Stabilized Clustering Enabled V2V Communication in an NDN-SDVN Environment for Content Retrieval

Abstract

Content retrieval is becoming an emerging application on an integrated network structure referred to as Software Defined Network and Vehicular Ad hoc Network (SDVN). The SDVN is expected to flexibly provide efficient packet routing without deploying road side units. In such the infrastructure-less environment, however, it is challenging to retrieve contents from dynamic networks clustered with vehicles on roadways. As a solution to the problem, we propose a new network design by conjoining Named Data Network (NDN) with SDVN (NDN-SDVN). The new NDN-SDVN is designed as to improve stableness of the cluster formation and hence enables to efficiently perform the content retrieval over Vehicle-to-Vehicle (V2V) communications. Note that one of the most important problems in the V2V is how to select a cluster head. The proposed NDN-SDN adaptively determines the cluster head by using the SDN controller under the maximum Quality of Service (QoS) criterion, while reserving an assistance head based on a Moth Flame Optimization (MFO) algorithm to immediately recompose a new cluster with the back-off time. Moreover, the content retrieval in the NDN-SDVN can be performed over both intra-and inter-cluster communications. To this end, a Kalman filter with Neural Network (KF-NN) is applied for selecting a certain number of preferable vehicles in 1-hop in order to mitigate the broadcast storm problem that often occurs in an NDN. Content retrieval is initiated by sending interest packets including two additional fields of the timestamp and the target MAC address. The head vehicle of the SDN controller maintains ontology regarding the content information cashed by other vehicles, which significantly improves the packet delivery ratio. This NDN-SDVN design is developed on the OMNeT++ simulator and their advantageous results are evaluated in terms of the head lifetime, the cluster lifetime, the satisfactory rate, the latency and the packet delivery ratio.

The Emerging of Named Data Networking: Architecture, Application, and Technology

“…Work in [57] uses identifiers on Blockchain in the NDN environment to safely store the content. Alowish et al came up with a survey on Blockchain over NDN that covers the benefit of Blockchain in decentralized payment, e-health care, and cloud computing [58].…”

Machine Learning-Based Efficient Clustering and Improve Quality of Service in Manet

“…QoS-guided Dynamic Scheduling approach is to recognize the best placements for all types of tasks; also, it schedules the clusters. It is a lightweight approach and provides a QoS assurance [Alowish et al, 2020]. QoS-oriented scheduling and auto-scaling approach is used to schedule jobs in the cluster.…”

Analysis of Vehicle-to-Vehicle Basic Safety Message Communication Using Connectivity Characteristic Matrix

“…These exchanges deliver information and vehicle sensors data that usually contain information about vehicle mechanics, road condition, driver status, and behavior, among others. The BSM message contributes greatly to the safety and mobility of vehicles by reducing accidents and congestion through exchanging critical information regarding position, speed, and other data such as intersections, blind spots, and pedestrian crossing, among many [20]- [22].…”