Optimal Content Prefetching in NDN Vehicle-to-Infrastructure Scenario

Data replication and in-network storage are two basic principles of the Information Centric Networking (ICN) framework in which caches spread out in the network can be used to store the most popular contents. This work shows how one of the ICN architectures, the Named Data Networking (NDN), with content pre-fetching can maximize the probability that a user retrieves the desired content in a Vehicle-to-Infrastructure scenario. We give an ILP formulation of the problem of optimally distributing content in the network nodes while accounting for the available storage capacity and the available link capacity. The optimization framework is then leveraged to evaluate the impact on content retrievability of topology- and network-related parameters as the number and mobility models of moving users, the size of the content catalog and the location of the available caches. Moreover, we show how the proposed model can be modified to find the minimum storage occupancy to achieve a given content retrievability level. The results obtained from the optimization model are finally validated against a Name Data Networking architecture through simulations in ndnSIM

Context-aware pending interest table management scheme for NDN-based VANETs

In terms of delivery effectiveness, Vehicular Adhoc NETworks (VANETs) applications have multiple, possibly conflicting, and disparate needs (e.g., latency, reliability, and delivery priorities). Named Data Networking (NDN) has attracted the attention of the research community for effective content retrieval and dissemination in mobile environments such as VANETs. A vehicle in a VANET application is heavily reliant on information about the content, network, and application, which can be obtained from a variety of sources. The information gathered can be used as context to make better decisions. While it is difficult to obtain the necessary context information at the IP network layer, the emergence of NDN is changing the tide. The Pending Information Table (PIT) is an important player in NDN data retrieval. PIT size is the bottleneck due to the limited opportunities provided by current memory technologies. PIT overflow results in service disruptions as new Interest messages cannot be added to PIT. Adaptive, context-aware PIT entry management solutions must be introduced to NDN-based VANETs for effective content dissemination. In this context, our main contribution is a decentralised, context-aware PIT entry management (CPITEM) protocol. The simulation results show that the proposed CPITEM protocol achieves lower Interest Satisfaction Delay and effective PIT utilization based on context when compared to existing PIT entry replacement protocols

On the realization of VANET using named data networking: On improvement of VANET using NDN-based routing, caching, and security

Named data networking (NDN) presents a huge opportunity to tackle some of the unsolved issues of IP-based vehicular ad hoc networks (VANET). The core characteristics of NDN such as the name-based routing, in-network caching, and built-in data security provide better management of VANET proprieties (e.g., the high mobility, link intermittency, and dynamic topology). This study aims at providing a clear view of the state-of-the-art on the developments in place, in order to leverage the characteristics of NDN in VANET. We resort to a systematic literature review (SLR) to perform a reproducible study, gathering the proposed solutions and summarizing the main open challenges on implementing NDN-based VANET. There exist several related studies, but they are more focused on other topics such as forwarding. This work specifically restricts the focus on VANET improvements by NDN-based routing (not forwarding), caching, and security. The surveyed solution herein presented is performed between 2010 and 2021. The results show that proposals on the selected topics for NDN-based VANET are recent (mainly from 2016 to 2021). Among them, caching is the most investigated topic. Finally, the main findings and the possible roadmaps for further development are highlighted

Named Data Networking in Vehicular Ad hoc Networks: State-of-the-Art and Challenges

International audienceInformation-Centric Networking (ICN) has been proposed as one of the future Internet architectures. It is poised to address the challenges faced by today's Internet that include, but not limited to, scalability, addressing, security, and privacy. Furthermore, it also aims at meeting the requirements for new emerging Internet applications. To realize ICN, Named Data Networking (NDN) is one of the recent implementations of ICN that provides a suitable communication approach due to its clean slate design and simple communication model. There are a plethora of applications realized through ICN in different domains where data is the focal point of communication. One such domain is Intelligent Transportation System (ITS) realized through Vehicular Ad hoc NETwork (VANET) where vehicles exchange information and content with each other and with the infrastructure. To date, excellent research results have been yielded in the VANET domain aiming at safe, reliable, and infotainment-rich driving experience. However, due to the dynamic topologies, host-centric model, and ephemeral nature of vehicular communication, various challenges are faced by VANET that hinder the realization of successful vehicular networks and adversely affect the data dissemination, content delivery, and user experiences. To fill these gaps, NDN has been extensively used as underlying communication paradigm for VANET. Inspired by the extensive research results in NDN-based VANET, in this paper, we provide a detailed and systematic review of NDN-driven VANET. More precisely, we investigate the role of NDN in VANET and discuss the feasibility of NDN architecture in VANET environment. Subsequently, we cover in detail, NDN-based naming, routing and forwarding, caching, mobility, and security mechanism for VANET. Furthermore, we discuss the existing standards, solutions, and simulation tools used in NDN-based VANET. Finally, we also identify open challenges and issues faced by NDN-driven VANET and highlight future research directions that should be addressed by the research community