# Adaptive Virtual RSU Scheduling for Scalable Coverage under Bidirectional Vehicle Traffic Flow
## 7 Conclusions  
With the prevalence of autonomous vehicle technology, the distributed RSUs can play a more significant role in the modern city environment. They are not only responsible for message broadcasts, but also can facilitate data dissemination for the large file size content. The efficiency of data dissemination in VANETs' environment will be influenced by the content replication among these distributed infrastructure resources.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Length of road segment & 50 km \\ Vehicle velocity & 0–100 km/h \\ Communication range & 400 m \\ Average bit rate & 6 Mbps \\ Number of replicas & 10,000 \\ Average size of each replica & 200 M \\ Average storage capacity of each vehicle & 2 G \\ Average storage capacity of each RSU & 10 T \\ Simulation time & 2 h \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vehicle-level parameter configuration.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scheduling Strategy**} & \multicolumn{2}{c}{**Cost Saving**} & \multicolumn{2}{c}{**Time Delay**} \\ \cline{2-5}  & **Average** & **Worst Case** & **Average** & **Worst Case** \\ \hline PROPHET & 43.2\% & 23.2\% & 10.8 min & 23.3 min \\ DBC & 44.4\% & 17.6\% & 8.7 min & 25.5 min \\ V2X & 62.8\% & 11.2\% & 5.4 min & 12.5 min \\ ABS & 73.7 \% & 6.3 \% & 16.8 min & 42.2 min \\ CMCS & 60.3\% & 14.3 \% & 11.4 min & 30.9 min \\ HE & 55.2\% & 13.1 \% & 12.8 min & 23.7 min \\ \hline \hline \end{tabular}
\end{table}
Table 3: Relative cost and time delay comparison.  
In this paper, we considered a cloud-based architecture, in which the virtual RSUs can act as the front edge nodes of the cloud platform. The RSU-level cooperation was analyzed in the dynamic environment including both the traffic conditions and request demand distribution. With the exploration of bidirectional vehicle traffic flow, a bidirectional allocation algorithm was presented to allocate the RSUs among different regions with time-varying link capacity. In addition, we formulated content replication in distributed RSUs as the minimum replication set coverage problem in a two-layer mapping model, and analyzed the solutions in different scenarios. Numerical results further proved the superiority of our proposed solution, as well as the scalability to traffic condition variations.  
In order to analyze the service efficiency under diverse traffic conditions, we assume that each type of data communication corresponds to the vehicle velocities in the same or opposite directions, and the link connectivity in each region refers to the data communications and local vehicle density. This simplified model can provide a straightforward view to analyze the impact of traffic conditions in an urban area. On the other hand, it sacrifices the details of the interrupt connections in VANETs. In our future work, we will further explore the RSU allocation and content replication under the impact of the characters in the wireless communications (i.e., the long-range V2I communications and the short-range V2V communications).  
This research is supported by the following grants: National Natural Science Foundation of China (Grant No. 61602214, 61672264, 61702227), Natural Science Foundation of Jiangsu Province in China (Grant No. BK20160191), National Key Research and Development Program of China (Grant No. 2016 YFB0800801), National Science and Technology Support Program of China (Grant No. 2015 BAH54F01), Research Innovation Program for College Graduates of Jiangsu Province (Grant No. SJZZ16_0218, KYLX16_769), Research and Practice on Graduate Educational Reform of Jiangsu Province (Grant No. JGZZ16_065).  
[PERSON] and [PERSON] contributed the algorithm design; [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper.  
The authors declare no conflict of interest.