Disorder Analytic Model-Based CMT Algorithms in Vehicular Sensor Networks

Recently, vehicular sensor networks (VSNs) have emerged as a new intelligent transport networking paradigm in the Internet of Things. By sensing, collecting, and delivering traffic-related information, VSNs can significantly improve both driving experience and traffic flow control, especially in constrained urban environments. Latest technological advances enable vehicular devices to be equipped with multiple wireless interfaces, which can support cooperative communications for concurrent multipath transfer (CMT) in VSNs. However, path heterogeneity and vehicle mobilitycauseCMT not to achieve the same high transport efficiency recorded in wired nonmobile network environments. This paper proposes a novel vehicular network-based CMT solution (VN-CMT) to address the above issues and improve data delivery efficiency. VN-CMT is based on a CMT disorder analytic model which can effectively and accurately evaluate the degree of out-of-order data. Based on this proposed model, a series of mechanisms are introduced as follows: (1) a packet disorder-reducing retransmission policy to reduce retransmission delay; (2) a path group selection algorithm to find the best path set for data multipath concurrent transfer; and (3) a data scheduling mechanism to distribute data according to each path's capacity. Simulation results show how VN-CMT improves data delivery efficiency in comparison with an existing state-of-the-art solution.