# Analysis of Mobile Edge Computing for Vehicular Networks +
## 1 Introduction
The authors in [6] present experiments that show a simple infrared light emitting diode can suffice for optical communication with a mobile vehicle. In [7] a method for car-to-car, or V2V, is proposed and also uses simple light emitting diodes paired with a PIN photodiode at the receiver. While commodity hardware is more feasible, another approach would be to employ more powerful hardware to operate in a cellular fashion. For example, optical receivers could reside on the cell towers, or RSUs, and act at the relay for communications between vehicles. This would also allow the full bandwidth and throughput of FSO communication to be exploited when uploading data from vehicle to the roadside infrastructure. As noted in [8] an additional benefit of FSO is low power usage and immunity to RF interference. FSO also has some inherent security since the transmission beam will be very narrow and any intrusion would be immediately detected as a drop in signal strength. Immunity to RF interference makes FSO a great candidate for a shared spectrum technology and could perform well alongside existing RF communications. Employing FSO in vehicular communications presents another source of unlicensed spectrum as well as another means for high-capacity wireless communications. With many challenges involved in tracking, alignment, and attenuation, FSO has been explored only for simple applications such as intra-vehicle and V2V communication via LED light sources already available on most modern vehicles. In any case, meeting the communication demands of vehicular networks necessitates a network architecture that can support highly mobile devices while maintaining the ability to handle both real-time messages and delay-tolerant data off-loads. As we progress toward semi, and eventually fully autonomous vehicles, demand for vehicle-collected data will increase greatly. The numerous on-board sensors, cameras, and LiDAR systems are expected to generate Terabytes of data daily for each vehicle [9]. This data will be invaluable to both vehicle manufactures and researchers and can be leveraged to monitor and improve vehicle performance, relieve traffic congestion, and so on. However, accommodating this influx of new data will put additional strain on both networking and computing infrastructure. Addressing the growing demand of future applications has also prompted researchers to explore edge-computing in vehicular networks. With future vehicles expected to possess a reasonable amount of computing resources, researchers have begun to explore the use of vehicles themselves as edge nodes in a distributed cloud system. In [10] the authors present a scheme that employs groups of vehicles as \"micro-clouds\" and can be used to aggregate and pre-process data before it is transferred to back-end resources. Clustering vehicles and electing a cluster-head (CH), allows for the data of multiple vehicles to be aggregated, which can reduce the amount of data communication between the access point, or RSU and reduce overall network traffic by transmitting fewer bytes. Other works have followed similar trends in which they employ computational and storage enabled RSUs to explore some of the hardware requirements for data-gathering in VANETs [11]. In this scenario, RSUs make an event-based decision on which data should be uploaded to the intelligent transportation system (ITS) servers. RSUs acting as a compute layer allows for decisions about which data to collect or reject and are made much closer to the network edge, thus reducing the load on the ITS servers and minimizing the amount of data that is transmitted through the network. Edge nodes with computational and storage capabilities also provide a means for efficient content delivery in vehicular networks [12]. Given sufficient storage space at edge nodes, the network has the potential to cache the contents and ensure a timely delivery to end users. This has added value for any location-based service that provides data to vehicles.