# Location-Based Lattice Mobility Model for Wireless Sensor Networks
## 4 Simulation Setup and Analysis of Results
#### (A) Average delay and average power consumption  
In Figures 4 and 5, we show the average power consumption based on increased event range and forwarding range. The sensor node within an event area reports to the border node in each region. The event area is centered at (300, 300) meters. The figures show the simulation runs for the high moderate traffic rate. The mobility is set to 50% and an event range is 25 and 50 m around the center in Figures 4 and 5, respectively. There are 200 sensor nodes, which participate in the event and use 42 sources (activities performed in each event). \begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline Size of WSN & 1600 \(\times\) 1600 m\({}^{2}\) \\ Size of each region & 400 \(\times\) 400 m\({}^{2}\) \\ Number of nodes & 500 \\ Routing Protocols & Pheromone termite routing protocol \\ Medium Access Control Protocol & BN-MAC \\ Transport layer Protocol & Reliable Multi Segment Transport protocol (RMST) \\ Queue-Capacity & 30 Packets \\ Mobility Model & WMM, RWMM, CMM, and LMM \\ Maximum number of retransmissions allowed & 03 \\ Event distances & 25 and 50 m \\ Size of Packets & 128 bytes for file transfer and 512 bytes for video, audio, and images \\ Data Rate & 300 kilobytes/s \\ Time for topology change & 1.2 s \\ Propagation model & Deterministic \\ Sensing Range of node & 15 m \\ Forwarding range & 4–40 m \\ Maximum bandwidth of node & 250 kilobytes/s \\ Simulation time & 14 min \\ Average Simulation Run & 15 \\ \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters and corresponding values. In Figures 4 and 5, we demonstrate that in the situation of high moderate traffic rate, which is now common in WSNs due to the emergence of new mobility based applications, reducing end-to-end delay is of paramount significance for obtaining improved throughput. Thus, we validate that end-to-end delay can be reduced by extending the forwarding range. This is a significant trade-off that has not been investigated until now. In addition, we have validated that LMM outperforms CMM, WMM and RWMM in walking mobility patterns. Furthermore, RWMM performs poorly in reduced and extended forwarding range. In Figures 6 and 7, we show an average power consumption using short and long event distances and forwarding ranges. Figure 4: Average delay versus forwarding range at 25 m event range and 50% mobility. Figure 5: Average delay versus forwarding range at 50 m event range and 50% mobility. The event area, the rate of traffic load, event ranges, forwarding ranges, number of participant nodes inside the event, traffic sources, and mobility parameters are the same as those mentioned in Figures 4 and 5. The simulation results demonstrate that extended forwarding range does not affect power consumption or latency. However, LMM consumes less power in short and extended forwarding ranges than CMM, RWMM and WMM. The RWMM also consumes more power than the other participants' mobility models. We also validated based on simulation results that RWMM is not best choice for WSNs from a latency and power consumption point of view. **(B) Time taken by sensor nodes to reach different locations**  
Here, we examine the compatibility of the LMM with sensor nodes. Based on the simulation results, we analyze the time taken for each moving node to reach different positions. We also compare the efficiency of the LMM with the CMM, WMM, and RWMM. Figure 8 presents the distances covered by the sensor nodes using the LMM and the other mobility models. Figure 6: Power consumption versus forwarding range at 25 m event range and 50% mobility rates. Figure 7: Power consumption versus forwarding range at 50 m event range and 50% mobility rates. The LMM is more compatible with sensor nodes because it takes less time to change location than the other mobility models.