Description

Radio-frequency identification (RFID) is the use of a wireless non-contact system that uses radio-frequency electromagnetic fields to transfer data from a tag attached to an object, for the purposes of automatic identification and tracking. RFID offers a number of advantages over previous ID systems. For example, unlike barcodes, RFID tags don’t require “line of sight” to the tag reader, while barcode scanners need to directly “see” to read the barcode. Standard barcodes only identify the manufacturer and product, not unique items, and RFID allows for individual items to have a unique identifier and can identify many items at once. We presented a new novel approach for inference and compression over the RFID Stream. It address the problem occur in previous work such as incomplete data, handle high volumes of data, and lack in identifying location of objects. This low-level substrate offers accurate interpretation of incomplete and noisy raw data. In particular, it infers locations of unobserved objects and inter-object relationships using probabilistic algorithms. To handle high data volume, it performs online interpretation, enabling online compression by identifying and discarding redundant data.