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.
We address content discovery in wireless networks with infrastructure, where mobile nodes store, advertise, and consume content while Broker entities running on infrastructure devices let demand and offer meet. We refer to this paradigm as match-making, highlighting its features within the confines of the standard publish-and-subscribe paradigm. We study its performance in terms of success probability of a content query, a parameter that we strive to increase by acting as follows: 1) We design a credit-based scheme that makes it convenient for rational users to provide their content (thus discouraging free-riding behavior), and it guarantees them a fair treatment. 2) We increase the availability of either popular or rare content, through an efficient caching scheme. 3) We counter malicious nodes whose objective is to disrupt the system performance by not providing the content they advertise. To counter the latter as well as free riders, we introduce a feedback mechanism that enables a Broker to tell apart well- and misbehaving nodes in a very reliable manner, and to ban the latter. The properties of our match-making scheme are analyzed through game theory. Furthermore, via ns-3 simulations, we show its resilience to different attacks by malicious users and its good performance with respect to other existing solutions. We consider a system where users aboard communication-enabled vehicles are interested in downloading different contents from Internet-based servers. This scenario captures many of the infotainment services that vehicular communication is envisioned to enable, including news reporting, navigation maps, and software updating, or multimedia file downloading. In this paper, we outline the performance limits of such a vehicular content downloading system by modeling the downloading process as an optimization problem, and maximizing the overall system throughput. Our approach allows us to investigate the impact of different factors, such as the roadside infrastructure deployment, the vehicle-to-vehicle relaying, and the penetration rate of the communication technology, even in presence of large instances of the problem. Results highlight the existence of two operational regimes at different penetration rates and the importance of an efficient, yet 2-hop constrained, vehicle-to-vehicle relaying. 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. Future vehicular networks are expected to deploy short-range communication technology for inter-vehicle communication. In addition to vehicle-to-vehicle communication, users will be interested in accessing the multimedia-rich Internet from within the vehicular network. This motivates a compelling application of Co-operative Networking in the Vehicular Ad-Hoc network where the Ad Hoc network extends and complements the Internet. The broadcast nature of the wireless medium drives us to explore different design paradigms from the ones used in typical wired settings.A new paradigm in content delivery on the Internet using peer-peer swarming protocols is emerging [1,2]. We propose SPAWN, a simple cooperative strategy for content delivery in future vehicular networks. We study the issues involved in using such a strategy from the standpoint of Vehicular Ad-Hoc networks. Several enhancements to a popular swarming protocol (BitTorrent) are discussed including a gossip mechanism that leverages the inherent broadcast nature of the wireless medium, and a piece-selection strategy that uses proximity to exchange pieces quicker. Preliminary results show that SPAWN increases the perceived performance of the network, resulting in faster downloads for popular files. 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 . There has been increasing interest in the exploitation of advances in information technology in surface transportation systems. One trend is to exploit on-board sensing, computing and communication capabilities in vehicles, e.g., to augment and enhance existing intelligent transportation systems. A natural approach is to use vehicle-to-vehicle communications to disseminate information. In this paper, we propose MDDV, a mobility-centric approach for data dissemination in vehicular networks designed to operate efficiently and reliably despite the highly mobile, partitioned nature of these networks. MDDV is designed to exploit vehicle mobility for data dissemination, and combines the idea of opportunistic forwarding, trajectory based forwarding and geographical forwarding. We develop a generic mobile computing approach for designing localized algorithms in vehicular networks. Vehicles perform local operations based on their own knowledge while they collectively achieve a global behavior. We evaluate the performance of the MDDV algorithm using realistic simulation of the vehicle traffic in Atlanta area.