# Collaborative Caching for Implementing a Location-Privacy Aware LBS on a MANET
## 6 Performance Evaluation
### Effect of Cache Memory Size  
We evaluated system performance across our restricted cache capacity range (2-6 POIs) while maintaining constant node count (400) and speed (4 p/s). Cache size variation is particularly critical as it directly impacts both system efficiency and privacy protection [6]. Our analysis focuses on restricted cache capacities to realistically model resource constraints in mobile devices [34] while examining the performance implications within this constrained range.  
An analysis of Tables 2-4 reveals several significant patterns consistent with distributed caching theory [29]:  
1. _Redundancy Control:_ The baseline system shows consistently high redundancy (24-27%) across all cache sizes due to uncoordinated caching. In contrast, our collaborative approaches maintain significantly lower redundancy levels (M-LBSv1: 4-10%, M-LBSv2: 6-12%), validating the effectiveness of our cell exit protocols in managing data distribution.
2. _Hit Rate Performance:_ Even with restricted cache sizes, both collaborative systems achieve excellent hit rates (up to 0.95), significantly outperforming the baseline (0.91 maximum). This shows that effective coordination can compensate for the limited individual cache capacity.
3. _Communication Efficiency:_ While larger caches reduce communication costs across all systems by increasing local hit probability, this effect is particularly pronounced in our collaborative approaches. M-LBSv1 shows the most significant improvement, reducing ACC from 26,495 to 5404 as the cache size increases from two to six POIs.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Cache Size** & **ACC** & **NH** & **HR** & **Average RR per Cell** & **CC** \\ \hline
2 & 29,194 & 90,806 & 0.76 & 24\% & 3.31 \\
3 & 19,456 & 100,544 & 0.84 & 27\% & 2.58 \\
4 & 14,398 & 105,602 & 0.88 & 25\% & 2.16 \\
5 & 12,374 & 107,626 & 0.90 & 26\% & 1.94 \\
6 & 10,459 & 112,541 & 0.91 & 24\% & 1.78 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cache sizes, baseline.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Cache Size** & **ACC** & **NH** & **HR** & **Average RR per Cell** & **CC** \\ \hline
2 & 26,495 & 93,505 & 0.78 & 4\% & 4.62 \\
3 & 15,387 & 104,613 & 0.87 & 8\% & 4.03 \\
4 & 8087 & 111,913 & 0.93 & 9\% & 3.69 \\
5 & 6401 & 113,599 & 0.95 & 10\% & 3.63 \\
6 & 5404 & 114,596 & 0.95 & 9\% & 3.56 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cache sizes, M-LBSv1.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Management Types** & **Number of Hits** & **Accesses to LBSs** \\ \hline FIFO & 104,664 & 15,336 \\ RAN & 100,469 & 19,531 \\ DIST & 105,302 & 14,698 \\ RST & 77,580 & 42,420 \\ POP & 97,290 & 22,710 \\ MINPD & 100,717 & 19,283 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of cache management policies.  
Figure 3 illustrates that while larger cache sizes improve performance across all systems, we observe diminishing returns beyond size 4, which is consistent with previous studies of LBS caching [6]. This validates our choice of restricted cache sizes, as additional capacity provides minimal benefit while increasing resource requirements. Both collaborative systems consistently outperform the baseline in reducing LBS queries with M-LBSv1 showing superior performance due to its redundancy-first optimization strategy. The results demonstrate that our approach achieves excellent performance even with limited cache capacity, supporting our focus on resource-constrained mobile environments.