Content Retrieval Based on Prediction and Network Coding in Vehicular Named Data Networking

Abstract

Named Data Networking (NDN) has the advantages of content-based, location-independent and in-network caching. These characteristics are naturally suitable for highly dynamic Vehicular Ad hoc Networks (VANETs), so VNDN has become a promising network architecture. However, achieving effective and reliable multi-hop content retrieval is still a major challenge in VNDN where network parameters change frequently and the channel reliability is poor. In this paper, we propose a protocol based on prediction and network coding for content retrieval in vehicular named data networking (PreNCCR). In order to adapt to the dynamic and random packet loss characteristics of VANETs and restrict flooding, a prediction-based opportunistic routing in which the priority of candidate forwarder is determined through prediction mechanism is proposed. On this basis, a network coding-based packet forwarding strategy is proposed in which the available network capacity are effectively utilized. The evaluation results show that under moderate network configuration parameters, compared with VNDN-geo, CCVN, and SelNC protocols, PreNCCR can not only achieve 33.8% ~ 99.8% higher request satisfaction ratio, 46.6% ~ 65.1% lower delay, but also can reduce the consumption of Interest and Data by 32.6% ~ 80.7%, 52.3% ~ 60.1%.