CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies <s> Accurate and Robust Localization <s> Growing convergence among mobile computing devices and embedded technology sparks the development and deployment of “context-aware” applications, where location is the most essential context. In this paper we present LANDMARC, a location sensing prototype system that uses Radio Frequency Identification (RFID) technology for locating objects inside buildings. The major advantage of LANDMARC is that it improves the overall accuracy of locating objects by utilizing the concept of reference tags. Based on experimental analysis, we demonstrate that active RFID is a viable and cost-effective candidate for indoor location sensing. Although RFID is not designed for indoor location sensing, we point out three major features that should be added to make RFID technologies competitive in this new and growing market. <s> BIB001 </s> CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies <s> Accurate and Robust Localization <s> In this paper we present new measurement results for an Angle of Arrival (AoA) approach to localize RFID tags at 868 MHz. Self-designed three element antenna arrays, off-the-shelf IDS R901G RFID reader ICs, and UPM Raflatec DogBone RFID tags are used to generate and detect ISO 18000–6C compliant signals. The experimental setup comprises three antenna arrays and a test environment of size 3 times 3 square meters with 25 test points. The AoA is estimated using the phase differences in the complex baseband signals of adjacent antenna elements within one array. A mean positioning error of 0.21 m was achieved for the considered test grid. <s> BIB002 </s> CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies <s> Accurate and Robust Localization <s> In many applications, we have to identify an object and then locate the object to within high precision (centimeter- or millimeter-level). Legacy systems that can provide such accuracy are either expensive or suffering from performance degradation resulting from various impacts, e.g., occlusion for computer vision based approaches. In this work, we present an RFID-based system, Tagoram, for object localization and tracking using COTS RFID tags and readers. Tracking mobile RFID tags in real time has been a daunting task, especially challenging for achieving high precision. Our system achieves these three goals by leveraging the phase value of the backscattered signal, provided by the COTS RFID readers, to estimate the location of the object. In Tagoram, we exploit the tag's mobility to build a virtual antenna array by using readings from a few physical antennas over a time window. To illustrate the basic idea of our system, we firstly focus on a simple scenario where the tag is moving along a fixed track known to the system. We propose Differential Augmented Hologram (DAH) which will facilitate the instant tracking of the mobile RFID tag to a high precision. We then devise a comprehensive solution to accurately recover the tag's moving trajectories and its locations, relaxing the assumption of knowing tag's track function in advance. We have implemented the Tagoram system using COTS RFID tags and readers. The system has been tested extensively in the lab environment and used for more than a year in real airline applications. For lab environment, we can track the mobile tags in real time with a millimeter accuracy to a median of 5mm and 7.29mm using linear and circular track respectively. In our year- long large scale baggage sortation systems deployed in two airports, our results from real deployments show that Tagoram can achieve a centimeter-level accuracy to a median of 6.35cm in these real deployments. <s> BIB003 </s> CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies <s> Accurate and Robust Localization <s> Many object localization applications need the relative locations of a set of objects as oppose to their absolute locations. Although many schemes for object localization using Radio Frequency Identification (RFID) tags have been proposed, they mostly focus on absolute object localization and are not suitable for relative object localization because of large error margins and the special hardware that they require. In this paper, we propose an approach called Spatial-Temporal Phase Profiling (STPP) to RFID based relative object localization. The basic idea of STPP is that by moving a reader over a set of tags during which the reader continuously interrogating the tags, for each tag, the reader obtains a sequence of RF phase values, which we call a phase profile, from the tag's responses over time. By analyzing the spatial-temporal dynamics in the phase profiles, STPP can calculate the spatial ordering among the tags. In comparison with prior absolute object localization schemes, STPP requires neither dedicated infrastructure nor special hardware. We implemented STPP and evaluated its performance in two real-world applications: locating misplaced books in a library and determining baggage order in an airport. The experimental results show that STPP achieves about 84% ordering accuracy for misplaced books and 95% ordering accuracy for baggage handling. <s> BIB004
