# Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study
## 4 Quantitative Performance Comparison via Simulation
### Delay Model
An expected outcome was to keep the nodes alive as long as possible, after which the sudden death of the majority of the nodes would occur over a given time. This outcome ensures a load-balancing data-collection mechanism in terms of energy consumption. To analyze the delay performance, we only considered the mobility of the sink nodes. The propagation delay is very low compared to the physical mobility of the sink. Two variables play a major role in the given delay analysis. The delay was compared to the area of the ROI. This figure also shows the delay-scalability performance of the adopted mobility model. From Figure 9, it can be seen that in the initial area size of 100 \(\times\) 100 m\({}^{2}\) to the last area size of 400 \(\times\) 400 m\({}^{2}\), NDCMC performed better than the IoJHR protocol. Figure 8: Analysis of sensor node lifetime. Figure 7: Analysis of energy performance. Not only did NDCMC perform better than the IoJHR protocol, but the delay also remained more or less consistent for NDCMC, whereas the delay exhibited a sharp increase with increasing area for the IoJHR protocol. This is because of the adopted sink node mobility models in the respective protocols. According to Figure 4, the IoJHR protocol follows a sink node mobility model, in which it moves back and forth in the ROI to collect data from the CHs. The mobility model ensures that data transmission occurs after reaching the optimal distance from a CH (but with increasing area size, the sink needs to travel an equal added distance). For NDCMC, the sink node follows a predefined mobility, where the stopping position is fixed by default. The mobility of the sink node in NDCMC is maintained using the greedy algorithm. It slightly increases with the increasing area of the ROI but does not entirely depend on this area. In Figure 6, the mobility model for NDCMC is explained. This is important because ground-based robot mobility is not easy to plan as it depends heavily on the type of terrain. Figure 10 illustrates the comparison of the control packet analysis among the investigated data-collection mechanisms. The data is collected by running the simulation five times, where each time consists of ten rounds. From the data, the average number of control packets consumed for a single round is depicted in the graph. Based on the theory and working mechanisms given in the original papers, the NDCMC and IoJHR protocols make use of GPS information. However, the HEED protocol does not require GPS assistance. The HEED clustering process consumes routing packets to form clusters and to select the CHs. NDCMC selects only the CH, and clustering is performed geographically. Mobile sink-based frameworks assume that the sensor nodes already know the predefined path of the mobile sinks. Another major assumption made by the IoJHR protocol is that the sensor node also knows the operating time and speed of the mobile sink so that the CHs can deliver data at the exact moment the mobile sink is in the vicinity. Although this is a drawback of the system, this mechanism helps to reduce the control packet consumption of the compared protocols. The NDCMC framework also utilizes GPS information. However, this framework receives beaconing assistance to alert the sensor nodes to start sending data. This criterion results in the consumption of more control packets. A cluster is formed in the BS, and the nodes do not need to form a cluster on their own, resulting in a smaller number of control packets. Conversely, in the HEED protocol, the mechanism does not require any help from GPS. Consequently, to form a cluster and the CH, many control packets need to be exchanged. As a result, the number of control packets in the HEED protocol is the largest among the investigated protocols. For these results, we used 30 rounds of data and then averaged the total number of control packets per round. Figure 9: Analysis of delay performance. Figure 11 shows the number of packet drop scenarios among the investigated WSN data-collection methods.