An Efficient Information Sampling Method for Multi-Category RFID Systems

In Radio Frequency Identification (RFID) applications, multiple tags may be deployed on the same object or area for security or precision purposes. These tags carry similar information and so can be grouped into a category. To collect such information, most existing works have to query all tags in each category, which is time-consuming. However, sampling a subset of tags from each category is sufficient. In this letter, we propose a new solution called arithmetic coding based sampling (ACS) protocol. Specifically, we construct a sparse vector to sample only a subset of tags from each category, which can not only avoid repetitive information collection but also reduce interference from unsampled tags. Moreover, we compress the sparse vector through arithmetic coding, which significantly reduces its transmission time. Both theoretical analysis and simulation results demonstrate that ACS outperforms existing solutions in time efficiency.