An improved RFID anti-collision algorithm and its application in food tracking

Food safety issues are frequent in today’s society, and how to accurately and accurately track food is an urgent problem to be solved. In the information age of the Internet of Things (IOT), the “connected objects” feature allows food to provide detailed information from the production to the sales process, tracking the processing flow of each link, and when the food quality and safety issues arise, it can be accurate. Quickly find the source of food and related product flows, effectively prevent problem foods from entering the market, and let consumers eat safe food. This thesis mainly studies the multi-tag recognition collision avoidance in the Internet of Things technology, and improves the multi-fork tree anti-collision algorithm. By introducing the slot idea in the ALOHA algorithm, a dual-slot dynamic search algorithm (GMT) is proposed. Simulation experiments show that the algorithm effectively reduces the time spent on RFID tags recognition, reduces the number of collisions of tags, and reduces the energy consumption of the system. It is an effective anti-collision algorithm.