Internet of Things Multi-area Monitoring Application Research by Integrating Data Warning and Blind Area Data Processing

Abstract

In the context of the rapid development of Internet of Things technology, improving the efficiency and accuracy of multi-area monitoring systems has become an important issue. This study aims to address the shortcomings of traditional monitoring systems in blind spot data processing and reduce resource consumption. By constructing a model based on RFID and finite state machine, and integrating probabilistic methods, a comprehensive data warning and blind spot data processing multi-area monitoring model for the Internet of Things has been formed. The results showed that the labels were randomly distributed within a range of 0-20 meters and moved at a speed of 2 meters per second. Under the change in the proportion of the main monitoring area from 0 to 1, warning time windows of 3 seconds, 5 seconds, and 7 seconds were applied. The experimental results showed that when the main monitoring area was 0%, the data capture efficiency of the 7-second window was the highest. Within a 180 second monitoring cycle, the number of alarms exceeding 10 seconds in the inner blind spot was 12, and the number of alarms exceeding 10 seconds in the outer blind spot was 6. The total number of alarms reached 18. Among the 35 predicted alerts, the 7-second window had the lowest warning error rate. The results demonstrate the effectiveness of this method in reducing early warning error rates in multi region monitoring, and its performance is superior to traditional fixed time window settings. This study provides a new and efficient solution in the field of multi-area monitoring in the Internet of Things.