We present a new vision for smart objects and the Internet of Things wherein mobile robots interact with wirelessly-powered, long-range, ultra-high frequency radio frequency identification (UHF RFID) tags outfitted with sensing capabilities. We explore the technology innovations driving this vision by examining recently-commercialized sensor tags that could be affixed-to or embedded-in objects or the environment to yield true embodied intelligence. Using a pair of autonomous mobile robots outfitted with UHF RFID readers, we explore several potential applications where mobile robots interact with sensor tags to perform tasks such as: soil moisture sensing, remote crop monitoring, infrastructure monitoring, water quality monitoring, and remote sensor deployment.
Roboticists have employed RFID to great effect. The unique identifier has proved useful for object recognition @cite_11 , as a high-confidence landmark in SLAM implementations @cite_4 , for waypoint navigation @cite_1 , and as a complementary sensing modality for multi-sensor fusion @cite_3 . Many of these systems rely on low-frequency (LF) and high-frequency (HF) RFID tags, which have very short read ranges (5-10 @math cm ). In contrast, ultra-high frequency (UHF) tags can be used for both short-range operation @cite_15 as well as long-range operation out to several meters @cite_18 . In robotics, UHF RFID tags have been used for robot localization @cite_20 , to locate tagged objects @cite_8 , for medication delivery @cite_23 , and for manipulation @cite_2 .
@cite_18 @cite_4 @cite_8 @cite_1 @cite_3 @cite_23 @cite_2 @cite_15 @cite_20 @cite_11
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We address the challenge of finding and navigating to an object with an attached ultra-high frequency radiofrequency identification (UHF RFID) tag. With current off-theshelf technology, one can affix inexpensive self-adhesive UHF RFID tags to hundreds of objects, thereby enabling a robot to sense the RF signal strength it receives from each uniquely identified object. The received signal strength indicator (RSSI) associated with a tagged object varies widely and depends on many factors, including the object’s pose, material properties and surroundings. This complexity creates challenges for methods that attempt to explicitly estimate the object’s pose. We present an alternative approach that formulates finding and navigating to a tagged object as an optimization problem where the robot must find a pose of a directional antenna that maximizes the RSSI associated with the target tag. We then present three autonomous robot behaviors that together perform this optimization by combining global and local search. The first behavior uses sparse sampling of RSSI across the entire environment to move the robot to a location near the tag; the second samples RSSI over orientation to point the robot toward the tag; and the third samples RSSI from two antennas pointing in different directions to enable the robot to approach the tag. We justify our formulation using the radar equation and associated literature. We also demonstrate that it has good performance in practice via tests with a PR2 robot from Willow Garage in a house with a variety of tagged household objects. We present a unique multi-antenna RFID reader (a sensor) embedded in a robot's manipulator that is designed to operate with ordinary UHF RFID tags in a short-range, near-field electromagnetic regime. Using specially designed near-field antennas enables our sensor to obtain spatial information from tags at ranges of less than 1 meter. In this work, we characterize the near-field sensor's ability to detect tagged objects in the robots manipulator, present robot behaviors to determine the identity of a grasped object, and investigate how additional RF signal properties can be used for “pre-touch” capabilities such as servoing to grasp an object. The future combination of long-range (far-field) and short-range (near-field) UHF RFID sensing has the potential to enable roboticists to jump-start applications by obviating or supplementing false-positive-prone visual object recognition. These techniques may be especially useful in the healthcare and service sectors, where mis-identification of an object (for example, a medication bottle) could have catastrophic consequences. In recent years, there has been an increasing interest within the robotics community in investigating whether Radio Frequency Identification (RFID) technology can be utilized to solve localization and mapping problems in the context of mobile robots. We present a novel sensor model which can be utilized for localizing RFID tags and for tracking a mobile agent moving through an RFID-equipped environment. The proposed probabilistic sensor model characterizes the received signal strength indication (RSSI) information as well as the tag detection events to achieve a higher modeling accuracy compared to state-of-the-art models which deal with one of these aspects only. We furthermore propose a method that is able to bootstrap such a sensor model in a fully unsupervised fashion. Real-world experiments demonstrate the effectiveness of our approach also in comparison to existing techniques. Radio Frequency Identification (RFID) technology has been widely used in inventory management in many scenarios, e.g., warehouses, retail stores, hospitals, etc. This paper investigates a challenging problem of complete identification of missing tags in large-scale RFID systems. Although this problem has attracted extensive attention from academy and industry, the existing work can hardly satisfy the stringent real-time requirements. In this paper, a Slot Filter-based Missing Tag Identification (SFMTI) protocol is proposed to reconcile some expected collision slots into singleton slots and filter out the expected empty slots as well as the unreconcilable collision slots, thereby achieving the improved time-efficiency. The theoretical