An Abnormal Information Collection Protocol for Large-Scale RFID Systems

Many technologies have become mature for RFID applications, but few studies have been carried out to collect large-scale RFID tags information. This paper focuses on the problem of abnormal information collection, which aims at effectively identifying the abnormal products through sensor information detection in large storage, so that it can be processed in a timely manner. In order to reduce the cost of transmission and improve the efficiency of recognition, an abnormal information collection protocol (AIC) is presented, which first randomly collects part of tags information, and estimates the number of tags that are detected as abnormal, then collects their ID number and sensor information. Through the experiment simulation, AIC can efficiently collects the abnormal tags information, and is better than the Gen2-based protocol, SIC and MIC protocols based on the bloom filter.

I. Introduction

As one of the key technologies of the Internet of things, the radio frequency identification technology (RFID) has been favored by major manufacturers after the integration of sensors [1]–[3]. Because tags can transmit sensor data back to the reader and the cost of deployment is low, this feature can meet the merchant’s demand for real-time monitoring of the goods [4]–[6]. Deploying RFID tags integrating a sensor can return the status information of the item in real time. If there are abnormal conditions, such as too high or too low temperature or humidity, the manufacturer can immediately make corresponding adjustments to extend the storage time of the product [5]. There are two characteristics of the above applications: 1. The storage time of the goods needs to be as long as possible. At this time, we need to eliminate all factors affecting the quality of product, for example, when some have been deteriorated, they need to be processed immediately. The environment must be the most suitable; 2. There are many products, and reading each information requires a lot of costs.