# Optimal Time Allocation in VANETs Advertising: A Price-based Approach using Stackelberg Game

## TL;DR

This paper models the allocation of VANET infrastructure advertising time as a Stackelberg game, proposing algorithms to optimize pricing and time distribution for maximizing profit and utility in urban settings.

## Contribution

It introduces a novel game-theoretic framework for optimal time and price allocation among advertising companies in VANETs, including both optimal and efficient sub-optimal algorithms.

## Key findings

- Sub-optimal algorithm performs close to the optimal with lower complexity.
- The proposed model effectively balances profit for the manager and utility for advertising companies.
- Simulation results validate the effectiveness of the algorithms in realistic scenarios.

## Abstract

Vehicular ad-hoc networks (VANETs) have recently attracted a lot of attention due to their immense potentials and applications. Wide range of coverage and accessibility to end users make VANETs a good target for commercial companies. In this paper, we consider a scenario in which advertising companies aim to disseminate their advertisements in different areas of a city by utilizing VANETs infrastructure. These companies compete for renting the VANETs infrastructure to spread their advertisements. We partition the city map into different blocks, and consider a manager for all the blocks who is in charge of splitting the time between interested advertising companies. Each advertising company (AdC) is charged proportional to the allocated time. In order to find the best time splitting between AdCs, we propose a Stackelberg game scheme in which the block manager assigns the companies to the blocks and imposes the renting prices to different companies in order to maximize its own profit. Based on this, AdCs request the amount of time they desire to rent the infrastructure in order to maximize their utilities. To obtain the Stackelberg equilibrium of the game, a mixed integer nonlinear optimization problem is solved using the proposed optimal and sub-optimal algorithms. The simulation results demonstrate that the sub-optimal algorithm approaches the optimal one in performance with lower complexity.