When Tags ‘Read’ Each Other: Enabling Low-Cost and Convenient Tag Mutual Identification

Passive Radio Frequency Identification (RFID) technology, widely considered to be a cheap, energy-efficient, and scalable solution for the Internet of Things (IoT), has been deployed for ubiquitous applications, such as retailing, warehousing, transportation, and manufacturing. The basic functions of RFID that have been extensively studied include (1) collecting the identity information of tagged items or users in a target area [10]; (2) counting or estimating the population of items/users [18, 31, 50]; (3) advanced sensing tasks that explore the physical signal features of RFID, such as localization and tracking [12, 13, 24, 28, 29, 33, 34, 37, 46, 48, 49]; and human activity sensing [8, 11, 36, 47].

However, current passive RFID applications are mainly restricted to industrial and logistics areas due to a fundamental limitation: an individual user finds it difficult to interact with tagged items or other tagged usersif the user carries no reader. The interactions with tagged items may include (1) collecting certain information from the digital profile behind a tag, such as the item name, price (for merchandise), packing location and time (for packages); (2) requesting the backend system to connect the user profile with the item profile and conduct certain processing, such as charging an item to the user account. Hence, passive RFID is seldom used in consumer applications.

Recent efforts have been made to improve the consumer experience of interacting with passive tags, such as a small reader [2], smartphone-based RFID scanner [1], and reading tags through the smartphone WiFi interface [3]. Smartphones may also have NFC interfaces. However, these methods still require non-trivial hardware carried by the users—a smartphone at a minimum. As a result, children, some seniors, people with certain disabilities, and other people who are not familiar with operating smartphones cannot utilize these methods. Moreover, having a reader embedded in a mobile device such as a smartphone or reading tags through a WiFi interface all require complex hardware modifications and increase the cost of deploying the system.

This work provides an extremely low-cost and convenient solution for user-tag interaction by answering this question: Can a user initiate the interaction with tagged items with nothing but a single passive tag? The proposed solution is called TagMii (Tag Mutual Identification Interface). TagMii allows two tags to ‘read’ each other: If two tags are placed in close physical proximity, the reader can identify these interacting tags among many other tags in the environment. The two tags can be the identifiers of a user and an item or two users. Compared with current RFID applications [18, 31, 50], which require the user to carry a cumbersome reader to interact with each tag, TagMii needs to deploy only one commercial RFID reader in the environment, which significantly lowers the cost and eases maintenance. One reader with multiple cabled antennas in a room can serve many users (visitors, customers, etc.) and the cost of adding more users is minimal: only passive tags. We present a number of IoT applications that can utilize TagMii to perform simple yet important tasks, which was difficult to achieve in existing RFID systems:

Application 1. A museum displays a collection of artwork. A tag is placed on the wall next to each piece of art. Users wear wireless headphones. If users want to listen to commentary on the art, they put their user tag close to the tag of the artwork. The backend system then sends the recorded commentary to their headphones in the preferred language according to the user profile.

Application 2. In a cashier-free retail store, a tag is attached to each item. When users put their user tag close to an item tag, they can hear the item price, decryption, and reviews from their headphones. They may repeat this operation to request the backend system to put the item in their virtual shopping cart. Warehouse workers put the items that they interacted with into a cart at checkout for them to pick up and charges are made to their account.

Application 3. In a conference, two attendees chatting during a coffee break may put their user tags together. An app running in the backend system will automatically exchange their digital profile (e-business card) and record the time and location. After the conference, the collected digital profiles will be emailed to the user and the user can easily review the profiles of the attendees with whom the user interacted.

Some existing technologies may also be able to accomplish the tasks in these applications. For example, customers can use their smartphone to scan the barcode [39] or QR code [25] attached to the items to retrieve item information and/or request payment information. Some mobile apps support e-business card exchange. Compared with these solutions, the key advantage of TagMii is that it is smartphone-free and, hence, very convenient for children, some seniors, people with certain disabilities, and others who do not operate smartphones. Other advantages of TagMii include: (1) Easy to carry. The user tag can be attached to a conference badge (or museum badge or shopping badge with a similar design). (2) Fast interaction. Using smartphones for code scanning or profile exchange usually takes half a minute. TagMii may only take 10 seconds. This speed advantage is especially important in busy situations such as conference breaks. (3) TagMii does not require light conditions, compared with code scanning, suitable for museums and warehouses. (4) System managers can easily obtain useful information monitored by the backend, such as the popular goods, shopping habits of users, and preference of art by users of different ages and regions. One might notice that TagMii still requires a reader placed in each room. However, this method is scalable: one reader per room can monitor multiple user tags and item tags.

