This paper provides an introductory overview of Vehicular Delay-Tolerant Networks. First, an introduction to Delay-Tolerant Networks and Vehicular Delay-Tolerant Networks is given. Delay-Tolerant schemes and protocols can help in situations where network connectivity is sparse or with large variations in density, or even when there is no end-to-end connectivity by providing a communications solution for non real-time applications. Some special issues like routing are addressed in the paper and an introductory description of applications and the most important projects is given. Finally, some research challenges are discussed and conclusions are detailed.

This article proposes an enhanced PRIvacy preserVing Opportunistic routing protocol (ePRIVO) for Vehicular Delay-Tolerant Networks (VDTN). ePRIVO models a VDTN as a time-varying neighboring graph where edges correspond to neighboring relationship between pairs of vehicles. It addresses the problem of vehicles taking routing decision meanwhile keeping their information private, i.e, vehicles compute their similarity and/or compare their routing metrics in a private manner using the Paillier homomorphic encryption scheme. The effectiveness of ePRIVO is supported through extensive simulations with synthetic mobility models and a real mobility trace. Simulation results show that ePRIVO presents on average very low cryptographic costs in most scenarios. Additionally, ePRIVO presents on average gains of approximately 29% and 238% in terms of delivery ratio for the real and synthetic scenarios considered compared to other privacy-preserving routing protocols.

Delay Tolerant Networks (DTNs) are sparse networks where complete direct end-to-end paths between source and destination can seldom be established. Routing mechanisms in DTN rely on the nodes' mobility to connect disconnected nodes, carrying messages around the network to overcome path disconnection. The proactive DTN approach consists of introducing dedicated nodes whose only purpose is to establish communication between ordinary nodes, and relieve them from energy-consuming work, such as message routing and forwarding. This paper addresses the use of Unmanned Aerial Vehicles (UAVs) as DTN relays, introducing a proactive scheme called Deadline Triggered Pigeon with Travelling Salesman Problem with Deadlines (DTP-TSP-D). We envision a DTN where ground nodes can only communicate by flying UAVs with the capacity of carrying messages from one location to another. Each UAV either belongs to one node or to a cluster of nodes, and its role is to hover over their home-ground node (or ferrying around their home cluster) until they are triggered to deliver messages directed to other ground nodes. The triggering criterium is based on the deadlines of the messages present in the UAVs buffer, evaluating its ability to deliver all of them in time. It uses a developed TSP Genetic Algorithm to compute the route that achieves the most (timely) deliveries. The performance of DTP-TSP-D has been compared to dedicated node protocols found in the literature, namely SIRA and MRT-Grid. The performance metrics used were delivery ratio and average delay. The results show that DTP-TSP-D achieves higher delivery rates than its competitors, while keeping a consistent average delay.

The Internet of Vehicles (IoV) is undoubtedly at the core of the future of intelligent transportation. It will prevail over the road ecosystem, and it will have a huge impact on our lives throughout the provision of seamless connectivity among diverse transportation means. For the network to operate efficiently, the data needs to be quickly spread throughout the network, which requires low computational and bandwidth overheads. However, the dynamics of vehicular environments due to frequent node mobility poses many challenges to realize efficient data dissemination. This work addresses this type of problem by proposing a novel clustering algorithm at the edge of the network and an efficient message routing approach, which is known as Group’n Route (GnR). Both mechanisms resort to machine learning and graph metrics that reflect the social relationships between the nodes. Our performance evaluation reveals that the clustering algorithm yields stable results with varying road scenarios, which are becoming an advisable approach in the presence of mobile IoV nodes. Also, the designed routing protocol achieves two orders of magnitude smaller overhead and almost double the delivery rate when it is compared to traditional routing protocols, which thereby justify that the combination of our two proposed clustering and routing methods are a plausible alternative to support IoV communications in real-world setups.