|Xiulong Liu||Hong Kong Polytechnical University, Hong Kong|
|Jiannong Cao||Hong Kong Polytechnical University, Hong Kong|
|Keqiu Li||Tianjin University, P.R. China|
|Jia Liu||Nanjing University, P.R. China|
|Xin Xie||Dalian University of Technology, P.R. China|
This paper takes the first step in studying the problem of range query for sensor-augmented RFID systems, which is to classify the target tags according to the range of tag information. The related schemes that seem to address this problem suffer from either low time-efficiency or the information corruption issue. To overcome their limitations, we first propose a basic classification protocol called Range Query (RQ), in which each tag pseudo-randomly chooses a slot from the time frame and uses the ON-OFF Keying modulation to reply its range identifier. Then, RQ employs a collaborative decoding method to extract the tag information range from even collision slots. The numerical results reveal that the number of queried ranges significantly affects the performance of RQ. To optimize the number of queried ranges, we further propose the Partition&Mergence (PM) approach that consists of two steps, i.e., top-down partitioning and bottom-up merging. Sufficient theoretical analyses are proposed to optimize the involved parameters, thereby minimizing the time cost of RQ+PM. The prominent advantages of RQ+PM over previous schemes are twofold: (i) it is able to make use of the collision slots, which are treated as useless in the previous schemes; (ii) it is immune to the interference from unexpected tags. We use the USRP and WISP tags to conduct a set of experiments, which demonstrate the feasibility of RQ+PM. Moreover, extensive simulation results reveal that RQ+PM can ensure 100% query accuracy, meanwhile reducing the time cost as much as 40% comparing with the existing schemes.