In recent years, there is an increasing demand for indoor localization services with the aim to locate people and objects inside buildings. However, localization accuracy is susceptible to inaccurate and high variant sensor measurements due to the unpredictable fluctuations of received wireless signals and the sensitivity of hardware devices. To address this issue, in this paper, we establish a new Bluetooth indoor localization system, whose architecture can be basically decomposed into two parts: the internet-of-things (IoT) framework and the localization module. Concretely, the IoT platform uses the state-of-the-art light weight Spring Boot microservice framework consisting of multi-layer structure. In the localization module, it follows the general process of trilateration but significantly distinguished from it. A set of measures are adopted to strengthen the system’s robustness when obtained measurements cannot be fully trusted. Specifically, in the first place, rather than using conventional propagation model to predict the distance between Bluetooth transmitter and receiver, we design a bran-new LSTM-based distance estimator which can better depict the nonlinearity of attenuation characteristics of radio signal. Moreover, we also employ a series of self-adaptive mechanisms, including elastic radius intersecting, multiple weighted centroid localization and self-adaptive Kalman tracking, to make the system robust against inaccurate measurements and unpredictable sudden variation of received wireless signal. A bunch of tests are conducted in both ideal lab environment and Alibaba’s large-scale warehouse, and experimental results show our indoor localization system outperforms the state-of-the-art benchmarks by a large margin in both localization accuracy and stability.

This paper studies the important sensory data collection problem in the sensor-augmented RFID systems, which is to quickly and accurately collect sensory data from a predefined set of target tags with the coexistence of unexpected tags. The existing RFID data collection schemes suffer from either low time-efficiency due to tag-collisions or serious data corruption issue due to interference of unexpected tags. To overcome these limitations, we propose the hierarchical-hashing data collection (HDC) protocol, which can not only significantly improve the utilization of RFID wireless communication channel by establishing bijective mapping between k target tags and the first k slots in time frame, but also effectively filter out the serious interference of unexpected tags. Although HDC has attractive advantages, the theoretical analysis reveals that the computation cost involved in it is as huge as O(k2k), where k is normally large in practice. By making some modifications to the basic HDC protocol, we propose the multi-framed hierarchical-hashing data collection (MHDC) protocol to effectively reduce the involved computation complexity. Unlike HDC that only issues a single time frame, MHDC uses multiple time frames to collaboratively collect sensory data from the k target tags. It can be understood as that a big computation task is disintegrated into multiple small pieces and then shared by multiple time frames. As a result, the computation cost involved in MHDC is reduced to O(k2n), where n ≪ k is the expected number of target tags that each time frame handles. Theoretical analysis is given to jointly consider the communication cost and computation cost thereby maximizing the overall time-efficiency of MHDC. Extensive simulation results reveal that the proposed MHDC protocol can correctly collect all sensory data and is always about more than 2× faster than the state-of-the-art RFID sensory data collection protocols.

This paper studies the practically important problem of range query for sensor-augmented RFID systems, which is to classify the target tags according to the ranges specified by the user. The existing RFID protocols that seem to address this problem suffer from either low time-efficiency or the information corruption issue. To overcome their limitations, we first propose a basic classification protocol called Range Query (RQ), in which each tag pseudo-randomly chooses a slot from the time frame and uses the ON-OFF Keying modulation to reply its range identifier. Then, RQ employs a collaborative decoding method to extract the tag range information from singleton and even collision slots. The numerical results reveal that the number of queried ranges significantly affects the performance of RQ. To optimize the number of queried ranges, we further propose the Partition&Mergence (PM) approach that consists of two steps, i.e., top-down partitioning and bottom-up merging. Sufficient theoretical analyses are proposed to optimize the involved parameters, thereby minimizing the time cost of RQ+PM or minimizing its energy cost. We can trade off between time cost and energy cost by adjusting the related parameters. The prominent advantages of the RQ+PM protocol over previous protocols are two-fold: (i) it is able to make use of the collision slots, which are treated as useless in previous protocols. Thus, frame utilization can be significantly improved; (ii) it is immune to the interference from unexpected tags, and does not suffer information corruption issue. We use USRP and WISP tags to conduct a set of experiments, which demonstrate the feasibility of RQ+PM. Extensive simulation results reveal that RQ+PM can ensure 100% query accuracy, and reduce the time cost as much as 40% when comparing with the state-of-the-art protocols.