A RFID-Integrated Framework for Tag Anti-Collision in UAV-Aided VANETs

In this paper, we investigate tags in anti-collision applications of radio frequency identification (RFID) technology in unmanned aerial vehicle (UAV)-aided vehicular ad hoc networks (VANETs). The integration of RFID technology in UAV-aided VANETs can provide reliable traffic-related services for vehicles. However, multiple tags’ simultaneous responses to a reader mounted on a UAV, denoted as tag collision, gravely affect the correct tag detection on each vehicle. Therefore, in order to decrease the collision probability and improve the throughput, we propose a multi-frequency tag identification method. In the proposed scheme, we devise a tag grouping method based on adaptive power control to make the reader dynamically match the optimal frame length. Based on the above matching results, we introduce a tag estimation method using the optimal weight to improve the accuracy of tag estimation. We theoretically analyze the closed-form expression of the security outage probability expression. Finally, our simulation results demonstrate that the proposed tag anti-collision scheme achieved significant performance superiority in terms of the throughput and identification time slots.

Clutter Suppression for Indoor Self-Localization Systems by Iteratively Reweighted Low-Rank Plus Sparse Recovery

Self-localization based on passive RFID-based has many potential applications. One of the main challenges it faces is the suppression of the reflected signals from unwanted objects (i.e., clutter). Typically, the clutter echoes are much stronger than the backscattered signals of the passive tag landmarks used in such scenarios. Therefore, successful tag detection can be very challenging. We consider two types of tags, namely low-Q and high-Q tags. The high-Q tag features a sparse frequency response, whereas the low-Q tag presents a broad frequency response. Further, the clutter usually showcases a short-lived response. In this work, we propose an iterative algorithm based on a low-rank plus sparse recovery approach (RPCA) to mitigate clutter and retrieve the landmark response. In addition to that, we compare the proposed approach with the well-known time-gating technique. It turns out that RPCA outperforms significantly time-gating for low-Q tags, achieving clutter suppression and tag identification when clutter encroaches on the time-gating window span, whereas it also increases the backscattered power at resonance by approximately 12 dB at 80 cm for high-Q tags. Altogether, RPCA seems a promising approach to improve the identification of passive indoor self-localization tag landmarks.

Reader–Tag Commands via Modulation Cutoff Intervals in RFID Systems

Radio frequency identification (RFID) technology facilitates a myriad of applications. In such applications, an efficient reader–tag interrogation process is crucial. Nevertheless, throughout reader–tag communication, significant amounts of time and power are consumed on inescapable simultaneous tag replies (i.e., collisions) due to the lack of carrier sensing at the tags. This paper proposes the modulation cutoff intervals (MCI) process as a novel reader–tag interaction given the lack of carrier sensing constraints in passive RFID tags. MCI is facilitated through a simple digital baseband modulation termination (DBMT) circuit at the tag. DBMT detects the continuous-wave cutoff by the reader. In addition, DBMT provides different flags based on the duration of the continuous-wave cutoff. Given this capability at the tag, the reader cuts off its continuous-wave transmission for predefined intervals to indicate different commands to the interrogated tag(s). The MCI process is applied to tag interrogation (or anti-collision) and tag-counting protocols. The MCI process effect was evaluated by the two protocols under high and low tag populations. The performance of such protocols was significantly enhanced with precise synchronization within time slots with more than 50% and more than 55.6% enhancement on time and power performance of anti-collision and counting protocols, respectively. Through the MCI process, fast and power-efficient tag identification is achieved in inventory systems with low and high tag mobility; alternatively, in addition to the rapid and power efficient interaction with tags, anonymous tag counting is conducted by the proposed process.

An Efficient Identification Algorithm to Identify Mobile RFID Tags

Tag identification in a fast-moving environment is an emerging challenge for future RFID systems. However, existing literatures on the tag reading protocol design primarily apply to stationary scenarios, which fail to cope with mobile environments with unreliable channel condition. In this paper, we first review various types of prior reading protocols and then discuss a new direction of mobile tag reading by proposing a novel partitioning strategy. This analysis and experimental results show its superiority in achieving reading performance for the UHF RFID system under a mobile environment.

