# The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks
## 4 Performance Evaluation
### Simulation Results  
#### 4.2.1 Effect of the Number of Queries  
In the first simulation, we varied the number of queries from 1000 to 10,000 and studied the effect of the number of queries on the communication cost and the server computation cost. Figure 7 shows the effect of the number of queries on (i) the communication cost (i.e., the total number of messages communicated between the server and moving objects) and (ii) the server computation cost (i.e., the amount of CPU-time the server takes for query processing). As shown in Figure 7a, the number of queries does not affect the communication cost of CM because, in CM, moving object periodically send location-updates to the server. On the other hand, as the number of queries is increased, the communication cost of DM is also increased. However, DM clearly outperforms CM because, in DM, moving objects utilize their computational capabilities for sending messages to the server only when necessary, i.e., when they (i) leave their current vicinity regions or (ii) enter or leave any of the assigned query segments. This figure also shows that DM with the SSP-tree (denoted by DM\({}_{\text{(SSP-tree)}}\)) performs much better than DM with the naive SSP-tree (denoted by DM\({}_{\text{(naive SSP-tree)}}\)). This is because by using the SSP-tree, where each node maintains the full list, the server can assign larger vicinity regions to moving objects than that using the naive SSP-tree, and thus the moving objects can reduce the number of sending RequestVR messages to the server for receiving new vicinity  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameter** & **Value Used (Default)** \\ \hline Number of queries & 1000\(\sim\)10,000 (**5000**) \\ Query distance & 50\(\sim\)500 (**250**) \\ Number of moving objects & 10,000\(\sim\)100,000 (**50,000**) \\ Minimum computational capability & 10\(\sim\)100 (**50**) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters and their values.  
regions (see the first advantage of the full list describe in Section 3.2). Please note that assigning larger vicinity regions to the moving objects makes them not frequently leave their current vicinity regions. In addition, the moving objects in DM\({}_{\text{(SSP-tree)}}\) can also reduce the number of sending unnecessary UpdateResult messages to the server (see the second advantage of the full list describe in Section 3.2). As compared to CM and DM\({}_{\text{(naive SSP-tree)}}\), on average, DM\({}_{\text{(SSP-tree)}}\) incurs 12.6% and 66.2%, respectively, of the communication cost.  
As shown in Figure 7b, the performance of all methods in terms of the server computation cost degrades as the number of queries is increased. However, as expected, CM performs much worse than DM\({}_{\text{(naive SSP-tree)}}\) and DM\({}_{\text{(SSP-tree)}}\) because, in CM, the server checks all moving objects if they affect the current results of all queries. It is also observed from this figure that DM\({}_{\text{(SSP-tree)}}\) outperforms DM\({}_{\text{(naive SSP-tree)}}\). In both methods, the amount of CPU-time the server takes is mainly affected by the search process for assigning vicinity regions to moving objects. As already mentioned above, because of the first advantage of the full list, the SSP-tree helps the server assign larger vicinity regions to the moving objects than the naive SSP-tree does. As a result, the server in DM\({}_{\text{(SSP-tree)}}\) can reduce more CPU-time than that in DM\({}_{\text{(naive SSP-tree)}}\). As compared to CM and DM\({}_{\text{(naive SSP-tree)}}\), on average, DM\({}_{\text{(SSP-tree)}}\) takes 32.3% and 63.7%, respectively, of the amount of CPU-time.