A group-based binary splitting algorithm For UHF RFID anti-collision systems

Identification efficiency is a key performance metrics to evaluate the ultra high frequency(UHF) based radio frequency identification (RFID) systems. In order to solve the tag collision problem and improve the identification rate in large scale networks, we propose a collision arbitration strategy termed as group-based binary splitting algorithm (GBSA), which is an integration of an efficient tag cardinality estimation method, an optimal grouping strategy and a modified binary splitting. In GBSA, tags are properly divided into multiple subsets according to the tag cardinality estimation and the optimal grouping strategy. In case that multiple tags fall into a same time slot and form a subset, the modified binary splitting strategy will be applied while the rest tags are waiting in the queue and will be identified in the following slots. To evaluate its performance, we first derive the closed-form expression of system throughput for GBSA. Through the theoretical analysis, the optimal grouping factor is further determined. Extensive simulation results supplemented by prototyping tests indicate that the system throughput of our proposed algorithm can reach as much as 0.4835, outperforming the existing anti-collision algorithms for UHF RFID systems

Node Cardinality Estimation in the Internet of Things Using Privileged Feature Distillation

The Internet of Things (IoT) is emerging as a critical technology to connect resource-constrained devices such as sensors and actuators as well as appliances to the Internet. In this paper, we propose a novel methodology for node cardinality estimation in wireless networks such as the IoT and Radio-Frequency IDentification (RFID) systems, which uses the privileged feature distillation (PFD) technique and works using a neural network with a teacher-student model. The teacher is trained using both privileged and regular features, and the student is trained with predictions from the teacher and regular features. We propose node cardinality estimation algorithms based on the PFD technique for homogeneous as well as heterogeneous wireless networks. We show via extensive simulations that the proposed PFD based algorithms for homogeneous as well as heterogeneous networks achieve much lower mean squared errors in the computed node cardinality estimates than state-of-the-art protocols proposed in prior work, while taking the same number of time slots for executing the node cardinality estimation process as the latter protocols.Comment: 15 pages, 17 figures, journal pape

Distributed Wireless Algorithms for RFID Systems: Grouping Proofs and Cardinality Estimation

The breadth and depth of the use of Radio Frequency Identification (RFID) are becoming more substantial. RFID is a technology useful for identifying unique items through radio waves. We design algorithms on RFID-based systems for the Grouping Proof and Cardinality Estimation problems. A grouping-proof protocol is evidence that a reader simultaneously scanned the RFID tags in a group. In many practical scenarios, grouping-proofs greatly expand the potential of RFID-based systems such as supply chain applications, simultaneous scanning of multiple forms of IDs in banks or airports, and government paperwork. The design of RFID grouping-proofs that provide optimal security, privacy, and efficiency is largely an open area, with challenging problems including robust privacy mechanisms, addressing completeness and incompleteness (missing tags), and allowing dynamic groups definitions. In this work we present three variations of grouping-proof protocols that implement our mechanisms to overcome these challenges. Cardinality estimation is for the reader to determine the number of tags in its communication range. Speed and accuracy are important goals. Many practical applications need an accurate and anonymous estimation of the number of tagged objects. Examples include intelligent transportation and stadium management. We provide an optimal estimation algorithm template for cardinality estimation that works for a {0,1,e} channel, which extends to most estimators and ,possibly, a high resolution {0,1,...,k-1,e} channel

Rapid Node Cardinality Estimation in Heterogeneous Machine-to-Machine Networks

Machine-to-Machine (M2M) networks are an emerging technology with applications in various fields, including smart grids, healthcare, vehicular telematics and smart cities. Heterogeneous M2M networks contain different types of nodes, e.g., nodes that send emergency, periodic, and normal type data. An important problem is to rapidly estimate the number of active nodes of each node type in every time frame in such a network. In this paper, we design two schemes for estimating the active node cardinalities of each node type in a heterogeneous M2M network with $T$ types of nodes, where $T \ge 2$ is an arbitrary integer. Our schemes consist of two phases-- in phase 1, coarse estimates are computed, and in phase 2, these estimates are used to compute the final estimates to the required accuracy. We analytically derive a condition for one of our schemes that can be used to decide as to which of two possible approaches should be used in phase 2 to minimize its execution time. The expected number of time slots required to execute and the expected energy consumption of each active node under one of our schemes are analysed. Using simulations, we show that our proposed schemes require significantly fewer time slots to execute compared to estimation schemes designed for a heterogeneous M2M network in prior work, and also, compared to separately executing a well-known estimation protocol designed for a homogeneous network in prior work $T$ times to estimate the cardinalities of the $T$ node types, even though all these schemes obtain estimates with the same accuracy.Comment: 14 pages, 21 figure