# Multi-Layer Problems and Solutions in VANETs: A Review
## 3 Discussion
The proposed architecture of IoV is based on three layers. The first layer consists of the sensors within a vehicle, the second layer consists of the all the communication between vehicles and sensors,pedestrians, infrastructure, and other vehicles. It also ensures connectivity with existing and the emerging networks like the LTE and Wimax. The third layer consists of the intelligence of the IoV. This includes the storage, statistics and processing capabilities. This layer is involved in the decision making process. Different research papers have proposed different layered architectures. Currently the layered architecture of IoV is under debate and has opened gates for researchers to propose new ideas. Apart from IoV, the Vehicular Cloud (VC) is also gaining much attention recently. Ever since the internet based cloud has come under the light, there has been a tremendous research on the possibility of using the cloud computing with various networks because of the ease of storage, computing resources, and the services on the remote servers with timely availability. Therefore, the vehicular cloud can assist in the development of the intelligent transportation system (ITS). There is a need of new protocols that can aid in managing the resources that are available in vehicle and can assist in cooperation among vehicles. One such peer to peer protocol has been proposed in [67]. Clouding computing based VANETs (CC-V) are researched in [68], where the author has performed an in-depth review of the architecture and challenges of CC-V. The layered architecture, application scenarios, security and privacy and other aspects are presented. Some not so thoroughly addressed issues including data dissemination to cloud, data offloading, security and privacy aware data sharing, and data-centric routing. * Cooperative Multi-hop Communication: Using relay nodes in the VANETs to assist the vehicles in case of emergency or to avoid traffic jams ahead. Each vehicle in network will act like a relay node. The vehicular nodes can also contribute to self-synchronization by using a multihop communication. A new cooperative communication algorithm in case of a network failure is presented in [69]. This new algorithm known as Network Formation Game for MAC-layer retransmissions (NGOMA) reduces the delay at MAC layer due to retransmissions by selecting a suitable relay node in the vicinity to retransmit packet from a source to destination node in case of network failure. This proposed algorithm shows less transmission delays compared to non-cooperative communication. More such algorithms are needed in the future. * 5 th Generation (5G) networks and V2X Communication: The 5G Device-to-Device (D2D) communication will be another cornerstone for the V2X communication. The D2D communication in 3 GPP Release 12 is limited to providing communication in regions where network coverage is not guaranteed. Therefore, it has three coverage scenarios: in coverage, out of coverage and partial coverage [70]. Although, the mentioned scenarios are for generic mobile devices, we can extend the concept to the V2X communication. The mobile device in this case could be a moving vehicle with on-board equipment. The idea of 5G vehicular networks would be highly intelligent, secure, always connected, and mostly autonomous vehicles. Recently a major research trend in vehicular communication is aimed at the LTE V2X (Vehicle-to-Everything). which is based on the D2D Communication. The most important area is the resource allocation in LTE-V2X [71; 72]. Various techniques have been introduced in order to solve the resource allocation problem through optimization [73; 74]. The resource allocation problem has been divided into optimal power and frequency (resource blocks) assignment in order to reduce the interference from the other users. Matching [75], Graph Theory [76], and Machine Learning [77] techniques have been used in order to allocate the resources optimally. Resource allocation in LTE based vehicular networks still remains a topic of interest in the research community.