packet delivery ratio and a low packet drop ratio, and it can be seen that the packet drop ratio in SMOC is a bit higher than the M-SMOC protocol and that M-SMOC delivers more data packets because of the extra mobility using multiple sinks. Figure 10: Performance comparison of the packet drop ratio. Figure 9: Comparison of the residual energy of sensor nodes with respect to the number of rounds. Efficiency in data delivery is directly proportional to the network connection time, as the nodes know the location of the mobile sink more precisely, and they select more efficient routes for data aggregation. Constituting the fundamental setup of the network, it is the approximation of the elapsed time during which a mobile sink changes a significant position that is noted by the nearby nodes. In the network connection time, the node performs better in data aggregation if the node updates the location of the mobile sink faster. Connection times of the proposed routing protocols SMOC and M-SMOC are shown in Figure 12 with the comparison of ZEEP and TARS. As per the simulation parameters, our proposed routing protocols share the location of the mobile sink and intelligently aggregate the sensed data. In the case of M-SMOC, all the CHs use a subset of route readjustment in the header and greedily share the updated location of the mobile sink with each other. This readjustment header leads the M-SMOC to a faster connection of nodes for the updated location of the mobile sink. Moreover, it can be seen that the proposed M-SMOC protocol achieved a better network connection time because of the multiple sinks in the network. As shown in Figure 13, with the increase in the number of sensor nodes in the network, the network stability achieved by any of the four algorithms also increases. Both the SMOC and M-SMOC algorithms perform better in the comparison of the ZEEP and TARS routing algorithms. This is due to the fact that the proposed spiral mobility pattern covers the whole network region and works better with the involvement of a higher number of sensor nodes. Figure 11: Performance comparison of the packet delivery ratio. Figure 12: Comparison of the network connection time with various sizes of the network. We pay attention to the number of sensors involved, because a higher number of sensors share the total energy consumption and the network response time. If the number of sensors in the network is higher, then these sensors require minimum energy to transmit data, but this will be at the cost of the shorter response time. With the deployment of a higher number of sensor nodes, the network stability of SMOC algorithm is low in comparison with M-SMOC, which is due to the single mobile sink in the SMOC algorithm; whereas the M-SMOC algorithm achieves higher network stability because multiple sinks are moving over CHs. This is the only reason why the network stability of the SMOC algorithm is comparatively shorter. Moreover, with the increase of sensors scattered in the field, the network lifetime of the SMOC and M-SMOC algorithms also improves. In Figure 14, we compare the network lifetime between the proposed algorithms with the impact of different heterogeneity levels in the various sizes of networks. In Figure 14, the considered network dimensions are 100 m \(\times\) 100 m, 150 m \(\times\) 150 m, 200 m \(\times\) 200 m, 250 m \(\times\) 250 m and 300 m \(\times\) 300 m, and the heterogeneity levels are 0.5 J, 1.0 J, 1.5 J, 2.0 J and 2.5 J, respectively. The number of sensors in the defined networks is 100, 200, 250, 350 and 500, respectively. As shown in Figure 14, with the increase of the network dimensions, even if a proportional number of sensor nodes in the network is scattered, the network lifetime is gradually increased in both algorithms.