Nearly Optimal Probabilistic Coverage for Roadside Advertisement Dissemination in Urban VANETs

TL;DR

This paper addresses the problem of selecting a minimal set of roadside access points in urban VANETs to probabilistically disseminate advertisements efficiently, ensuring coverage thresholds while minimizing costs.

Contribution

It formulates the RAP selection as an NP-hard problem and proposes a greedy approximation algorithm with proven ratio, validated by extensive real-world simulations.

Findings

The proposed algorithm achieves near-optimal coverage with fewer RAPs.

Simulation results demonstrate the algorithm's effectiveness and efficiency.

The approach reduces dissemination costs while maintaining desired success probabilities.

Abstract

Advertisement disseminations based on Roadside Access Points (RAPs) in vehicular ad-hoc networks (VANETs) attract lots of attentions and have a promising prospect. In this paper, we focus on a roadside advertisement dissemination, including three basic elements: RAP Service Provider (RSP), mobile vehicles and shops. The RSP has deployed many RAPs at different locations in a city. A shop wants to rent some RAPs, which can disseminate advertisements to vehicles with some probabilites. Then, it tries to select the minimal number of RAPs to finish the advertisement dissemination, in order to save the expenses. Meanwhile, the selected RAPs need to ensure that each vehicle's probability of receiving advertisement successfully is not less than a threshold. We prove that this RAP selection problem is NP-hard. In order to solve this problem, we propose a greedy approximation algorithm, and give the corresponding approximation ratio. Further, we conduct extensive simulations on real world data sets to prove the good performance of this algorithm.