QoS-Aware Content Dissemination Based on Integrated Social and Physical Attributes Among Cellular and V2V Users

Abstract

With the recent advancement in intelligent transportation systems (ITS), the vehicular network has become the major force in content dissemination, and it is essential to improve efficiency in content delivery and quality and service (QoS). The rapid growth in vehicular data traffic and limited resources in the vehicular network have created a bottleneck in content dissemination. In addition, the demand of high data rate, low latency and user's satisfaction on content make content dissemination more challenging. To address the above problem which involves vehicle to vehicle (V2V) peer discovery and resource allocation, we propose an efficient content dissemination scheme which integrates both social and physical attributes of the users. V2V users are paired to share content based on the similarity in their preferences and link reliability, while considering the QoS of vehicle to network (V2N) users in both uplink and downlink phases. Moreover, we present an improved matching algorithm for channel allocation, which maximizes the sum rate of V2V pairs weighted by the intensity of their social relationship. Simulation results demonstrate the performance of the proposed scheme in terms of weighted sum rate and user's satisfaction on content.

Trust-Aware V2V Relay-Assisted Content Distribution in Cellular V2X Networks

“…In direct V2V mode, the signal to noise ratio (SINR) of the V2V link and cellular link on a channel can be expressed as [26].…”

Socially-Aware V2X QoS for NOMA Dual-Connectivity

“…When considering IoT applications, [24] proposed a generic IoT framework for deploying QoS-aware multi-component IoT applications over fog infrastructures. In terms of vehicle networks, [25] considers the social relationship between V2V users, link reliability, and QoS of V2N and V2V users to maximize the weighted sum rate of V2V users. In Vehicular Ad-hoc NETworks (VANETs) [26] used ant colony optimization to compute feasible routes subject to multiple QoS constraints dependent on the network traffic type.…”