Radio Frequency Identification Data Management Research Articles

Managing RFID Data: Challenges, Opportunities and Solutions

The advances of Radio-Frequency Identification (RFID) technology have significantly enhanced the capability of capturing data from pervasive space. It becomes a great challenge in the information era to effectively understand human behavior, mobility and activity through the perceived RFID data. Focusing on RFID data management, this article provides an overview of current challenges, emerging opportunities and recent progresses in RFID. In particular, this article has described and analyzed the research work on three aspects: algorithm, protocol and performance evaluation. We investigate the research progress in RFID with anti-collision algorithms, authentication and privacy protection protocols, localization and activity sensing, as well as performance tuning in realistic settings. We emphasize the basic principles of RFID data management to understand the state-of-the-art and to address directions of future research in RFID.

Modeling Object Flows from Distributed and Federated RFID Data Streams for Efficient Tracking and Tracing

In the emerging environment of the Internet of things (IoT), through the connection of billions of radio frequency identification (RFID) tags and sensors to the Internet, applications will generate an unprecedented number of transactions and amount of data that require novel approaches in RFID data stream processing and management. Unfortunately, it is difficult to maintain a distributed model without a shared directory or structured index. In this paper, we propose a fully distributed model for federated RFID data streams. This model combines two techniques, namely, tilted time frame and histogram to represent the patterns of object flows. Our model is efficient in space and can be stored in main memory. The model is built on top of an unstructured P2P overlay. To reduce the overhead of distributed data acquisition, we further propose several algorithms that use a statistically minimum number of network calls to maintain the model. The scalability and efficiency of the proposed model are demonstrated through an extensive set of experiments.

The Development of RFID Data Management System Based on Embedded System

Embedded system in general is formed by the embedded computer system and implementing device. Embedded system is a control program stored in ROM embedded processor control board. The basic function of Radio Frequency Identification (RFID) is to realize the precise data acquisition. RFID reader via the antenna and the RFID tag for wireless communication can be achieved on the label identification code and a memory data read or write operation. The paper presents the development of RFID data management system based on embedded system. Experimental results show that the proposed algorithm has high efficiency.

A Split-Path Schema-Based RFID Data Storage Model in Supply Chain Management

In modern supply chain management systems, Radio Frequency IDentification (RFID) technology has become an indispensable sensor technology and massive RFID data sets are expected to become commonplace. More and more space and time are needed to store and process such huge amounts of RFID data, and there is an increasing realization that the existing approaches cannot satisfy the requirements of RFID data management. In this paper, we present a split-path schema-based RFID data storage model. With a data separation mechanism, the massive RFID data produced in supply chain management systems can be stored and processed more efficiently. Then a tree structure-based path splitting approach is proposed to intelligently and automatically split the movement paths of products. Furthermore, based on the proposed new storage model, we design the relational schema to store the path information and time information of tags, and some typical query templates and SQL statements are defined. Finally, we conduct various experiments to measure the effect and performance of our model and demonstrate that it performs significantly better than the baseline approach in both the data expression and path-oriented RFID data query performance.

Behavior-Based Cleaning for Unreliable RFID Data Sets

Radio Frequency IDentification (RFID) technology promises to revolutionize the way we track items and assets, but in RFID systems, missreading is a common phenomenon and it poses an enormous challenge to RFID data management, so accurate data cleaning becomes an essential task for the successful deployment of systems. In this paper, we present the design and development of a RFID data cleaning system, the first declarative, behavior-based unreliable RFID data smoothing system. We take advantage of kinematic characteristics of tags to assist in RFID data cleaning. In order to establish the conversion relationship between RFID data and kinematic parameters of the tags, we propose a movement behavior detection model. Moreover, a Reverse Order Filling Mechanism is proposed to ensure a more complete access to get the movement behavior characteristics of tag. Finally, we validate our solution with a common RFID application and demonstrate the advantages of our approach through extensive simulations.

RFIDTango: A Scenario-Based Massive RFID Data Generator

RFID (Radio Frequency IDentification) technology has been widely used in many applications. In recent years, researchers have begun to study various RFID data management problems. Unfortunately, it is difficult for researchers to obtain massive raw RFID data because of hardware cost and limitations of lab environment. We have designed RFIDTango, a massive raw RFID data generator, to simulate real RFID application scenarios. In this paper, we present a system of RFIDTango and show how RFIDTango can simulate a RFID-enabled supermarket scenario effectively and efficiently.

Adaptive product tracking in RFID-enabled large-scale supply chain

The Radio Frequency Identification (RFID) technology is gradually being adopted and deployed for product flow management in the supply chain. In order to track RFID-tagged products efficiently in the RFID-enabled, large-scale supply chain, this paper first presents the design of a product tracking system that can collaborate with the EPC Network, a suite of network services for RFID data management in the supply chain. Next, we explain a product monitoring procedure that is performed by comparing the actual path of a product with its planned path. Finally, we develop an adaptive product search algorithm based on a reinforcement learning technique to efficiently locate a product deviated from its planned path. Experiment results are provided to show the performance of the search algorithm.

RFID Application Development With the Accada Middleware Platform

The proliferation of radio frequency identification (RFID) systems in application domains such as supply chain management requires an IT infrastructure that provides RFID device and data management and supports application development. In this paper, we discuss these application requirements in detail. We also contend that the characteristics of passive RFID technology introduce constraints that are unique to the development of middleware for the RFID domain. These constraints include the occurrence of false negative reads, tag memory variations, the heterogeneous reader landscape, and the limited communication bandwidth available to RFID readers. To address these constraints and the application requirements for filtered and aggregated RFID data, we developed Accada, an open source RFID platform. This paper shows that the Accada implementation, which is based on a set of specifications developed by the EPCglobal community and a number of extensions, such as the surrogate concept and the virtual tag memory service, addresses the majority of the application requirements and limitations of passive RFID technology.

RFID 데이터 스트림의 효율적인 필터링 기법

Radio Frequency Identification(RFID) technology is set to play an essential role in object tracking or supply chain management systems. New challenges for RFID data management are needed in the RFID applications. RFID data are generated quickly and automatically, and can be used for object tracking, or for real-time monitoring. These applications are mostly associated with the timestamps when the events happen. In this paper, we propose a temporal RFID data model to maintain the history of events and state changes and to monitor the states of RFID objects. Also we propose data filtering method of non active data based on temporal RFID data model. This data model involves essential basic operations for RFID data. We show increased query performance through the data filtering method of non active data.