The design of TagMii is based on two important ideas: inductive coupling and phase profile similarity. We observed from experiments that when two tags are placed in physical proximity (e.g., 91 <2 cm), the backscatter signal from either tag would be different from the signal when a tag is put alone. And we call the status of these two closely placed tags “the inductive coupling state”. A sudden change in the backscatter signal strength from both tags will occur at the beginning of inductive coupling. TagMii tracks such signal strength changes to find out the potential tags that may be in the coupling state among all tags in the environment. In addition, TagMii further selects the pair of tags that are truly in the coupling state. We utilize the tags’ phase profiles collected by multiple antennas based on the fact that nearby RFID tags experience a similar multipath environment and, thus, exhibit similar phase profiles. By evaluating and comparing the similarities between phase profiles of potential coupling tags, TagMii can accurately identify the tags placed together and, therefore, recognize the user and the target item that is interacting with the user.

One may ask why we do not simply adopt the existing RFID tag localization protocols [13, 24, 43, 46] to localize all tags and consider any two tags in very close positions to be interacting tags. The main reason for not using this approach is its false positives. To identify the interacting tag pair, we need to not only localize the user tag and item tag accurately but also need to be able to identify and differentiate different users in the space if more than one user exists. Existing tag localization methods, if using commodity readers, are not able to achieve location errors \(\lt \!\!10\) cm [24, 46]. Hence, it is likely that two tags within 10 to 20 cm distance are localized to the same position. These tags are not necessarily the user tag and item tag that are interacting. Though PinIt [43] can locate the tagged object with a median accuracy of 11 cm, it requires the usage of a sophisticated software-defined radio device for localization, and the antennae need to be moved back and forth during the experiment.

In this work, we address and resolve three main challenges in design and implementation of TagMii. (1) We design an accurate tag coupling detection algorithm for complex environments with rich multipath, mobility, wireless signals, electrical devices, and magnetic fields. (2) TagMii should identify the coupling tags in a short period of time even though there are a large number of tags in the environment. (3) TagMii should be able to extract sufficient information from signal dynamics using commodity RFID readers, which have low measurement resolution.

We implement TagMii using commodity off-the-shelf (COTS) RFID devices only. Although we are the first to study mutual identification (interacting a tag with another tag), we do not conduct our study in a laboratory condition that is free of multipath reflectors and moving objects. In fact, all implementation and experiments are conducted in various complex environments with rich multipath, mobility, wireless signals, electrical devices, and magnetic fields in order to validate TagMii for practical applications. Even in these environments, TagMii provides high accuracy. We believe that TagMii serves as an important extension of current RFID applications and that tag mutual identification will attract further research due to its low cost and convenience, enabling consumers to successfully interact with tagged items.

The rest of the article is organized as follows. Section 2 presents the related work. Section 3 introduces the background and models. We present the detailed design of TagMii in Section 4. System implementation and evaluation results are presented in Section 5. Section 6 contains details on the evaluation methodology. We provide discussion in Section 7 and our conclusions in Section 8.

User–item interaction. Automatic identification and data capture (AIDC) techniques such as barcodes [39] and quick response (QR) codes [19] provide fast, easy, and accurate data collection approaches for inventory control and product management [40]. A barcode [39] is an optical machine-readable representation of data related to the object to which it is attached. It systematically represents data by varying the width and spacing of parallel lines. A quick response (QR) [32] code is a two-dimensional (2D) barcode developed to improve the reading speed of complex-structured 2D barcodes. It provides a very convenient way to display information, such as a URL, which can be easily scanned and processed by mobile devices [19]. A barcode or QR code requires being read by a scanner or camera. In contrast, RFID does not require such line-of-sight scanning as RF signals can penetrate through non-metal objects [27]. RFID sensing techniques have been widely used for detecting gesture-based inputs [4, 44]. IDSense [21] recognizes the physical movements and touch events of everyday objects using real-time classification of the RSSI and phase angles with only one reader. However, it requires training and calibration and is insufficiently precise. PaperID [20] is similar in that it uses supervised machine learning to detect different types of on-tag and free-air interactions with custom-designed RFID tags. Pradhan et al. [30] show how changes in the received signal phase caused by touching an RFID tag can be leveraged to detect the finger swipe or touch gesture without any pretraining stage. The authors of [9] propose and design a passive, thin metal tag called LiveTag that can be printed on paper-like substrates and attached onto objects. When being touched by fingers, LiveTag will disturb an ambient WiFi channel in a deterministic way, making the interaction observable by the system. However, it requires elaborate tag design and implementation. Researchers are also trying to solve the issue that users have to use RFID readers to query tags and, thus, cannot benefit from the convenience provided by their mobile devices. However, as mentioned in Section 1, individual users who have no reader find it difficult to interact with tagged items. TiFi [3] allows a 2.4 GHz WiFi receiver to identify 800 MHz UHF RFID tags. This system leverages the underlying harmonic backscattering of tags to open a second channel and uses it to communicate with WiFi receivers. Other related techniques include small RFID readers [2] and smartphone-based RFID scanners [1]. However, all of them require the user to carry a smartphone at minimum. Compared with these works, TagMii requires each user to carry only a tag and/or other necessary feedback devices of the application (such as headphones in the museum commentary case).

