## 4 Performance Comparison with Benchmarks
We also observe that different variants of Push-and-track are specified in [6]. For this study, the relevant variant is the _floating data_ scenario, where Push-and-track is used to guarantee that nodes receive a message within a delay tolerance upon entering a specific geographic area. Specifically, at the beginning of the simulation, a number of copies are randomly injected into the seed nodes, and no new copies of the content are re-injected during the simulation. A main difference with respect to OORS is that the content copies are never removed from the nodes' local caches. Additionally, nodes store content copies regardless of their interest in the content. Clearly, this can significantly reduce the content latency, but it leads to inefficient use of resources, such as memory and bandwidth, since nodes might carry and forward messages that are irrelevant to them. Figure 16 shows the performance comparison between Push-and-track, SNSNI, and the optimally tuned OORS for the synthetic mobility model as a function of the global content request rate (\(\lambda_{0}\)) for two network densities. First, we highlight that OORS improves system utility by at least 25% compared to Push-and-track and SNSNI across all the  
Figure 14: Synthetic mobility model: utility vs. \(T_{s}/T_{c}\) ratio for \(\lambda_{0}=0.8\) req/s. (**a**) \(N=100\); (**b**) \(N=50\). Figure 15: Synthetic mobility model: Utility versus the number of nodes for setting \(B\) and \(\lambda_{0}=0.5\) req/s. considered parameter setups. Second, the performance of the three offloading schemes is impacted negligibly by traffic intensity. Finally, the utility value increases as network density increases. To explain these results, we examine the trends of the individual components of the utility function. Figure 15(b) shows that OORS, Push-and-track, and SNSNI achieve similar offloading efficiencies. At the same time, Figure 15(c) shows that OORS requires 45% fewer content copies than Push-and-track and SNSNI. These results confirm that selecting an optimal sharing timeout minimises the buffer space needed to store content copies with a negligible impact on offloading efficiency. It is worth pointing out that the offloading efficiency of Push-and-track and SNSNI is similar, with SNSNI negligibly outperforming Push-and-track. The explanation is that the considered vehicular scenario is homogeneous, with all vehicles having the same centrality values. Thus, the random selection of initial seed nodes used in Push-and-track does not negatively affect the offloading performance. Finally, Figure 17 illustrates the ratio between the optimal \(T_{s}\) and \(T_{c}\) values under the same settings as in Figure 16. Interestingly, in scenarios with low content request rates and high network density, the optimal \(T_{s}\) can be up to forty times longer than \(T_{c}\). However, the optimal \(T_{s}\) rapidly decreases as \(\lambda_{0}\) increases. Figure 18 shows the performance comparison between Push-and-track, SNSNI, and the optimally tuned OORS for the real-world mobility model as a function of the global content request rate (\(\lambda_{0}\)) considering different percentages of seed nodes. The graphs confirm the same trends observed with the synthetic mobility model. The main difference is that the offloading efficiency is lower with the real-world (human-based) mobility model compared to the synthetic (vehicular) mobility model. Interestingly, we note that the offloading efficiency of Push-and-track and SNSNI decreases as the percentage of seed nodes increases. Additionally, it can be observed that SNSNI slightly outperforms Push-and-track in terms of offloading efficiency, especially when the percentage of seed nodes is higher. This can be explained by noting that the human mobility patterns are heterogeneous, and SNSNI is  
Figure 16: Synthetic mobility model: performance comparison between Push-and-track, SNSNI, and optimally tuned OORS as a function of \(\lambda_{0}\) for \(T_{c}=5\) s and setting \(B\).