An advertisement dissemination strategy with maximal influence for Internet-of-Vehicles

Yanfei Lu, Zihan Zhao, Bowu Zhang and Qinghe Gao

International Journal of Distributed Sensor Networks, 2019, vol. 15, issue 11, 1550147719888106

Abstract: The recent advances in computation and communication technologies have led to the emergence of Internet-of-Vehicles where vehicles are connected to each other through sensors so that they can exchange information to improve driving safety, efficiency, and comfort. Internet-of-Vehicle has attracted attention from both academia and industry, as it promises huge commercial and research value. This article studies an advertisement dissemination problem in Internet-of-Vehicle with an aim to maximize the profit (number of vehicles to receive advertisements) given a limited budget. In this problem, advertisements will be first sent to a selected set of seed vehicles, then forwarded to neighboring vehicles. To find the influential set of vehicles, we examine the probability of interaction between vehicles by exploiting their mobility. In particular, we present the computation of node marginal gain in four cases by examining vehicle connectivity and topology. Simulation results demonstrate that the proposed algorithm outperforms existing methods for influence maximization by running time and deliver ratio under different traffic scenarios.

Keywords: Internet-of-Vehicles; advertisements dissemination; influence maximization; greedy algorithm; node connectivity (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:15:y:2019:i:11:p:1550147719888106

DOI: 10.1177/1550147719888106

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