TagMark: Reliable stimations of RFID Tags for Business Processes

摘要

Radio Frequency Identification (RFID) promises optimization of commodity flows in all industry segments. But due to physical constraints, RFID technology cannot detect all RFID tags from an assembly of items. This poses problems when integrating RFID data with enterprise-backend systems for tasks like inventory management or shelf replenishment. In this paper we propose the TagMark method to accomplish this integration. TagMark targets at a retailer scenario, where it estimates the number of tagged items from samples like the sales history or the tags read by smart shelves. The problem is challenging because most existing estimation methods depend on assumptions that do not hold in typical RFID applications, e.g., static item sets, simple random samples, or the availability of samples with user-defined sizes. TagMark adapts mark-recapture-methods in order to provide guarantees for the accuracy of the estimation and bounds for the sample sizes. It can be implemented as a database extension, allowing seamless integration into existing enterprise backend systems. A study with RFID-equipped goods acknowledges that our approach is effective in realistic scenarios, and database experiments with up to 1, 000,000 items confirm that it can be efficiently implemented. Finally, we explore a broad range of extreme conditions that might stress TagMark, including a thief who knows the location of unread items.