Light-weight integration and interoperation of localization systems in IoT

As the ideas and technologies behind the Internet of Things (IoT) take root, a vast array of new possibilities and applications is emerging with the significantly increased number of devices connected to the Internet. Moreover, we are also witnessing the fast emergence of location-based services with an abundant number of localization technologies and solutions with varying capabilities and limitations. We believe that, at this moment in time, the successful integration of these two diverse technologies is mutually beneficial and even essential for both fields. IoT is one of the major fields that can benefit from localization services, and so, the integration of localization systems in the IoT ecosystem would enable numerous new IoT applications. Further, the use of standardized IoT architectures, interaction and information models will permit multiple localization systems to communicate and interoperate with each other in order to obtain better context information and resolve positioning errors or conflicts. Therefore, in this work, we investigate the semantic interoperation and integration of positioning systems in order to obtain the full potential of the localization ecosystem in the context of IoT. Additionally, we also validate the proposed design by means of an industrial case study, which targets fully-automated warehouses utilizing location-aware and interconnected IoT products and systems

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

TDoA-based outdoor positioning in a public LoRa network

The performance of LoRa Geo-location for outdoor tracking purposes has been evaluated on a public LoRaWAN network. Time Difference of Arrival (TDOA) localization accuracy, probability and update frequency were evaluated for different trajectories (walking, cycling, driving) and LoRa spreading factors. A median accuracy of 200m was obtained and in 90% of the cases the error was less then 480m

ReLoc: Hybrid RSSI- and phase-based relative UHF-RFID tag localization with COTS devices

Radio frequency identification (RFID) technology brings tremendous advancements in the Industrial Internet of Things (IIoT), especially for smart inventory management, as it provides a fast and low-cost way of counting or positioning items in the warehouse. In the last decade, many novel solutions, including absolute and relative positioning methods, have been proposed for this application. However, the available methods are quite sensitive to the minor changes in the deployment scenario, including the orientation of the tag and antenna, the materials contained inside the carton, tag distortion, and multipath propagation. To this end, we propose a hybrid relative passive RFID localization method (ReLoc) based on both the received signal strength indicator (RSSI) and measured phases, which orders the RFID tags horizontally and vertically. In this article, the phase-based variant maximum likelihood estimation is proposed for lateral positioning, and the RSSI profiles of two tilted antennas are compared with each other for level distinguishing. We implement the proposed positioning system ReLoc with commercial off-the-shelf RFID devices. The experiment in a warehouse shows that ReLoc is a powerful solution for practical item-level inventory management. The experimental results show that ReLoc achieves an average lateral and level ordering accuracy of 94.6% and 94.3%, respectively. Notably, when considering liquid or metal materials inside the carton or tag distortion, ReLoc still performs excellently with more than 93% ordering accuracy both horizontally and vertically, indicating the robustness of the proposed method

Performance comparison of RSS algorithms for indoor localization in large open environments

We develop and benchmark four RSS localisation algorithms where different a priori knowledge is required. The selection of the best algorithm depends on the availability of additional information on path loss exponent and/or transmit power. We compare our algorithms with centroid localization and show that the algorithms provide better results for shadowing on the values not exceeding 6dB. We perform experiments and simulations with Bluetooth Low Energy and LoRaWAN technologies and select the best technology and algorithm for localisation in large open industrial environments

Phase-based variant maximum likelihood positioning for passive UHF-RFID tags

Radio frequency identification (MD) technology brings tremendous advancement in Internet-of-Things, especially in supply chain and smart inventory management. Phase-based passive ultra high frequency RFID tag localization has attracted great interest, due to its insensitivity to the propagation environment and tagged object properties compared with the signal strength based method. In this paper, a phase-based maximum-likelihood tag positioning estimation is proposed. To mitigate the phase uncertainty, the likelihood function is reconstructed through trigonometric transformation. Weights are constructed to reduce the impact of unexpected interference and to augment the positioning performance. The experiment results show that the proposed algorithms realize line-grained tag localization, which achieve centimeter-level lateral accuracy, and less than 15-centimeters vertical accuracy along the altitude of the racks

TDoA-Based Outdoor Positioning with Tracking Algorithm in a Public LoRa Network

The performance of LoRa geolocation for outdoor tracking purposes has been investigated on a public LoRaWAN network. Time Difference of Arrival (TDoA) localization accuracy, update probability, and update frequency were evaluated for different trajectories (walking, cycling, and driving) and LoRa spreading factors. A median accuracy of 200 m was obtained for the raw TDoA output data. In 90% of the cases, the error was less than 480 m. Taking into account the road map and movement speed significantly improves accuracy to a median of 75 m and a 90th percentile error of less than 180 m

ReLoc 2.0 : UHF-RFID relative localization for drone-based inventory management

Radio frequency identification (RFID) technology attracts extensive attention for industrial applications, especially supply chain businesses and inventory management. In this article, a passive RFID localization scheme based on unmanned aerial vehicles (UAVs) or drones is established for inventory management in warehouses. In case of the major challenge of the on-board antenna tracking errors in practical scenarios, the state-of-the-art (SOTA) numerical methods rely on accurate antenna positions, such as synthetic aperture radar (SAR)-based algorithms, which will introduce large positioning errors and become unreliable. To this end, we propose a new relative RFID localization method based on phase, received signal strength indicator (RSSI), and readability, which is little affected by antenna tracking errors. In the proposed method, the tagged items on the racks are located laterally through pinpointing the minimum of the unwrapped phases, which have been unwrapped based on the hybrid random forest (RF) model. RSSI differences and the read count of each patch antenna's beam are utilized to distinguish the rack level of tagged assets via a boosting tree classifier. The experimental results in a real warehouse show that the proposed method achieves 27.1-cm mean lateral positioning accuracy and orders the tagged items horizontally and vertically with more than 96.7% and 98.0% accuracy, respectively