Research of information processing for massive sensors in extended IOV applications

By RFID (Radio Frequency Identification Technology) technology and internet, IOV (Internet of Vehicles) can extract and use kinds of static or dynamic information from all the vehicles. In addition, it can manage vehicles as well as supporting comprehensive services according to different application demands. Nowadays, IOV needs more composite information including not only basic RFID info but also location info or OBD(On-Board Diagnostics) info and the types of IOV are also be extended to composite management systems for example the system which are in charge of off-gas management and so on. Therefore, to realize the goal of precise location, adaptive reasoning and reliable fusion in these more unstructured, heterogeneous, wide-area and massive WSN based systems, we should sense the changes of the system context efficiently and accurately in real time. Therefore, we propose a uncertain context fusion framework that supports QoC(Quality of Contexts) management in various layers. By this framework, we can use threshold management, quality factor management and inconsistent context management to protect and provide QoS-enriched context fusion efficiently for context-aware applications and services.