A Review on Missing Tags Detection Approaches in RFID System

Radio Frequency Identification (RFID) system can provides automatic detection on very large number of tagged objects within short time. With this advantage, it is been using in many areas especially in the supply chain management, manufacturing and many others. It has the ability to track individual object all away from the manufacturing factory until it reach the retailer store. However, due to its nature that depends on radio signal to do the detection, reading on tagged objects can be missing due to the signal lost. The signal lost can be caused by weak signal, interference and unknown source. Missing tag detection in RFID system is truly significant problem, because it makes system reporting becoming useless, due to the misleading information generated from the inaccurate readings. The missing detection also can invoke fake alarm on theft, or object left undetected and unattended for some period. This paper provides review regarding this issue and compares some of the proposed approaches including Window Sub-range Transition Detection (WSTD), Efficient Missing-Tag Detection Protocol (EMD) and Multi-hashing based Missing Tag Identification (MMTI) protocol. Based on the reviews it will give insight on the current challenges and open up for a new solution in solving the problem of missing tag detection

Efficient unknown tag identification protocols in large-scale RFID systems

PublishedJournal ArticleOwing to its attractive features such as fast identification and relatively long interrogating range over the classical barcode systems, radio-frequency identification (RFID) technology possesses a promising prospect in many practical applications such as inventory control and supply chain management. However, unknown tags appear in RFID systems when the tagged objects are misplaced or unregistered tagged objects are moved in, which often causes huge economic losses. This paper addresses an important and challenging problem of unknown tag identification in large-scale RFID systems. The existing protocols leverage the Aloha-like schemes to distinguish the unknown tags from known tags at the slot level, which are of low time-efficiency, and thus can hardly satisfy the delay-sensitive applications. To fill in this gap, two filtering-based protocols (at the bit level) are proposed in this paper to address the problem of unknown tag identification efficiently. Theoretical analysis of the protocol parameters is performed to minimize the execution time of the proposed protocols. Extensive simulation experiments are conducted to evaluate the performance of the protocols. The results demonstrate that the proposed protocols significantly outperform the currently most promising protocols.This work was supported by NSFC (Grant Nos. 60973117, 61173160, 61173162, and 60903154), New Century Excellent Talents in University (NCET) of Ministry of Education of China, The Research Fund for the Doctoral Program of Higher Education (Program No. 20130041110019) and the National Science Foundation for Distinguished Young Scholars of China (Grant No. 61225010)

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Energy-efficient active tag searching in large scale RFID systems

Radio Frequency Identification (RFID) has attracted much research attention in recent years. RFID can support automatic information tracing and management during the management process in many fields. A typical field that uses RFID is modern warehouse management, where products are attached with tags and the inventory of products is managed by retrieving tag IDs. Many practical applications require searching a group of tags to determine whether they are in the system or not. The existing studies on tag searching mainly focused on improving the time efficiency but paid little attention to energy efficiency which is extremely important for active tags powered by built-in batteries. To fill in this gap, this paper investigates the tag searching problem from the energy efficiency perspective. We first propose an Energy-efficient tag Searching protocol in Multiple reader RFID systems, namely ESiM, which pushes per tag energy consumption to the limit as each tag needs to exchange only one bit data with the reader. We then develop a time efficiency enhanced version of ESiM, namely TESiM, which can dramatically reduce the execution time while only slightly increasing the transmission overhead. Extensive simulation experiments reveal that, compared to state-of-the-art solution in the current literature, TESiM reduces per tag energy consumption by more than one order of magnitude subject to comparable execution time. In most considered scenarios, TESiM even reduces the execution time by more than 50%.This work is partially supported by the National Science Foundation of China (Grant Nos. 61103203, 61332004, 61402056 and 61420106009), NSFC/RGC Joint Research Scheme (Grant No. N_PolyU519/12), and the EU FP7 CLIMBER project (Grant Agreement No. PIRSES-GA-2012-318939)

AirCode: Unobtrusive Physical Tags for Digital Fabrication

We present AirCode, a technique that allows the user to tag physically fabricated objects with given information. An AirCode tag consists of a group of carefully designed air pockets placed beneath the object surface. These air pockets are easily produced during the fabrication process of the object, without any additional material or postprocessing. Meanwhile, the air pockets affect only the scattering light transport under the surface, and thus are hard to notice to our naked eyes. But, by using a computational imaging method, the tags become detectable. We present a tool that automates the design of air pockets for the user to encode information. AirCode system also allows the user to retrieve the information from captured images via a robust decoding algorithm. We demonstrate our tagging technique with applications for metadata embedding, robotic grasping, as well as conveying object affordances.Comment: ACM UIST 2017 Technical Paper