Elsevier

Vehicular Communications

Volume 1, Issue 4, October 2014, Pages 208-213
Vehicular Communications

Invited paper
VENDNET: VEhicular Named Data NETwork

https://doi.org/10.1016/j.vehcom.2014.09.002Get rights and content

Abstract

Named Data Networking (NDN) is proposed for effective content distribution when a large number of end-users demand for popular content at the same time. Thus, NDN can be implemented in Vehicular ad-hoc NETwork (VANET) to meet the particular requirements. Therefore, all of vehicles refer to the real time traffic status by a faster and more efficient network. In this paper, we propose VEhicular Named Data NETwork (VENDNET) according to three different vehicle communication mechanisms, which are vehicle-to-infrastructure (V2I), a hybrid of vehicle to road side unit (V2R) and vehicle to vehicle (V2V). Furthermore, this paper illustrates the experimental results conducted by OPNET Modeler, and shows that the solution with NDN enhances the Quality of Service (QoS) of VANET significantly.

Introduction

Vehicular ad-hoc NETwork (VANET) is a technique that uses moving vehicles as wireless nodes in a mobile network, in which each wireless node takes a role as an end-user and wireless router to support wide range communications. Motivated by the increasing demand for efficient and reliable information dissemination and retrieval, the Named Data Networking (NDN) presents a simple and effective communication model [1]. In NDN, an interest packet (IntPk) and a data packet (DataPk) are two packet types mainly used to identify a content, which is typically hierarchical and human readable. Each NDN node maintains three data structures: Forwarding Information Base (FIB), Pending Interest Table (PIT) and Content Store (CS). Once an NDN node receives a IntPk, it will lookup for a content in the CS. If the appropriate content is found, the DataPk will be sent in response to the request, otherwise the IntPk will be checked in the PIT. The PIT takes a role to keep track on unsatisfied IntPks. After the PIT creates a new entry for unsatisfied IntPk, which is forwarded upstream towards a potential content source based on the FIB's information. A returned DataPk will be sent downstream and stored in the CS buffer.

Related to the exponent growth of traffic, a skewness characteristic of the popularity of content was found, that is to say, a few popular contents are often queried by the huge number of end-users. The high skewness of popular content makes the Least Recently Used (LRU) and Least Frequently Used (LFU) replacement policies suffer from low efficiency for NDN. In order to fully exhibit the better performance of the NDN compared to traditional network architectures, we suggest to use the popularity prediction mechanism. That is to say, by counting the times of the prefix's appearance in the content, the NDN node maintains a prefix tree (PT) for all contents, and quickly finds popular contents in PT [2]. Popular contents are classified and every content is marked with a suitable lifetime. A more popularity level content will be given a longer lifetime. The above prediction-based scheme is dubbed Prefix-Tree LRU (PT-LRU). Hence, PT-LRU is a simple approach for NDN nodes to achieve higher hitting rate than LRU and LFU.

Furthermore, an enhanced version of PT-LRU, dubbed Prefix-Tree Sharing (PT-Sharing), is taken into account. NDN nodes running PT-LRU are periodic exchange of the most popular prefix information with their neighboring NDN nodes. Therefore, in the PT-Sharing mechanism, NDN nodes can learn and predict about the popularity trend for a near future, posed the PT-Sharing mechanism finds the most popular content more quickly than PT-LRU mechanism. With a simple cooperation between NDN nodes, higher hitting rate and faster convergence speed to final state are achieved. The four schemes LRU, LFU, PT-LRU and PT-Sharing are successfully constructed in the NDN node. The simulation results indicate that PT-LRU and PT-Sharing outperform LRU and LFU with highly effective caching.

In this paper, we propose our solution, Vehicular Named Data Networking (VENDNET), by inheriting the basic principle of the NDN. However, extending the NDN model to the VANET is not straightforward due to a lot of challenges in the vehicle environment such as the limited and intermittent connectivity, and node mobility. The contribution of the paper as follows. We first introduce some meeting challenges in different types of vehicle communication mechanism. Then we discuss and evaluate the benefits brought by the prediction-based schemes for the NDN. Motivation from the NDN model simulation, the VENDNET performance is taken into account by clearly comparing the VANET under two scenarios: with typical clients–server connection and with NDN connection.

The remainder of this paper is organized as follows. Section 2 provides the VANET background, and reactive routing applied for the NDN. Section 3 illustrates simulation and evaluation results for basic NDN model. Then, Section 4 portrays envisioned VENDNET network architecture of the simulation setup and discusses simulation results. Finally, Section 5 concludes this paper.

Section snippets

VANET: an overview

In vehicle-to-infrastructure (V2I) network, assistance transmission networks are required, such as 2.5G, 3G, 4G, to centrally manage all the vehicles communication [3]. With handover technique between radio cells, vehicles always keep pace with a server supplying VANET applications. For instance, a serving distance of mobile base stations operated at 900 MHz carrier frequency is typically from 2 km in microcell up to 35 km in macrocell. Therefore, vehicles are mobility and the handover between

Basic NDN network architecture

To evaluate the performance of NDN mechanism, we implemented NDN and conducted simulations using the OPNET Modeler 16.0 [9], [10]. There are many simulators for VANET but none of them can provide a complete solution for simulating VANETs [11], [12]. Among a number of simulation tools such as VanetMobiSim, SUMO, NS2, QualNet, etc., we would like to use OPNET because it supports for a realistic mobile network environment (e.g. 2.5G/3G/4G). In the simulation, NDN is overlayed over the IP layer.

VENDNET simulation and results

Motivation from the NDN model simulation, we enhance VENDNET simulation with two scenarios: V2I network and V2R&V2V network. V2I network simulation is illustrated in Fig. 5(a). There are three cells in LTE network, and each cell includes an eNodeB connected with NDN node. The eNodeB provides a radio communication within 2000 meters range while NDN node is added component to implement NDN protocol. Two RSUs that are implemented in each LTE cell, generate content with 64 Kbps rate and transmit

Conclusion

In this paper, we have introduced two variants of a new cache decision and replacement policy for NDN that take into account of content popularity. Furthermore, we have implemented the VENDNET model in two networks scenario: V2I and V2R&V2V. The performance of the proposed policy and the VENDNET model have been evaluated using OPNET simulations. The obtained results show that NDN mechanism can improve the performance of the network significantly. In the future work, the VANDNET model should be

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