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)

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)

Building efficient wireless infrastructures for pervasive computing environments

Pervasive computing is an emerging concept that thoroughly brings computing devices and the consequent technology into people\u27s daily life and activities. Most of these computing devices are very small, sometimes even invisible , and often embedded into the objects surrounding people. In addition, these devices usually are not isolated, but networked with each other through wireless channels so that people can easily control and access them. In the architecture of pervasive computing systems, these small and networked computing devices form a wireless infrastructure layer to support various functionalities in the upper application layer.;In practical applications, the wireless infrastructure often plays a role of data provider in a query/reply model, i.e., applications issue a query requesting certain data and the underlying wireless infrastructure is responsible for replying to the query. This dissertation has focused on the most critical issue of efficiency in designing such a wireless infrastructure. In particular, our problem resides in two domains depending on different definitions of efficiency. The first definition is time efficiency, i.e., how quickly a query can be replied. Many applications, especially real-time applications, require prompt response to a query as the consequent operations may be affected by the prior delay. The second definition is energy efficiency which is extremely important for the pervasive computing devices powered by batteries. Above all, our design goal is to reply to a query from applications quickly and with low energy cost.;This dissertation has investigated two representative wireless infrastructures, sensor networks and RFID systems, both of which can serve applications with useful information about the environments. We have comprehensively explored various important and representative problems from both algorithmic and experimental perspectives including efficient network architecture design and efficient protocols for basic queries and complicated data mining queries. The major design challenges of achieving efficiency are the massive amount of data involved in a query and the extremely limited resources and capability each small device possesses. We have proposed novel and efficient solutions with intensive evaluation. Compared to the prior work, this dissertation has identified a few important new problems and the proposed solutions significantly improve the performance in terms of time efficiency and energy efficiency. Our work also provides referrable insights and appropriate methodology to other similar problems in the research community

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