A compensation model of cooperative downloading for vehicular network1. Issue 5 (5th March 2013) - Liu, Jianhang; Bi, Jingping; Ge, Yuming; Cui, Xuerong; Ding, Shuyan; Li, Zhongcheng
ABSTRACT: The ever growing dependence on the Internet makes people keen on accessing the network anytime and anywhere, even while driving cars. Cooperative downloading among vehicles could help the clients obtain more data when running out of access point coverage area. One of the methods is that the access point predicts communication duration between the helper and the client according to the registered speed of vehicles to decide how many packets should be carried by the helper. However, it is possible that cooperative vehicles cannot always transmit all of the carried data to the client due to the changing speed. A key challenge that lies at the heart of this problem is how to increase the packet transmitting rate of cooperative vehicles. Towards the end, this paper proposes a novel compensation model that exploits nodes own regulatory function to mitigate the packet transmission loss. By analysing a real speed dataset collected from a test car with global positioning system running from May 2011 to July 2012 on the No. G15 highway in China, we derive a strong spatiotemporal regularity in vehicle speeds. Impending speed can be predicted, therefore, using Markov chain. On the basis of the aforementioned quantitative prediction, the compensation schemes is presented to increase received data amount by using the characteristics of encountering, overtaking and being overtaken among vehicles. Simulation results indicate the benefits of the proposed model in terms of increasing throughput and reducing delay. Copyright © 2013 John Wiley & Sons, Ltd. Abstract : This paper proposes a dynamic optimization model for cooperative downloading strategy with multi‐class request. The model defines the two‐dimensional state space and behaviour space as well as proposes the Bellman recurrence equation, which provides a basis to choose the user and its helper for maximum system revenue. Furthermore, the strategy distinguishes users' downloading priorities according to user‐defined downloading class. Under satisfying the fairness of system operation condition, the strategy provides different services according to the users' expectation, and it also helps service providers obtain the most benefit by leveraging the repetition ratio of data and user‐defined class.