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)

RePos : relative position estimation of UHF-RFID tags for item-level localization

Radio frequency identification (RFID) technology brings tremendous applications in location-based services. Specifically, ultra-high frequency (UHF) RFID tag positioning based on phase (difference) of arrival (PoA/PDoA) has won great attention, due to its better positioning accuracy than signal strength-based methods. In most cases, such as logistics, retailing, and smart inventory management, the relative orders of the objects are much more attractive than absolute positions with centimetre-level accuracy. In this paper, a relative positioning (RePos) approach based on inter-tag distance and direction estimation is proposed. In the RePos positioning system, the measured phases are reconstructed based on unwrapping method. Then the distances from antenna to the tags are calculated using the distance differences of pairs of antenna's positions via a least-squares method. The relative relationships of the tags, including relative distances and angles, are obtained based on the geometry information extracted from PDoA. The experimental results show that the RePos RFID positioning system can realize about 0.28-meter ranging accuracy, and distinguish the levels and columns without ambiguity

A survey on subjecting electronic product code and non-ID objects to IP identification

Over the last decade, both research on the Internet of Things (IoT) and real-world IoT applications have grown exponentially. The IoT provides us with smarter cities, intelligent homes, and generally more comfortable lives. However, the introduction of these devices has led to several new challenges that must be addressed. One of the critical challenges facing interacting with IoT devices is to address billions of devices (things) around the world, including computers, tablets, smartphones, wearable devices, sensors, and embedded computers, and so on. This article provides a survey on subjecting Electronic Product Code and non-ID objects to IP identification for IoT devices, including their advantages and disadvantages thereof. Different metrics are here proposed and used for evaluating these methods. In particular, the main methods are evaluated in terms of their: (i) computational overhead, (ii) scalability, (iii) adaptability, (iv) implementation cost, and (v) whether applicable to already ID-based objects and presented in tabular format. Finally, the article proves that this field of research will still be ongoing, but any new technique must favorably offer the mentioned five evaluative parameters.Comment: 112 references, 8 figures, 6 tables, Journal of Engineering Reports, Wiley, 2020 (Open Access

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)

Learning to Transform Time Series with a Few Examples

We describe a semi-supervised regression algorithm that learns to transform one time series into another time series given examples of the transformation. This algorithm is applied to tracking, where a time series of observations from sensors is transformed to a time series describing the pose of a target. Instead of defining and implementing such transformations for each tracking task separately, our algorithm learns a memoryless transformation of time series from a few example input-output mappings. The algorithm searches for a smooth function that fits the training examples and, when applied to the input time series, produces a time series that evolves according to assumed dynamics. The learning procedure is fast and lends itself to a closed-form solution. It is closely related to nonlinear system identification and manifold learning techniques. We demonstrate our algorithm on the tasks of tracking RFID tags from signal strength measurements, recovering the pose of rigid objects, deformable bodies, and articulated bodies from video sequences. For these tasks, this algorithm requires significantly fewer examples compared to fully-supervised regression algorithms or semi-supervised learning algorithms that do not take the dynamics of the output time series into account