Embracing Collisions to Increase Fidelity of Sensing Systems with COTS Tags

RFID techniques have been extensively used in sensing systems due to their low cost. However, limited by the structural simplicity, collision is one key issue which is inevitable in RFID systems, thus limiting the accuracy and scalability of such sensing systems. Existing anti-collision techniques try to enable parallel decoding without sensing based applications in mind, which can not operate on COTS RFID systems. To address the issue, we propose COFFEE, which enables parallel channel estimation of COTS passive tags by harnessing the collision. We revisit the physical layer design of current standard. By exploiting the characteristics of low sampling rate and channel diversity of RFID tags, we separate the collided data and extract the channels of the collided tags. We also propose a tag identification algorithm which explores history channel information and identify the tags without decoding. COFFEE is compatible with current COTS RFID standards which can be applied to all RFID-based sensing systems without any modification on tag side. To evaluate the real world performance of our system, we build a prototype and conduct extensive experiments. The experimental results show that we can achieve up to 7.33x median time resolution gain for the best case and 3.42x median gain on average.

An Efficient Early Frame Breaking Strategy for RFID Tag Identification in Large-Scale Industrial Internet of Things

With the increase in the number of tags, an efficient approach of tag identification is becoming an urgent need in Industrial Internet of Things (IIoT). However, the identification performance of existing Aloha-based anticollision schemes is limited when the initial frame size is seriously mismatched with the actual tag population size. The performance will degrade further when IIoT is deployed in the error-prone channel environment. To optimize the identification performance of RFID system in an error-prone channel environment, we propose an efficient early frame breaking strategy based anticollision algorithm (EFB-ACA) with channel awareness. The EFB-ACA divides the whole tag identification process into two phases: convergence phase and identification phase. The function of convergence phase is to make the adjusted frame quickly converge to an appropriate size. The early frame breaking strategy is embedded in the convergence phase. Numerical results show that the proposed EFB-ACA algorithm outperforms the other methods on efficiency and stability in the error-prone channel. In addition, EFB-ACA algorithm also outperforms the other methods in the error-free channel.

An Efficient Q -Algorithm for RFID Tag Anticollision

In large-scale Internet of Things (IoT) applications, tags are attached to items, and users use a radiofrequency identification (RFID) reader to quickly identify tags and obtain the corresponding item information. Since multiple tags share the same channel to communicate with the reader, when they respond simultaneously, tag collision will occur, and the reader cannot successfully obtain the information from the tag. To cope with the tag collision problem, ultrahigh frequency (UHF) RFID standard EPC G1 Gen2 specifies an anticollision protocol to identify a large number of RFID tags in an efficient way. The Q -algorithm has attracted much more attention as the efficiency of an EPC C1 Gen2-based RFID system can be significantly improved by only a slight adjustment to the algorithm. In this paper, we propose a novel Q -algorithm for RFID tag identification, namely, HTEQ, which optimizes the time efficiency of an EPC C1 Gen2-based RFID system to the utmost limit. Extensive simulations verify that our proposed HTEQ is exceptionally expeditious compared to other algorithms, which promises it to be competitive in large-scale IoT environments.

A Fast Hybrid Strategy-Based RFID Tag Identification Protocol

Tag collision is one of the critical problems in radiofrequency identification (RFID) technology which can be widely used to identify objects using tag attachment automatically. Through the transmission and reflection of wireless radiofrequency signals, noncontact identification is realized. However, when multiple tags respond to the reader simultaneously, a collision occurs, significantly degrading the identification performance of RFID systems. To tackle the tag collisions, we propose a fast hybrid strategy-based RFID anticollision (FHS-RAC) protocol. Based on the conventional query tree algorithm, the proposed FHS-RAC makes full use of collision bits and the total response bits to achieve the faster tag identification. Extensive simulations and experiments verify the feasibility and effectiveness of our proposed scheme.