## 5 Performance Evaluation of EMOR
### Results
#### 5.2.1 Link Connectivity
Keeping in view the weights used in Table 3, we can analyze the decrease in overall connectivity in Figure 8, as compared to link connectivity calculated in the first scenario (i.e., for fv1 = fv2 = 10, we can see a reduction of 25% in the impact of TF parameter for link connectivity in the second scenario, for fv3 = fv4 = 8, we have 40% decrease in the impact of TF parameter for link connectivity). In case of fv5 = fv6 and fv7 = fv8, the effect of TF-parameter in the link connectivity calculation is reduced by 55% and 70%, respectively. Using Equations (4) and (6) and the assumption that total working time of  
Figure 7: Link connectivity between different sensors and mobile sink (based on time frequency (TF) parameter, residual energy, buffer size, and link quality factor) against maximum status transition frequency of nodes in a spanning tree. sensor nodes is 75% of the data collection duration, the effect of the TF parameter on link connectivity calculation can be determined by the following expression,  
\[TF_{ViVj} = \left(\frac{F_{STVi}}{F_{STmax}}\times\frac{(0.75*T_{CP})}{T_{CP}} \right)\left(\frac{F_{STVj}}{F_{STmax}}\times\frac{(0.75*T_{CP})}{T_{CP}}\right) \tag{23}\] \[TF_{ViVSNIK} = \left(\frac{F_{STVi}}{F_{STmax}}\times\frac{(0.75*T_{CP})}{T_{CP}}\right)\] (24) \[\frac{TF_{ViVj}}{TF_{ViVSNIK}} = \left(\frac{F_{STVi}}{F_{STmax}}\times 0.75\right) \tag{25}\]  
where the ratio of the TF parameter for adjacent node pairs to the TF parameter between mobile sink and single sensor node in Equation (25), is used to study the impact or influence of the TF parameter on the link connectivity calculation in the second scenario. In addition to it, the effect of residual energy on the link connectivity calculation can be determined using Equations (26) and (27):  
\[R_{E(1^{a}scenario)}=\beta R_{Ei};R_{E(2^{a}scenario)}=\beta(R_{Ei}*R_{Ej}) \tag{26}\]  
\[\frac{R_{E(2^{a}scenario)}}{R_{E(1^{a}scenario)}}=R_{Ej} \tag{27}\]  
The residual energy ratio in Equation (27) can be used to study the impact of residual energy on the link connectivity calculation in the second scenario. It is quite obvious that the only difference in residual energy impact on link connectivity is residual energy of node \(j\) in the node pair. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Status Transition** & **Status Transition** & **Percentage** & **Overall Link** \\  & **Frequency in First** & **Frequency in** & **Change** & **Connectivity** \\  & **Scenario** & **Second Scenario** & **(\%)** & **Increase/Decrease** \\ \hline \multirow{4}{*}{TF Parameter} & Fv1 = 10 & Fv1 = Fv2 = 10 & 25\% & Decrease \\  & Fv3 = 8 & Fv3 = Fv4 = 8 & 40\% & Decrease \\ \cline{1-1}  & Fv5 = 6 & Fv5 = Fv6 = 6 & 55\% & Decrease \\ \cline{1-1}  & Fv7 = 4 & Fv7 = Fv8 = 4 & 70\% & Decrease \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effect of TF parameter on link connectivity. Figure 8: Link connectivity between different adjacent sensors (based on the TF parameter, residual energy, buffer size, and link quality factor) against maximum status transition frequency of nodes in the spanning tree. For example, if the residual energy of a node is 0.94 in the first scenario and the residual energies of the adjacent node pair are 0.94 and 0.74, subject to the condition that the node used in the first scenario belongs to the node pair in the second scenario, then the percentage change in the impact of \(\mathrm{R_{E}}\) on overall link connectivity will be 26% decreased according to Equation (27) and Table 5. It should also be noted that during our simulation, the effect of buffer size for all nodes is not considered as the percentage change in overall link connectivity because the buffer size was negligible.