analysis is conducted to minimize the execution time of the proposed SFMTI. We then propose a cost-effective method to extend SFMTI to the multi-reader scenarios. The extensive simulation experiments and performance results demonstrate that the proposed SFMTI protocol outperforms the most promising Iterative ID-free Protocol (IIP) by reducing nearly 45 of the required execution time, and is just within a factor of 1.18 from the lower bound of the minimum execution time. Radio frequency identification (RFID) technologies are poised to revolutionize retail, warehouse, and supply chain management. One of their interesting applications is to automatically detect missing tags in a large storage space, which may have to be performed frequently to catch any missing event such as theft in time. Because RFID systems typically work under low-rate channels, past research has focused on reducing execution time of a detection protocol to prevent excessively long protocol execution from interfering normal inventory operations. However, when active tags are used for a large spatial coverage, energy efficiency becomes critical in prolonging the lifetime of these battery-powered tags. Furthermore, much of the existing literature assumes that the channel between a reader and tags is reliable, which is not always true in reality because of noise interference in the environment. Given these concerns, this paper makes three contributions. First, we propose a novel protocol design that considers both energy efficiency and time efficiency. It achieves multifold reduction in both energy cost and execution time when compared to the best existing work. Second, we reveal a fundamental energy-time tradeoff in missing-tag detection, which can be flexibly controlled through a couple of system parameters in order to achieve desirable performance. Third, we extend our protocol design to consider channel error under two different models. We find that energy time cost will be higher in unreliable channel conditions, but the energy-time tradeoff relation persists. RFID (radio frequency identification) technologies are poised to revolutionize retail, warehouse and supply chain management. One of their interesting applications is to automatically detect missing tags (and the associated objects) in a large storage space. In order to timely catch any missing event such as theft, the detection operation may have to be performed frequently. Because RFID systems typically work under low-rate channels, past research has focused on reducing execution time of a detection protocol, in order to prevent excessively-long protocol execution from interfering normal inventory operations. However, when active tags are used to provide a large spatial coverage, energy efficiency becomes critical in prolonging the lifetime of these battery-powered tags. Existing literature lacks thorough study on how to conserve energy in the process of missing-tag detection and how to jointly optimize energy efficiency and time efficiency. This paper makes two important contributions: First, we propose a novel protocol design that takes both energy efficiency and time efficiency into consideration. It achieves multi-fold reduction in both energy cost and execution time when comparing with the best existing work. In some cases, the reduction is more than an order of magnitude. Second, we reveal a fundamental energy-time tradeoff in missing-tag detection. Through our analytical framework, we are able to flexibly control the tradeoff through a couple of system parameters in order to achieve desirable performance. Reliable and energy-efficient reading of Radio Frequency IDentification (RFID) tags is of utmost importance, especially in mobile and dense tag settings. We identify tag collisions as a main source of inefficiency in terms of wasting both medium access control (MAC) frame slots and reader’s energy. We propose modulation silencing (MS), a reader-tag interaction framework to limit the effect of tag collisions. Utilizing relatively simple circuitry at the tag, MS enhances the performance of existing anti-collision protocols by allowing readers to terminate collision slots once a decoding violation is detected. With shorter collision slots, we revisit the performance metrics and introduce a new generalized time efficiency metric and an optimal frame selection formula that takes into consideration the MS effects. Through analytical solutions and extensive simulations, we show that the use of MS results in significant performance gains under various scenarios. In order to protect privacy, Radio Frequency Identification (RFID) systems employ Privacy-Preserving Authentication (PPA) to allow valid readers to explicitly authenticate their dominated tags without leaking private information. Typically, an RF tag sends an encrypted message to the reader, then the reader searches for the key that can decrypt the cipher to identify the tag. Due to the large-scale deployment of today's RFID systems, the key search scheme for any PPA requires a short response time. Previous designs construct balance-tree based key management structures to accelerate the search speed to O(logN), where N is the number of tags. Being efficient, such approaches are vulnerable to compromising attacks. By capturing a small number of tags, compromising attackers are able to identify other tags that have not been corrupted. To address this issue, we propose an Anti- Compromising authenticaTION protocol, ACTION, which employs a novel sparse tree architecture, such that the key of every tag is independent from one another. The advantages of this design include: 1) resilience to the compromising attack, 2) reduction of key storage for tags from O(logN) to O(1), which is significant for resource critical tag devices, and 3) high search efficiency, which is O(logN), as good as the best in the previous designs. Purpose – Radio frequency identification (RFID) is a technology for