DeepNDN: Opportunistic Data Replication and Caching in Support of Vehicular Named Data

in 21st IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) (2020, September)

Although many target applications in VANETs are information-centric, the performance of Named Data Networking (NDN) in vehicular ad-hoc networks is severely hampered by persistent network partitioning ... [more ▼]

Although many target applications in VANETs are information-centric, the performance of Named Data Networking (NDN) in vehicular ad-hoc networks is severely hampered by persistent network partitioning, typical of many vehicular scenarios. Existing approaches try to address this issue by relying on opportunistic communications. However, they leave open the crucial issue of how to guarantee content persistence and tight QoS levels while optimizing the resource utilization in the vehicular environment. In this work we propose DeepNDN, a communication scheme based on the joint application of NDN and of probabilistic spatial content caching, which enables content retrieval in fragmented and dynamic network topologies with tight delay constraints. We present a data-based approach to DeepNDN management, based on locally modulating content replication and delivery in order to achieve a target hit ratio in a resource-efficient manner. Our management algorithm employs a Convolutional Neural Network (CNN) architecture for effectively capturing the complex relations between spatio-temporal patterns of mobility and content requests and DeepNDN performance. Its numerical assessment in realistic, measurement-based scenarios suggest that our management approach achieves its target set goals while outperforming a set of reference schemes. [less ▲]

Coordination Mechanisms for Floating Content in Realistic Vehicular Scenario

in Coordination Mechanisms for Floating Content in Realistic Vehicular Scenario (2017)

The increasing interest in vehicular communications draws attention to scalability and network congestion problems and therefore on techniques to offload the traffic, typically carried through the ... [more ▼]

The increasing interest in vehicular communications draws attention to scalability and network congestion problems and therefore on techniques to offload the traffic, typically carried through the infrastructure, to the Vehicle-to-vehicle (V2V) network. Floating content (FC) represents a promising paradigm to share ephemeral content without direct support from infrastructure. It is based on constraining geographically within the Anchor Zone (AZ) the opportunistic replication of a given content among vehicles, in a way that strikes a balance between minimization of resource usage and content availability. Existing works on FC performance modeling are based on standard, homogeneous synthetic mobility models, and it is hence unclear how they actually fit in realistic mobility scenarios. Moreover, the approaches to FC dimensioning they propose assume users have full knowledge of Spatio-temporal mobility patterns, which is hard to achieve in practice. Finally, despite FC is an infrastructure-less communication paradigm, some form of infrastructure support could be available in the vast majority of those application scenarios for which it has been proposed. In this paper, we perform a first attempt at tackling these issues. We focus on how to dimension an Anchor Zone in a realistic vehicular scenario. We propose the first set of simple dimensioning strategies, based on the estimation of some key mobility parameters and of FC performance. We assess such strategies on measurement-based vehicular traces, providing a first indication of their relative performance, and of the feasibility of FC in practical scenarios. [less ▲]