The emergence of in-vehicle entertainment systems and self-driving vehicles, and the latter's need for high-resolution, up-to-date maps, will bring a further increase in the amount of data vehicles consume. Considering how difficult WiFi offloading in vehicular environments is, the bulk of this additional load will be served by cellular networks. Cellular networks, in turn, will resort to caching at the network edge in order to reduce the strain on their core network, an approach also known as mobile edge computing, or fog computing. In this work, we exploit a real-world, large-scale trace coming from the users of the We-Fi app in order to (i) understand how significant the contribution of vehicular users is to the global traffic demand; (ii) compare the performance of different caching architectures; and (iii) studying how such a performance is influenced by recommendation systems and content locality. We express the price of fog computing through a metric called price-of-fog, accounting for the extra caches to deploy compared to a traditional, centralized approach. We find that fog computing allows a very significant reduction of the load on the core network, and the price thereof is low in all cases and becomes negligible if content demand is location specific. We can therefore conclude that vehicular networks make an excellent case for the transition to mobile-edge caching: thanks to the peculiar features of vehicular demand, we can obtain all the benefits of fog computing, including a reduction of the load on the core network, reducing the disadvantages to a minimum.
Specific to vehicular networks, caching has long been studied in the traditional scenario where no infrastructure support is available @cite_16 @cite_17 , as well as in infrastructure-powered cases @cite_18 , where a sparse coverage by road-side units (RSUs) exists. More recent works deal with caching in the context of content discovery @cite_18 @cite_20 , content distribution @cite_23 and content downloading @cite_4 . A common theme of these works is deploying mobile caches at the vehicles, and exploiting past and future mobility in order to ensure content availability throughout the network. Our scenario features fixed caches; however, vehicular mobility and traffic patterns do play an important role in defining the caching needs at different parts of the topology.
@cite_18 @cite_4 @cite_23 @cite_16 @cite_20 @cite_17
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Content caching on the edge of 5G networks is an emerging and critical feature to support the thirst for content of future connected cars. Yet, the compactization of 5G cells, the finite edge storage capacity and the need for content availability while driving motivate the need to develop smart edge caching strategies adapted to the mobility characteristics of connected cars. In this paper, we propose a Mobility-Aware Probabilistic (MAP) scheme, which optimally caches content at edge nodes where connected vehicles mostly require it. Unlike blind popularity decisions, the probabilistic caching used by MAP considers vehicular trajectory predictions as well as content service time by edge nodes. We evaluate our approach on realistic mobility datasets and against popularity-based edge approaches. Our MAP edge caching scheme provides up to 40 enhanced content availability, 70 increased cache throughput, and 40 reduced backhaul overhead compared to popularity-based strategies. Vehicular users are expected to consume large amounts of data, for both entertainment and navigation purposes. This will put a strain on cellular networks, which will be able to cope with such a load only if proper caching is in place, this in turn begs the question of which caching architecture is the best-suited to deal with vehicular content consumption. In this paper, we leverage a large-scale, crowd-collected trace to (i) characterize the vehicular traffic demand, in terms of overall magnitude and content breakup, (ii) assess how different caching approaches perform against such a real-world load, (iii) study the effect of recommendation systems and local contents. We define a price-of-fog metric, expressing the additional caching capacity to deploy when moving from traditional, centralized caching architectures to a "fog computing" approach, where caches are closer to the network edge. We find that for location-specific contents, such as the ones that vehicular users are most likely to request, such a price almost disappears. Vehicular networks thus make a strong case for the adoption of mobile-edge caching, as we are able to reap the benefit thereof -- including a reduction in the distance traveled by data, within the core network -- with little or no of the associated disadvantages. ICN CCN advocates ubiquitous in-network caching to enhance content distribution. Non-safety application in vehicular communication is emerging beyond the initial safety application. However, it suffers from a typical issue of low delivery ratio in urban environments, where high buildings block and attenuate the radio propagation from RSU infrastructures as well as other technical issues. In this paper, LCE in-network caching strategy with LRU algorithm in vehicular networks is proposed according to traffic characteristics in metropolitan areas. We compare this scheme with the legacy TCP IP based scheme by simulation tools of OMNeT++ & Veins and SUMO. The simulation results validate that the proposed scheme could achieve stronger robustness against obstacles, higher file capture rate and less dependency on RSU infrastructure. Vehicular communications are becoming an emerging technology for safety control, traffic control, urban monitoring, pollution control, and many other road safety and traffic efficiency applications. All these applications generate a lot of data which should be distributed among communication parties such as vehicles and users in an efficient manner. On the other hand, the generated data cause a significant load on a network infrastructure, which aims at providing uninterrupted services to the communication parties in an urban scenario. To make a balance of load on the network for such situations in the urban scenario, frequently accessed contents should be cached at specified locations either in the vehicles or at some other sites on the infrastructure providing connectivity to the vehicles. However, due to the high mobility and sparse distribution of the vehicles on the road, sometimes, it is not feasible to place the contents on the existing infrastructure, and useful information generated from the vehicles may not be sent to its final destination. To address this issue, in this paper, we propose a new peer-to-peer (P2P) cooperative caching scheme. To minimize the load on the infrastructure, traffic information among vehicles is shared in a P2P manner using a Markov chain model with three states. The replacement of existing data to accommodate newly arrived data is achieved in a probabilistic manner. The probability is calculated using the time to stay in a waiting state and the frequency of access of a particular data item in a given time interval. The performance of the proposed scheme is evaluated in comparison to those of existing schemes with respect to the metrics such as network congestion, query delay, and hit ratio. Analysis results show that the proposed scheme has reduced the congestion and query delay by 30 with an increase in the hit ratio by 20 .