The goal of this thesis is to develop statistical and learning algorithms for the analysis, measurement, and modeling of wireless networking( Radio frequency identification systems).Radio frequency identification (RFID) systems are widely used in logistic, supply chain industry and inventory management. RFID is already in use in multiple industries and for various purposes. The device in your car that lets you zoom by in the fast lane at a tollbooth, while deducting a dollar amount from your account, is an example of RFID technology in everyday use. Mostly, existing RFID systems are primarily used to identify the RFID tags present in a tag population(e.g., tracking a specific tag from a tag population) while identifying some specific tags is a critical operation, it is usually very time consuming and is not desired or necessary in some situations. For instance, if the objective is to determine whether any of the tags are missing(e.g., to detect some items according to a consignment), the first thing to do is to identify all tags’ ID and then compare with the original record to determine if there is any tags are missing. Definitely, the whole process will be very slow if we have a very large tag population. In this thesis, I present novel statistical algorithms to enable fast and new applications in RFID systems. For example, detecting the missing tags in a large tag population with high accuracy while using the existing infrastructure of RFID systems which is already deployed in industry. More pacifically, I present my work on designing statistical algorithms for estimation the number of missing tags in a population of RFID tags, for detecting and identifying the missing tags from a population of RFID tags.The key distinction of my work compared to prior art is that my methods are compliant with EPCGlobal Class 1 Generation 2 (C1G2) RFID standard. It is critical for RFID methods to be compliant with the C1G2 standard since the commercially available of-the- shelf RFID equipment follows the C1G2 standard. A method which does not comply with the C1G2 standard cannot be deployed on the existing installations of RFID systems because it requires custom hardware, which will cost a lot. In an RFID-enabled warehouse, there may be thousands of tagged items that belong to different categories, e.g., different places of origin or different brands [72]. Each tag attached to an item has a unique ID that consists of two fields: a category ID that specifies the category of the attached object, and a member ID that identifies this object within its category. As a manager of the warehouse, one may desire to timely monitor the product stock of each category. If the stock of a category is too high, it may indicate that this product category is not popular, and the seller needs to adjust the marketing strategy (e.g., lowering prices to increase sales). On the contrary, if the stock of a category is too low, the seller should perform stock replenishment as soon as possible. Manual checking is laborious and of low time-efficiency. You cannot imagine how difficult it is for a manager to manually count the number of items in each category that may be stacked together or placed on high shelves. Hence, it is desirable to exploit the RFID technique to quickly obtain the number of tagged items in each category. A multi-category RFID estimation protocol should satisfy three additional requirements. First, it should be standard compliant; otherwise, it will be difficult to be deployed. Second, it should preserve the privacy of tags by not reading their member IDs. Third, it should work with both a single-reader and multiple-reader environments. As the communication range between a tag and a reader is limited, a large population of tags is often covered by multiple readers whose regions often overlap.
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