Accurate object localization benefits various context-aware applications in the era of Industry 4.0, e.g., automatic product tracking and sorting. Currently, there are various localization techniques such as GPS, Wi-Fi, Bluetooth, and RFID. For large-scale warehouse in future Industry 4.0, RFID has attractive advantages over the other techniques. GPS can work well for the outdoor environment (Google Maps, driving navigation) but does not work for the indoor environment. Wi-Fi can pinpoint the location of the human that takes a smartphone. However, it will be nearly impossible to affix each product with a phone. Bluetooth requires each product has a battery-powered beacon device. The limited battery volume severely shortens the lifetime of the localization services. Some other technologies are rising for localization and human activity recognition such as 5G. However, 5G is still on its way and cannot be accessed ubiquitously. Therefore, the relating technology for localization and human activity recognition are not well developed. Compared with the above techniques, passive RFID tags are low-cost (five cents for each tag), small-size (as thin as a paper), and battery-free (almost infinite lifetime), which make RFID more suitable for localization in Industry 4.0. The existing RFID localization approaches can be classified into two types: RSS-based approaches (received signal strength), and phase-based approaches. In the infancy of the RFID localization study, Ni et al. exploited the RSS value of RFID tags to implement a localization system called LANDMARC BIB001 . The basic working principle of LANDMARC is as follows. The manager of a warehouse deploys many reference tags in the surveillance region in prior. As a matter of fact, RSS value closely depends on the distance between tag and reader antenna. Thus, RSS values of the nearby tags should also be similar. The RFID readers keep interrogating the concerned tags as well as the reference tags. For each tag reading, we can obtain not only the tag IDs but also the RSS. Then, we find out k reference tags whose RSS values are the closest to that of the concerned tag and use the weighted average of the locations of these k reference tags as the location of the target tag. However, RSS is an unreliable location indicator, especially for UHF tags BIB004 . Recently, the researchers shifted attention to use RF phase information when addressing the problem of RFID localization. The Angle of Arrival (AOA) based methods make use of multiple reader antenna arrays BIB002 . For one antenna array, the phase difference is used to measure the angle between the target tag and this antenna array plane. Using two antenna arrays, we can draw two lines based on the measured angles, and the intersection point is expected to be the tag location. Wang et al. implemented a localization system called PinIt  , which is based on comparing the multi-path effects instead of RSS. Specifically, they use a moving antenna to measure the multi-path profiles of the reference tags at known positions in advance. Then, PinIt locates the target tag according to the insight that nearby tags have similar multi-path effects. For the anonymous RFID system, PinIt is hard to use because we know nothing about the reference tags. Yang et al. proposed the location tracking system called Differential Augmented Hologram (DAH) BIB003 , which exploits the target tag's mobility to construct a virtual antenna array by using readings from a few physical antennas over time. They further proposed a comprehensive solution to accurately recover the tag's moving trajectories and its locations, relaxing the assumption of knowing tag's track function in advance. DAH partitions the monitoring region into grids, and use the collected phase profile to calculate the probability that the target tag locates in each grid. The grid with the highest probability is treated as the location of the target tag. The experimental results reveal that DAH can achieve cm-level accuracy. However, DAH requires precise calibration of the reader antennas. More seriously, for the large surveillance region, DAH needs to deploy multiple sets of reader antennas, which costs a lot. However, RF-Scanner begets high overhead regarding computational complexity, and thus cannot satisfy the real-time requirement for tag-dense RFID systems. To evaluate the existing RFID localization systems, we need to concern with four metrics. Availability: whether the localization system only consists of the Commercial Off-The-Shelf (COTS) devices, and the proposed localization algorithms are only installed as software modules. Mobility: whether a single localization system could be able to cover a vast region in a mobile manner to save cost. Reliability: whether the system could provide fine-grained localization accuracy (i.e., cm-level) with high confidence. Scalability: whether the computational complexity of the RFID localization algorithms is quite low so that it is scalable to a large number of tags. In Table 1 , we have summarized the pros and cons of the existing RFID localization systems. In terms computational complexity, we have to explain the involved notations. x represents the number of tag replies received by the reader; ξ is the number of reference reader antennas; and γ is a factor that is determined by localization granularity. From this table, we can easily find that the DAH and RF-Scanner can provide the most accurate localization results. Compared with DAH, RF-Scanner is more suitable for the large-scale environment. 