tracking objects that is expected to be widely adopted in very near future. A reader device sends probes to a set of RFID tags, which then respond to the request. A tag is recognized only when it is the only one to respond to the probe. Only reader has collision detection capability. The problem considered here is to minimize the number of probes necessary for reading all the tags, assuming that the number of tags is known in advance.Design methodology approach – Well known binary and n‐ary partitioning algorithms can be applied to solve the problem for the case of known number of tags. A new randomized hybrid tag identification protocol has been proposed, which combines the two partitioning algorithms into a more efficient one. The new scheme optimizes the binary partition protocol for small values of n (e.g. n=2, 3, 4). The hybrid scheme then applies n‐ary partition protocol on the whole set, followed by binary partition on the tags tha... A radio frequency identification (RFID) reader recognizes objects through wireless communications with RFID tags. Tag collision arbitration for passive tags is a significant issue for fast tag identification due to communication over a shared wireless channel. This paper presents an adaptive memoryless protocol, which is an improvement on the query tree protocol. Memoryless means that tags need not have additional memory except ID for identification. To reduce collisions and identify tags promptly, we use information obtained from the last process of tag identification at a reader. Our performance evaluation shows that the adaptive memoryless protocol causes fewer collisions and takes shorter delay for recognizing all tags while preserving lower communication overhead than other tree based tag anticollision protocols RFID (radio-frequency identification) is an emerging technology with extensive applications such as transportation and logistics, object tracking, and inventory management. How to quickly identify the missing RFID tags and thus their associated objects is a practically important problem in many large-scale RFID systems. This paper presents three novel methods to quickly identify the missing tags in a large-scale RFID system of thousands of tags. Our protocols can reduce the time for identifying all the missing tags by up to 75 in comparison to the state of art. Radio frequency identification systems with passive tags are powerful tools for object identification. However, if multiple tags are to be identified simultaneously, messages from the tags can collide and cancel each other out. Therefore, multiple read cycles have to be performed in order to achieve a high recognition rate. For a typical stochastic anti-collision scheme, we show how to determine the optimal number of read cycles to perform under a given assurance level determining the acceptable rate of missed tags. This yields an efficient procedure for object identification. We also present results on the performance of an implementation. Abstract The safety of medical data and equipment plays a vital role in today’s world of Medical Internet of Things (MIoT). These IoT devices have many constraints (e.g., memory size, processing capacity, and power consumption) that make it challenging to use cost-effective and energy-efficient security solutions. Recently, researchers have proposed a few Radio-Frequency Identification (RFID) based security solutions for MIoT. The use of RFID technology in securing IoT systems is rapidly increasing because it provides secure and lightweight safety mechanisms for these systems. More recently, authors have proposed a lightweight RFID mutual authentication (LRMI) protocol. The authors argue that LRMI meets the necessary security requirements for RFID systems, and the same applies to MIoT applications as well. In this paper, our contribution has two-folds, firstly we analyze the LRMI protocol’s security to demonstrate that it is vulnerable to various attacks such as secret disclosure, reader impersonation, and tag traceability. Also, it is not able to preserve the anonymity of the tag and the reader. Secondly, we propose a new secure and lightweight mutual RFID authentication (SecLAP) protocol, which provides secure communication and preserves privacy in MIoT systems. Our security analysis shows that the SecLAP protocol is robust against de-synchronization, replay, reader tag impersonation, and traceability attacks, and it ensures forward and backward data communication security. We use Burrows-Abadi-Needham (BAN) logic to validate the security features of SecLAP. Moreover, we compare SecLAP with the state-of-the-art and validate its performance through a Field Programmable Gate Array (FPGA) implementation, which shows that it is lightweight, consumes fewer resources on tags concerning computation functions, and requires less number of flows. Comparing with the classical barcode system, RFID extends the operational distance from inches to a number of feet (passive RFID tags) or even hundreds of feet (active RFID tags). Their wireless transmission, processing and storage capabilities enable them to support the full automation of many inventory management functions in the industry. This paper studies the practically important problem of monitoring a large set of RFID tags and identifying the missing ones - the objects that the missing tags are associated with are likely to be missing, too. This monitoring function may need to be executed frequently and therefore should be made efficient in terms of execution time, in order to avoid disruption of normal inventory operations. Based on probabilistic methods, we design a series of missing-tag identification protocols that employ novel techniques to reduce the execution time. Our best protocol reduces the time for detecting the missing tags by 88.9 or more, when comparing with existing protocols. With the emerging mass production of very small, cheap Radio Frequency Identification (RFID) tags, it