## 5 Performance Evaluation of EMOR
### Results  
#### 5.2.1 Link Connectivity  
As from the Section 4.3, \"Construction of Opportunistic Connection Random Graph\", we know that link connectivity between a mobile sink node and any sensor node or between sensor nodes depend on several parameters like time-frequency parameter, residual energies of both nodes, remaining buffer size of both nodes, and link quality factor. Two scenarios are designed to conduct simulations to evaluate link connectivity. In the first scenario, the link connectivity is calculated between mobile sink node and sensor node. In this scenario, we assume that mobile sink node is always in working status but the sensor node, depending on its working-sleeping cycle, performs certain status transitions, so the link connectivity if calculated between the mobile sink node and any sensor node, depends on the time frequency (TF) parameter of that sensor node, residual energy of that sensor node, remaining buffer size of that sensor node, and signal-to-noise ratio between that sensor node and mobile sink node. In this scenario, we consider multiple sensor nodes with different values of TF, residual energy, and link quality factor to evaluate their link connectivity against mobile sink node. Also, as we have assumed that the mobile sink node always remains in the working state, so its energy is always 100% and the total working time of sensor nodes is 75% of data collection duration \(T_{CP}\). Figure 7 depicts that the overall link connectivity decreases with the increase in max status transition frequency. The main reason will be the reduction in residual energies of nodes and the decreased status transition frequencies of individual nodes. For nodes with higher status transition frequency, the link connectivity is higher. Foregoing in view, the higher status transition of a node will create increased opportunities of sensing data sharing between that node and mobile sink node, thus improving the link connectivity between them but at the same time, for higher status transitions of a node, there will be a corresponding decrement in the residual energy as the power is being consumed every time (waking up and sleeping) a node undergoes a status transition as shown in Figure 2.  
In the second scenario, the link connectivity is calculated between different adjacent sensor node pairs. In this scenario, we assume that all the sensor nodes, depending on their working-sleeping cycle, perform certain status transitions, so the link connectivity if calculated between adjacent sensor node pairs, depending on the TF parameter of those sensor nodes, the residual energy of those sensor nodes, the remaining buffer size of those sensor nodes, and signal-to-noise ratio between that node pair. In this scenario, we consider multiple sensor nodes with different values of TF, residual energies, and link quality factors to evaluate their link connectivity between them. Figure 8 depicts that the overall link connectivity decreases with the increase in maximum status transition frequency. The main reason will be the decreasing status transition frequencies of different node pairs and cumulative reduction in residual energies used for simulation. We can see from Table 4 that when node pair status transition frequency decreases from 10 to 4, there is a corresponding effect on the overall link connectivity between each node pair. The difference between the first scenario and second scenario is the mobile sink. When we use a mobile sink and a sensor node for data sharing, we have to consider only the effect of sensor node parameters on the link connectivity whereas in the case of adjacent node pairs, we have to consider the effect of parameters related to the node pair.