Retrieving data from mobile source vehicles is a crucial routine operation for a wide spectrum of vehicular network applications, in- cluding road surface monitoring and sharing. Network coding has been widely exploited and is an effective technique for diffusing in- formation over a network. The use of network coding to improve data availability in vehicular networks is explored in this paper. With random linear network codes, simple replication is avoided, and instead, a node forwards a coded block that is a random combination of all data received by the node. We use a network-coding-based approach to improve data availability in vehicular networks. To deter- mine the feasibility of this approach, we conducted an empirical study with extensive simulations based on two real vehicular GPS traces, both of which contain records from thousands of vehicles over more than a year. We observed that, despite significant improve- ment in data availability, there is a serious issue with linear correlation between the received codes. This reduces the data-retrieval success rate. By analyzing the real vehicular traces, we discovered that there is a strong community structure within a real vehicular network. We verify that such a structure contributes to the issue of linear dependence. Then, we point out opportunities to improve the network-coding-based approach by developing community-aware code-distribution techniques.
智慧城市是物联网、云计算、移动网络、大数据等为代表的信息技术与城市化发展相结合的产物.如何有效地实现对智慧城市中海量、异构、多源数据的数据共享和融合是智慧城市必须要解决的核心问题.首先分析了传统数据融合技术的特点,然后阐述了当前可以用来解决智慧城市大数据共享和融合问题的技术.在此基础上提出了一种新的智慧城市数据共享和融合框架——智慧城市数据互联框架(smart city linked data framework),并详细阐述了其总体架构以及架构中每层的作用、关键技术和需要解决的问题,最后详细阐述了数据语义标注标签和数据互联层相关问题.
