## 3 Traffic Data Sensing and Gathering
### Social Media Feeds  
Closely connected with the notion of smart cities, social media feeds (e.g., Twitter, Facebook, Google+, Waze) can play a crucial role in improving the accuracy of the road traffic information provided by the conventional monitoring equipment (e.g., road sensors, induction loops, etc.) [54; 55]. As an example, revealing the possible causes of an unexpected increase in the congestion levels (e.g., car accidents, road works, or mass political and social events), social media sources may help achieve a more appropriate reaction from the side of the responsible authorities to mitigate the effects of these situations [56].  
At the same time, it is important to note that there is a need to verify the accuracy of the provided data to prevent from exploiting the unreliable sources of information. To this end, several research groups addressed the design of powerful security and privacy preservation solutions [57; 58; 59; 60; 61; 62; 63].  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **Comm.** & **Comput.** & **Scalability** & **Latency** & **Delivery** & **Network** & **Target** & **Infrastructure** \\  & **Overhead** & **Overhead** & **Level** & **Ratio** & **Flexibility** & **Scenario** & **Dependent** \\ \hline
**VADD**[64] & Low & Medium & Medium & Medium & Low & High & Rural & No \\ \hline
**GPCR**[65] & Low & Low & Medium & High & Low & No & Urban & No \\ \hline
**LORA-CBF**[66] & Medium & Low & High & Low & High & Medium & Urban & No \\ \hline
**SADV**[67] & Low & Low & Medium & Medium & Medium & High & Urban & Yes \\ \hline
**UMB**[68] & Medium & Medium & Medium & High & Medium & Medium & Urban & Yes \\ \hline
**ARBR**[69] & Low & Medium & Medium & Medium & High & High & Urban & Yes \\ \hline
**PDCR**[70] & Medium & Medium & Medium & Medium & Medium & No & Urban & No \\ \hline
**MURU**[71] & Low & Medium & Medium & Low & Medium & High & Urban & No \\ \hline
**A-state**[72] & Medium & Low & Medium & Medium & Medium & No & Urban & No \\ \hline
**GyTAR**[73] & Low & Low & Medium & Low & Medium & High & Urban & No \\ \hline
**GVGrid**[74] & Medium & Medium & Medium & Medium & Medium & Urban & Yes \\ \hline
**BROADCOMM**[75] & High & Low & Medium & Low & Low & Medium & Highway & Yes \\ \hline
**V-TRADE**[76] & Medium & Low & Medium & Medium & Low & No & Highway & No \\ \hline
**IVG**[77] & Low & Low & High & Low & Medium & High & Highway & No \\ \hline
**3 rule**[78] & Low & Low & Unknown & Unknown & High & No & All & Yes \\ \hline
**DV-CAST**[79] & Low & Low & High & Low & Medium & Very High & All & No \\ \hline
**CAR**[80] & Medium & Medium & Medium & Medium & Medium & Medium & All & Yes \\ \hline \hline \end{tabular} Used acronyms: **VADD** (Vehicle-Assisted Data Delivery); **GPCR** (Greeedy Perimeter Condoriant Routing); **LORA-CBF** (Location-Based Routing Algorithm with Cluster-Based Flooding; **SADV** (Static-node assisted Adaptive data Dissemination protocol); **UMB** (Urban Multi-hop Broadcast protocol); **ARBR** (Adaptive Road-Based Routing); **PDGR** (Predictive Directional Greedy Routing); **MURU** (Multi-Hop Routing Protocol); **GVTAR** (Greedy Traffic Aware Routing protocol); **GVGrid** (A QoS Routing Protocol for Vehicular Ad Hoc Networks); **BRADCOMM** (Emergency Broadcast Protocol for Inter-Vehicle Communications); **V-TRADE** (Vector based TRack Detection); **IVG** (Inter-Vehicles Geocast); **DV-CAST** (Distributed Vehicular BroadCAST); **CAR** (Connectivity-Aware Routing).  
\end{table}
Table 2: Main characteristics of reviewed vehicular routing protocols.