A Context-Aware Budget-Constrained Targeted Advertising System for Vehicular Networks

Abstract

The emergence of self-driving automobiles has drawn great attention to VANETs, where vehicles can interact with each other through wireless communications. A variety of interesting applications thus have been developed to enable vehicles to monitor traffic/congestion, and share information/files real-time. One of most promising services over Vehicular ad hoc networks (VANETs) is the advertisements dissemination that provides users (drivers and passengers) with commercial ads, such as tourism/shopping/restaurant promotions. Owing to the mobility of vehicles, advertisements can spread to anywhere as the vehicles move through vehicle-to-vehicle communications. In this paper, we address the problem of advertisements (ads) dissemination in VANETs with a budget constraint, where ads are first sent from road side units to a selected set of vehicles (seed vehicles), then forwarded to nearby vehicles as seed vehicles moving. We aim to maximize the number of vehicles that receive ads during the dissemination process and prove that this optimization problem is NP-hard. We then propose a heuristic algorithm based on genetic methods to solve the problem. In particular, we consider the user preferences when advertising making sure that a perfect message reaches the perfect audience at the perfect time. Simulation results demonstrate that the proposed algorithm outperforms existing methods by delivering ads to more vehicles under different traffic scenarios.