# Multi-Layer Problems and Solutions in VANETs: A Review
## 2 Multi-Layer Challenges in VANETs
### Proposed Solutions
#### 2.2.2 MAC Layer  
1 Synchronization: In the last section we discussed about the need of distributed time synchronization techniques. A comprehensive study of synchronization in VANETs is performed in [30]. The paper explains that the current synchronization algorithms are not suitable for VANETs. It also presents a new hybrid synchronization algorithm based on a whole new architecture which integrates the sensor and vehicular nodes to form a hybrid network (sensor nodes are static roadside equipment in this scenario). This integration of Wireless Sensor Networks (WSNs) with VANETs gives way to the Hybrid Clock Synchronization (HCS). The simulation proves that the algorithm is stable for both high node mobility and low traffic conditions. Research is still required for other distributed synchronization algorithms for VANETs. 2 Congestion Control Techniques: The Congestion Control is one of the most researched areas in VANETs. Figure 3 depicts the basic strategy of congestion control mechanism. The channel is sensed over a period of time and if the channel usage is above a certain threshold, the congestion control algorithm is applied and the channel is sensed again to check the impact of the algorithm. Three basic parameters are used for the congestion control: the transmission power control, packet transmission frequency, and the scheduling of messages in various channels based on priorities. A number of optimization techniques have been applied in the past to solve the congestion problem. One such technique in [31] is a meta-heuristic approach based on Tabu search algorithm for congestion control. The proposed algorithm improves the performance in terms of delay and packet loss, under varying traffic density. The impact of decentralized congestion control on contention based forwarding in VANETs is highlighted in [32]. The Decentralized Congestion Control (DCC) has been surveyed in [33] for the periodic beacon broadcast. It presents a detailed study on the classification of DCC schemes for the V2V safety communication. A dynamic congestion control scheme (DCCS) is presented in [34] whereby the control channel is monitored using self-originated event-based detection and neighbor-originated event-based detection along with adaptive congestion control. The aforementioned method has shown to improve performance in terms of low delay, lesser packet loss, and high throughput. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Challenges** & \multicolumn{2}{c}{**Solutions**} \\ \hline Single/Dual Antenna & \
\begin{tabular}{c} Dual Antenna for better spectrum utilization at the \\ cost of higher complexity and cost of deployment. \\ \end{tabular} & [25] \\ \hline Channel Selection and Estimation &
\begin{tabular}{c} Mitigation of the effects of imperfect \\ Channel State Information \\ \end{tabular} & [26; 27] \\ \hline Communication Range & Antenna Diversity (MIMO) & [28] \\ \hline Spectrum Scarcity & Cognitive Radios & [29] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Physical Layer. The congestion control algorithms are broadly categorized in three categories:  
* Proactive Congestion Control: The proactive algorithms work by estimating the transmission parameters on the basis of number of nodes in the neighborhood. It is an open loop strategy and as the name suggest, the Proactive Congestion Control involves preventing the congestion before it can happen. The famous proactive algorithm is the Distributed Fair Power Adjustment in Vehicular Ad hoc Networks (D-FPAV) [35]. It can achieve the Congestion Control by adjusting the transmission power while the node observes the vehicles in its vicinity. In [33] the author made a performance analysis to compare the static and dynamic D-FPAV with dynamic D-FPAV showing better performance. * Reactive Congestion Control: The Reactive Algorithms employ the information of channel conditions and then decide how to control the congestion by tuning the transmission parameters.