An Integrated Credit-Based Incentive Protocol for Symbol-Level Network-Coded Cooperative Content Distribution among Vehicular Nodes

Abstract

In traditional symbol-level network coding (SLNC)-based cooperative content distribution approaches, they ignore nodes in the vehicular ad hoc network (VANET) having various network-coded content pieces and distinct levels of interests and selfishness for different kinds of content data, which further prevents these vehicular nodes from forwarding their content information to other nodes. With these approaches, these nodes suffer from the low ratio and the long latency to receive all content information. In this paper, based on distinct levels of node interests and selfishness on different content information, we first categorize vehicular nodes into four classes, that is, the destination, intermediate, irrelevant and overhearing ones and then designate their associated credit-based incentive approaches. Second, we modify the flow of traditional SLNC-based cooperative content distribution operations and propose the content bitmap to realize the difference of network-coded content pieces among vehicular nodes. Further, we rigidly combine the proposed credit-based incentive approach with the modified SLNC-based cooperative content distribution operations in SocialCode to encourage all classes of vehicular nodes to rise their incentives for sharing content data in the cooperative content distribution process. Finally, we perform NS-2 simulations on a street map of downtown Taipei, Taiwan to exhibit the high efficiency of SocialCode over related credit-based incentive approaches by analyzing the following performance metrics, that is, average decoding percentage, file downloading delay and credits, with respect to different file sizes and total numbers of vehicular nodes. As the best knowledge we have, SocialCode is one of the first few researches that works on the integration between the credit-based incentive protocol and the SLNC-based cooperative content distribution.