# Collaborative Caching for Implementing a Location-Privacy Aware LBS on a MANET
## 6 Performance Evaluation
### Experimental Setup  
The experiments were conducted on an Intel(R) Core (TM) i7-9750H 2.60 GHz with 16 GB memory running Java SE 8. Each experiment was repeated five times to calculate the mean values, following statistical significance practices established in similar LBS privacy studies [2; 6].  
Simulation Parameters and Baseline Design  
Our experimental design is grounded in established MANET and LBS research.  
* _Spatial Configuration:_ We model a city environment using a 12 \(\times\) 12 grid of disjoint cells, each sized 10 \(\times\) 10 steps (\"\(p\)\" units) to simulate walking distances. This cell granularity aligns with spatial partitioning approaches from previous LBS privacy studies [6; 10].  
* _Node Density:_ We deploy 400 nodes by default, following density patterns established in [10] and validated in collaborative caching schemes [29]. This density ensures sufficient peer availability while maintaining realistic urban scenarios.
* _Mobility Model:_ Nodes follow a random walk pattern with speeds from 2 to 6 steps/s, simulating pedestrian movement. As shown independently by [32; 33], random walk effectively models individual movement behavior without constraints of physical structure. While more sophisticated models exist, random walk provides conservative performance estimates with minimal assumptions.
* _Cache Configuration:_ Cache capacity is intentionally restricted to reflect the resources of mobile devices [34]. Although cache hit rates can improve with larger caches (up to 30 elements), we focus on low-capacity configurations (2-6 POIs) to realistically model resource-limited mobile devices. Our results demonstrate that even with this restricted range, we achieve high hit rates while maintaining reasonable communication costs. We evaluated three variants of the system:
* _Baseline:_ A non-collaborative MANET-LBS with individual caches and no cell exit protocols, representing the simplest distributed implementation and it serves as our lower bound.
* _M-LBSv1_: Our proposed system with redundancy-optimized cell exit protocol.
* _M-LBSv2_: Our proposed system with communication-optimized cell exit protocol.  
The evaluation uses four key metrics established in distributed caching and privacy literature:  
* _Communication cost (CC):_ Number of messages exchanged during simulation [10], measuring system overhead.
* _Redundancy (RR)_: Proportion of duplicate POIs in user caches [35], which is measured at both city and cell levels. For cell-level analysis, we calculate the mean redundancy across cells containing users.
* _Hit rate (HR)_: The cache hit rate [6], which is defined as \[\text{Hit rate (HR)}=\frac{\text{NH}}{\text{NH}+\text{NM}}\] (7) where NH represents successful cache hits (answers found in local/neighbor caches) and NM represents cache misses requiring LBS access.
* _Number of accesses to the LBS (ACC)_: Frequency of LBS server queries when MANET responses fail [7], directly indicating privacy exposure risk.  
Our experiments fix the city size, simulation time (300 s), and cell size while varying key parameters identified in privacy-preserving LBS literature [2; 6]. The default configuration follows established benchmarks:  
* Simulation time = 300 s (steady-state behavior) [29];
* Nodes = 400 (urban density patterns) [10];
* Cache size = 4 (balancing storage and hit rate) [6];
* Speed = 4 p/s (average pedestrian movement).