Recent studies have also proposed to study tag-to-tag interactions. RFIBricks [14] is a building-block system in which symmetric 2D patterns of RFID contact switches are deployed on the top and bottom of each block. This design enables the backend system to recognize which block is stacked on which as well as the stacking orientation. RFIMatch [22] detects finger-touching on a tag based on the correlated state change between the tag and an RFIMatch fingerstall worn by the user. Tip-Tap [17] recognizes 2D discrete touch events by sensing the intersection between two arrays of RFID tags. However, these works require physically modifying the tag circuitry and detecting the correlated state changes of electromechanical sensors in the tag circuitry caused by human behaviors. Trio [7] mainly focuses on the refined localization in a small area using RSS, which is fragile when being applied in a large dynamic space.

RFID Localization. Using RFID for localization is a possible research direction for TagMii, considering that any two tags in very close positions are interacting tags. Prior work can be divided into two categories: absolute localization and relative localization. Early RFID-based localization methods mainly rely on RSSI information to acquire tag location. They usually rely on densely pre-deploying tags in the area of monitoring and leverage the pre-collected RSSI values of these tags as references to locate a specific tag [5, 26, 49]. The major limitation of RSSI-based methods is that they are highly sensitive to multi-path propagation and, thus, cannot achieve high-accuracy localization in a multi-path environment. Meanwhile, there is a growing interest in using phase differences [16, 24, 35, 44] or Angle of Arrival (AoA) information [13] to estimate absolute locations of tags. Localization using advanced electromagnetism and communication techniques such as synthetic aperture radar (SAR) [43] or multiple antennas [46] have also been studied. However, simply adopting the existing RFID tag localization protocols [13, 24, 43, 46] to localize all tags in TagMii will not work, as existing tag localization methods using commodity readers are not able to achieve location errors \(\lt \!\!10\) cm [24, 46]. Hence, it is likely that two tags within 10 to 20 cm distance are localized to the same position. These are not necessarily the user tag and item tag that are interacting.

The TagMii program has four modules: tag interaction detection, signal preprocessing, candidate tag selection, and target tag determination (Figure 7). Tag interaction detection determines the begin and end time for an interaction action performed by the user. The signal preprocessing module performs RSS profile smoothing and phase de-periodicity over the received signal. Then, TagMii selects candidate tags by detecting the RSS decreases and, finally, determines the interacting tags among all of the candidate tags by comparing the phase profiles.

We evaluate the performance of TagMii in two complex environments with various multi-path reflectors, moving objects, wireless signals (WiFi, LTE, and Bluetooth), electrical devices (servers, workstations, printers, refrigerator), and magnetic fields (whiteboard): (a) an office room and (b) a meeting room as shown in Figure 16. The office environment simulates a retail store where shelves are densely placed; the meeting room simulates the museum application where artworks are placed on the wall. In the office environment we use a tag array that has 3 rows and 5 columns, as illustrated in Figure 16, to simulate the tags attached on a rack of commodities or exhibitions. In the meeting room, we further exploit TagMii’s performance when tags are attached in a horizontal line. The location of the tag array is highlighted with red markers shown in Figure 16.

To be consistent with the real implementation, the distance between the reader antennas and the tag array is 1.5 m in the office room and 3 m in the meeting room. Note that, in practice, reader antennas can be hung from the ceiling to reduce the probability of blocking LOS signals by moving objects. Note that, although in theory UHF RFID can operate at 10 m distance, practical deployments usually allow the distance to be only around 3 m or less. However, we are not able to reconstruct the ceiling to hang antennas. The distance between two adjacent tags is initially set to be 30 cm in the office room and 80 cm in meeting room, while the horizontal distances between them are both set to be 30 cm. We will show that longer distance between adjacent tags will result in more accurate results. Consider the application in retail stores, it would be rather time- and cost-inefficient if passive tags were attached to each item. Moreover, a large tag population will degrade the sampling rate for each tag. We may assign one item tag for each type of item. Hence, a distance of 30 cm is a practical setting. In our experiments, we invite 4 volunteers with heights varying from 160 cm to 180 cm. We let volunteers arbitrarily move in the space and use their tags to interact with the item tags in the environment. In the worst case, a moving volunteer may block the LOS path of the signal between tags and the reader antenna, as shown in Figure 17. Each user is trained for only 3 minutes on what to do to interact with item tags.