is becoming feasible to deploy such tags on a large scale. In this paper, we advocate distribution schemes where passive RFID tags are deployed in vast quantities and in a highly redundant fashion over large areas or object surfaces. We show that such an approach opens up a whole spectrum of possibilities for creating novel RFID-based services and applications, including a new means of cooperation between mobile physical entities. We also discuss a number of challenges related to this approach, such as the density and structure of tag distributions, and tag typing and clustering. Finally, we outline two prototypical applications (a smart autonomous vacuum cleaner and a collaborative map-making system) and indicate future directions of research. RFIDs are emerging as a vital component of the Internet of Things. In 2012, billions of RFIDs have been deployed to locate equipment, track drugs, tag retail goods, etc. Current RFID systems, however, can only identify whether a tagged object is within radio range (which could be up to tens of meters), but cannot pinpoint its exact location. Past proposals for addressing this limitation rely on a line-of-sight model and hence perform poorly when faced with multipath effects or non-line-of-sight, which are typical in real-world deployments. This paper introduces the first fine-grained RFID positioning system that is robust to multipath and non-line-of-sight scenarios. Unlike past work, which considers multipath as detrimental, our design exploits multipath to accurately locate RFIDs. The intuition underlying our design is that nearby RFIDs experience a similar multipath environment (e.g., reflectors in the environment) and thus exhibit similar multipath profiles. We capture and extract these multipath profiles by using a synthetic aperture radar (SAR) created via antenna motion. We then adapt dynamic time warping (DTW) techniques to pinpoint a tag's location. We built a prototype of our design using USRP software radios. Results from a deployment of 200 commercial RFIDs in our university library demonstrate that the new design can locate misplaced books with a median accuracy of 11 cm. Radio Frequency IDentification (RFID) technology has been adopted in many applications, such as inventory control, object tracking, theft prevention, and supply chain management. Privacy-preserving authentication in RFID systems is a very important problem. Existing protocols employ tree structures to achieve fast authentication. We observe that these protocols require a tag to transmit a large amount of data in each authentication, which costs significant bandwidth and energy overhead. Current protocols also impose heavy computational demand on the RFID reader. To address these issues, we design two privacy-preserving protocols based on a new technique called cryptographical encoding, which significantly reduces both authentication data transmitted by each tag and computation overhead incurred at the reader. Our analysis shows that the new protocols are able to reduce authentication data by more than an order of magnitude and reduce computational demand by about an order of magnitude, when comparing with the best existing protocol. Radio-frequency identification devices (RFID) may emerge as one of the most pervasive computing technologies in history. On the one hand, with tags affixed to consumer items as well as letters, packets or vehicles costs in the supply chain can be greatly reduced and new applications introduced. On the other hand, unique means of identification in each tag like serial numbers enable effortless traceability of persons and goods. But data protection and privacy are worthwhile civil liberties. We introduce a simple scheme relying on one way hash-functions that greatly enhances location privacy by changing traceable identifiers on every read getting by with only a single, unreliable message exchange. Thereby the scheme is safe from many threats like eavesdropping, message interception, spoofing, and replay attacks. We investigate the use of received RF-signals for activity recognition in scenarios with multiple receive nodes and multiple simultaneously active individuals. Our system features a short 0.5 second window over which features are calculated and we report on experiences in the choice of the neighbourhood size of the k-nearest neighbour (k-NN) classifier utilised. In a case study with software defined radio nodes utilised in an active, device-free activity recognition (DFAR) system, we observe a good recognition accuracy for the recognition of multiple simultaneously conducted activities with two and more receive devices. This is the first study to distinguish this particular set of activities from users conducting them simultaneously. For a single individual, we repeat the experiment and report the recognition accuracy in scenarios where the recognition area per receive node is larger than 8sqm In traditional radio-based localization methods, the target object has to carry a transmitter (e.g., active RFID), a receiver (e.g., 802.11x detector), or a transceiver (e.g., sensor node). However, in some applications, such as safe guard systems, it is not possible to meet this precondition. In this paper, we propose a model of signal dynamics to allow tracking of transceiver-free objects. Based on radio signal strength indicator (RSSI), which is readily available in wireless communication, three tracking algorithms are proposed to eliminate noise behaviors and improve accuracy. The midpoint and intersection algorithms can be applied to track a single object without calibration, while the best-cover algorithm has potential to track multiple objects but requires calibration. Our experimental test-bed is a grid sensor array based on MICA2 sensor nodes. The experimental results show that the best side length between sensor nodes in the grid is 2 meters and the best-cover algorithm can reach localization accuracy to 0.99 m