ROVERS: Incentive-Based Recruitment of Connected Vehicles for Urban Big Data Collection

Abstract

The growth in mobile devices results in constant generation and consumption of a large amount of data by mobile users on the go which is unbearable by the current mobile networks in terms of cost and bandwidth. At the same time, the technological advancements in modern vehicles allow us to harness their computing, caching, and communication capabilities to support various smart city applications. It is now possible to recruit a set of connected vehicles to collect, store, and share heterogeneous information regarding urban streets and facilitate citizens with different location-aware services. However, for a user to find and retrieve relevant content among the fleet of hundreds of vehicles on urban roads is challenging due to high mobility and intermittent connectivity. To address this, in this paper we propose ROVERS for a service provider to recruit the best set of vehicles to facilitate users on urban streets with different location-based applications. To identify the best vehicles, we first use a distributed ranking scheme CarRank , where the vehicle autonomously classifies itself as important with respect to urban users’ interest. Then, we present a centralized recruitment scheme, exploiting game-theory for the service provider to fairly and optimally select the best vehicles under desired coverage, redundancy, and quality requirements. Comparative analysis after in-depth simulations using realistic mobility traces of 2986 vehicles shows that the set of vehicles selected using ROVERS yield better results compared to other selection approaches.