Every accuracy value shown in this section is the average of 120 production experiments. Note that all results are analyzed offline and that there is no direct communication between the reader and user devices (headphone/smartphone, etc.), meaning that the users are unable to learn the information of the item tag they are interacting with during the experiment.

In the following subsections, we will first show the evaluation results of two important steps of TagMii: interaction event detection and coupling tag determination.

Though TagMii provides accurate mutual identification in the aforementioned evaluations, we acknowledge the following limitations of the current system and propose potential solutions.

Limitations on system settings. Although TagMii can achieve relatively high accuracy in most evaluations, the limitations on experimental settings cannot be overlooked. The performance of TagMii relies on the choices of several thresholds ( \(L_h,T_c, {etc.}\) ). To achieve better system performance, it is recommended for TagMii to first learn multiple thresholds through a series of analysis (see Section 6.2) before using it.

Limitations on multi-user cases. Multi-user sensing is a well-known challenging research problem in wireless sensing, since the signals reflected from multiple targets will get mixed at the receiver, interfering with each other. As can be seen from the experiment results, the current version of TagMii works well with a single-user target. However, if multiple users read item tags almost at the same time, it is not easy for our system to distinguish them clearly. We believe it will be a challenging yet interesting research direction to deal with multi-user cases in future work.

Limitations on number of tags. The current RFID protocol uses slotted ALOHA as the MAC layer solution. Hence, tags compete with the time slots to reply to the reader. The commodity RFID readers support a constant number of successful read operations per second (around 400 on our device) regardless of the number of tags with the interrogate range. When the number of tags increases, the share of time slots of every tag decreases. We define individual reading rate (IRR) as the number of readings obtained from a particular tag per second [23]. In order to achieve high accuracy, the IRR of a tag should be sufficiently high such that its state changes can be continuously and correctly captured. Missing RSS and phase samples will consequently reduce the accuracy of both candidate tag selection and coupling tag determination.

Figure 21 shows an example in which eight items and four users are tagged. In this set of experiments, we further attach five more tags in the environment without any other changes in experiment settings. Consequently, the average IRR of each tag reduces from about 40 reads/s to fewer than 30 reads/s. Figure 21 shows that lower IRR will result in an obvious accuracy decrease.

We leverage several possible methods to improve the IRR, including (1) Decrease Electronic Product Code (EPC) length. Shorter packet durations can increase the individual reading rate. One approach is to decrease the EPC length. Following the commercial EPC C1G2 standard, the length of the EPC code can be set to 8 bits at a minimum [10]. We may adopt this length in our implementation. (2) Adopt FM0 reader inventory mode. As another approach to decrease packet lengths, the reader may use the faster reader inventory mode (i.e., FM0 mode) to speed up the communication rate [11]. (3) Utilize PHY-layer filtering. The PHY-layer filtering feature is supported by the RFID Class 1 Generation 2 (C1G2) protocol, which allows the reader to read only a subsection of the tags [23].

Future Work. In contrast to TagMii, which uses an abrupt change in a tag’s RSS caused by the user wave as the starting time indicator for coupling interaction, RFTrack regards the problem of recognizing the coupling state from an RSS signal stream as a binary time series classification (TSC) problem, where the objective is to determine whether the current RSS signal is in a coupling state.

TagMii is a new approach to enable user–item interactions using passive RFID tags. Compared with other solutions that require a user to carry non-trivial hardware, TagMii requires every user to carry only a passive tag. The reader deployed in the environment monitors the interaction events and pairs the user tag and corresponding item tag. The key advantage of TagMii is that it is cost-efficient and especially convenient for children, some seniors, people with certain disabilities, and others who do not operate smartphones. We evaluate TagMii in complex environments with rich multi-path, mobility, wireless signals, and magnetic fields and find TagMii to be accurate in recognizing user–item interactions in various setups. TagMii is the first step of tag mutual identification and will attract further research on improving its accuracy and designing new applications based this idea.

We thank the anonymous reviewers for their suggestions and comments.