Research Of Data Fusion Algorithm And Anti-collision Algorithm Based On The Internet Of Things

The internet of things uses the sensor network, RFID and other devices to connect all the things with internet so as to exchange information and realize identification, position, monitoring and management. The internet of things has attracted wide attention around the world in theory and application research, which become an important emerging industry to promote the economic growth of the world. The applications of the internet of things include intelligent transportation, health services, industry monitoring, urban management and other area. The internet of things can realize the interconnection of any time, any place and any things, which enhances level of the whole society information, and has broad application prospects.The main topics of this dissertation are the research for the theory and design of data fusion algorithms in sensor networks and multiple tag anti-collision algorithms in RFID systems, which are the key technology of perception level in the internet of things. The principal tasks and improvements are as follows:1. Describe layered architecture and key technology of the internet of things, and mainly analyze the perception and recognition technology. Then describe data fusion model, routing protocols in sensor networks, and working principle, anti-collision algorithm in RFID systems.2. Improved the clustering Leach protocol based on distributed data fusion, which can reduce energy consumption and prolong the network life cycle. Then describe a data fusion algorithm based on gauss membership function. Combining of the data fusion technology and routing protocol to process data collected by sensors, which can enhance the system efficiency and accuracy, reduce number of data transmission and network congestion.3. A new efficient binary search algorithm with the tag estimation function is brought up for RFID systems, which can be use to identify the unknown number tags around the reader. Theory analysis and simulation results show that AEBS enhance the system identification efficiency and stability. On the case that tag number is more than 0 and less than 300, the system efficiency of AEBS maintains between 79.0%~87.0%, the average number of iterations to identify one tag maintain between 1.15~1.26, and the system identification time is reduced.