RFID Localisation For Internet Of Things Smart Homes: A Survey

The Internet of Things (IoT) enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. The IoT incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. Localisation in indoor positioning systems plays an important role in the IoT. Location Based IoT applications range from tracking objects and people in real-time, assets management, agriculture, assisted monitoring technologies for healthcare, and smart homes, to name a few. Radio Frequency based systems for indoor positioning such as Radio Frequency Identification (RFID) is a key enabler technology for the IoT due to its costeffective, high readability rates, automatic identification and, importantly, its energy efficiency characteristic. This paper reviews the state-of-the-art RFID technologies in IoT Smart Homes applications. It presents several comparable studies of RFID based projects in smart homes and discusses the applications, techniques, algorithms, and challenges of adopting RFID technologies in IoT smart home systems.Comment: 18 pages, 2 figures, 3 table

Performance Evaluation of a UWB Positioning System Applied to Static and Mobile Use Cases in Industrial Scenarios

Indoor positioning systems are essential in the industrial domain for optimized production and safe operation of mobile elements, such as mobile robots, especially in the presence of static machinery and human operators. In this paper, we assess the performance of a commercial UWB radio-based positioning system deployed in a realistic industrial scenario, considering both static and mobile use cases. Our goal is to characterize the accuracy of this system in the context of industrial use cases and applications. For the static case, an extensive analysis was presented based on measurements performed at 72 measurement positions at 3 different heights (above, at similar a level to, and below the average clutter level) in different industrial clutter conditions (open and cluttered spaces). The extensive analysis in the mobile case considered several runs of a route covered by an autonomous mobile robot equipped with multiple tags in different positions. The results indicate that a similar degree of accuracy with a median 2D positioning error smaller than 20 cm is possible in both static and mobile conditions with an optimized anchor deployment. The paper provides a complete statistical characterization of the system’s accuracy and addresses the multiple deployment trade-offs and system dynamics observed for the different configurations

Evaluation and Comparison of Ultrasonic and UWB Technology for Indoor Localization in an Industrial Environment

Evaluations of different technologies and solutions for indoor localization exist but only a few are aimed at the industrial context. In this paper, we compare and analyze two prominent solutions based on Ultra Wide Band Radio (Pozyx) and Ultrasound (GoT), both installed in an industrial manufacturing laboratory. The comparison comprises a static and a dynamic case. The static case evaluates average localization errors over 90 s intervals for 100 ground-truth points at three different heights, corresponding to different relevant objects in an industrial environment: mobile robots, pallets, forklifts and worker helmets. The average error obtained across the laboratory is similar for both systems and is between 0.3 m and 0.6 m, with higher errors for low altitudes. The dynamic case is performed with a mobile robot travelling with an average speed of 0.5 m/s at a height of 0.3 m. In this case, low frequency error components are filtered out to focus the comparison on dynamic errors. Average dynamic errors are within 0.3–0.4 m for Pozyx and within 0.1–0.2 m for GoT. Results show an acceptable accuracy required for tracking people or objects and could serve as a guideline for the least achievable accuracy when applied for mobile robotics in conjunction with other elements of a robotic navigation stack