Exploiting Network Coding for Data Availability in Vehicular Networks: Issues and Opportunities

Retrieving data from mobile source vehicles is a crucial routine operation for a wide spectrum of applications of vehicular networks, such as road surface monitoring and sharing. The key to improving the data retrieval performance is to increase the data availability such that a retrieving node can easily acquire its desired data from the vehicles that it contacts. Network coding has widely been exploited as an effective technique for defusing information over a network. In this paper we explore the exploitation of network coding for improving data availability in vehicular networks. By random linear codes, simple replication is avoided, and instead a node forwards a coded block which is a random combination of all data received by the node. We implement a network coding based approach for improving data availability in vehicular networks. To understand the performance of this approach, we have conducted empirical study with extensive simulations based on real vehicular GPS traces from around 2,000 taxis in Shanghai, China. We make the important observation that in spite of significant improvement up to 300% in data availability, there is a serious issue with linear correlation among the received codes, which degrades the performance of data retrieval success rate. By analyzing the real vehicular traces, we reveal that there is strong community structure with a real vehicular network and then explain that such community structure may contribute to the issue of linear dependence. Then, we point out the opportunities for improving the network coding based approach by developing community aware codes distribution techniques.