This paper discusses how the state-of-the-art techniques in cyber-physical systems facilitate building smart warehouses to achieve the promising vision of industry 4.0. We focus on four significant issues when applying CPS techniques in smart warehouses. First, efficient CPS data collection: when limited communication bandwidth meets numerous CPS devices, we need to make more effort to study efficient wireless communication scheduling strategies. Second, accurate and robust localization: localization is the basis for many fundamental operations in smart warehouses, but still needs to be improved from various aspects like accuracy and robustness. Third, human activity recognition: human activity recognition can be applied in human–computer interaction for remote machine operations. Fourth, multi-robot collaboration: smart robots will take the place of humans to accomplish most tasks particularly in a harsh environment, and smart and fully-distributed robot collaborating algorithms should be investigated. Finally, we point out some challenging issues in the future CPS-based smart warehouse, which could open some new research directions.

This paper studies the problem of tag localization using RFID-augmented robots, which is practically important for promising warehousing applications, e.g., automatic item fetching and misplacement detection. Existing RFID localization systems suffer from one or more of following limitations: requiring specialized devices; only 2D localization is enabled; having blind zone for mobile localization; low scalability. In this paper, we use Commercial Off-The-Shelf (COTS) robot and RFID devices to implement a Mobile RF-robot Localization (MRL) system. Specifically, when the RFID-augmented robot moves along the straight aisle in a warehouse, the reader keeps reading the target tag via two vertically deployed antennas ( $\mathcal {R}_1$ and $\mathcal {R}_2$ ) and returns the tag phase data with timestamps to the server. We take three points in the phase profile of antenna $\mathcal {R}_1$ and leverage the spatial and temporal changes inherent in this phase triad to construct an equation set. By solving it, we achieve the location of target tag relative to the trajectory of antenna $\mathcal {R}_1$ . Based on different phase triads, we can have candidate locations of the target tag with different accuracy. Then, we propose theoretical analysis to quantify the deviation of each localization result. A fine-grained localization result can be achieved by assigning larger weights to the localization results with smaller deviations. Similarly, we can also calculate the relative location of target tag with respect to the trajectory of antenna $\mathcal {R}_2$ . Leveraging the geometric relationships among target tag and antenna trajectories, we eventually calculate the location of target tag in 3D space. We perform various experiments to evaluate the performance of the MRL system and results show that the proposed MRL system can achieve high accuracy in both 2D and 3D localization.

Wi-Fi CSI-based indoor localization systems can realize decimeter-level localization accuracy. However, these systems require that the location and antenna array orientation of Wi-Fi Access Point (AP) are known in advance, which makes it impractical for large-scale deployment. In this paper, we present MapFi, which can realize autonomous mapping of Wi-Fi infrastructure without labor-intensive site survey. To this end, we focus on addressing three problems. First, as there will be diverse layouts of devices and antennas with respective to numerous and heterogeneous Wi-Fi APs, we propose a general method to estimate AoA and generate the Wi-Fi map. Second, while the existing systems can provide a promising median localization accuracy, tail performance is usually far worse. Consequently, we develop a revision method to reduce tail errors. Third, when deployed in large-scale indoor environment, obstacles and long-distance communication might incur failed CSI collection. Therefore, we segment Wi-Fi APs into groups and finally merge these groups to generate Wi-Fi map. We conduct experiments in different scenarios to verify proposed methods. The experimental results show that we can realize the 80% localization error within 1.15m and 0.74m in office room and open space respectively, which is as accurate as localization systems requiring known Wi-Fi map.

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 nłl 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 2x faster than the state-of-the-art RFID sensory data collection protocols.

Sleep posture recognition is practically important in various scenarios such as sleep healthcare, bedridden patient care, and chronic disease diagnosis. With concerns of user privacy preserving, we prefer the wireless sensing methods to computer vision methods when dealing with sleep posture recognition. However, the existing wireless sensing methods suffer from at least one of the following major limitations: (i) difficult to deploy in practice; (ii) few posture categories; (iii) insufficient accuracy; (iv) poor generalization ability. In this paper, we use commercial-off-the-shelf (COTS) mmWave radar to implement a sleep posture recognition system called PosMonitor. When designing the PosMonitor system, we need to address the following challenging issues. First, we propose an angle purification method based on multi-frame joint analysis to alleviate the sparsity and instability of the point cloud. Then, we endow the point cloud with respiratory features to enhance its representation of the sleep posture. Further, to make the system applicable to different users, we extract relative respiratory features by normalization to overcome individual differences. Extensive experimental results show that our PosMonitor system can achieve 98% accuracy on average in recognizing 6 typical sleep postures and has good reliability across different conditions.

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.