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

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Diseño de un robot móvil autónomo de telepresencia

The recent rise in tele-operated autonomous mobile vehicles calls for a seamless control architecture that reduces the learning curve when the platform is functioning autonomously (without active supervisory control), as well as when tele-operated. Conventional robot plat-forms usually solve one of two problems. This work develops a mobile base using the Robot Operating System (ROS) middleware for teleoperation at low cost. The three-layer architec-ture introduced adds or removes operator complexity. The lowest layer provides mobility and robot awareness; the second layer provides usability; the upper layer provides inter-activity. A novel interactive control that combines operator intelligence/ skill with robot/autonomous intelligence enabling the mobile base to respond to expected events and ac-tively react to unexpected events is presented. The experiments conducted in the robot laboratory summarises the advantages of using such a system.El reciente auge de los vehículos móviles autónomos teleoperados exige una arquitectura de control sin fisuras que reduzca la curva de aprendizaje cuando la plataforma funciona de forma autónoma (sin control de supervisión activo), así como cuando es teleoperada. Las plataformas robóticas convencionales suelen resolver uno de los dos problemas. Este tra-bajo desarrolla una base móvil que utiliza el middleware Robot Operating System (ROS) para la teleoperación a bajo coste. La arquitectura de tres capas introducida añade o elimina la complejidad del operador. La capa más baja proporciona movilidad y conciencia robótica; la segunda capa proporciona usabilidad; la capa superior proporciona interactividad. Se presenta un novedoso control interactivo que combina la inteligencia/habilidades del op-erador con la inteligencia autónoma del robot, lo que permite que la base móvil responda a los eventos esperados y reaccione activamente a los eventos inesperados. Los experi-mentos realizados en el laboratorio robótica resumen las ventajas de utilizar un sistema de este tipoDepartamento de Ingeniería de Sistemas y AutomáticaMáster en Electrónica Industrial y Automátic

Object Localization and Tracking in 3D

The field of Computer Vision has repeatedly been recognized as an intellectual frontier whose boundaries of applicability are yet to be stipulated. The work attempts to demonstrate that vision can achieve an automatic localization and tracking of targets in a 3D space. Localization of targets has gained importance in the recent past due to the myriad of applications it plays a significant role in. It is analogous to detection of objects in a video sequence in the image processing domain. This work aims to localize a target based on range measurements obtained using a network of sensors scattered in the 3D continuum. To this end, the use of the biologically inspired particle swarm optimization(PSO) algorithm is motivated. In this context, a novel modification of PSO algorithm is proposed that leads to faster convergence, and eliminates the ip ambiguity encountered by coplanar sensors. The initial results over several simulation runs highlight the accuracy and speed of the proposed approach

Software for Embedded Module for Image Processing

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Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

Infrastructure Wi-Fi for connected autonomous vehicle positioning : a review of the state-of-the-art

In order to realize intelligent vehicular transport networks and self driving cars, connected autonomous vehicles (CAVs) are required to be able to estimate their position to the nearest centimeter. Traditional positioning in CAVs is realized by using a global navigation satellite system (GNSS) such as global positioning system (GPS) or by fusing weighted location parameters from a GNSS with an inertial navigation systems (INSs). In urban environments where Wi-Fi coverage is ubiquitous and GNSS signals experience signal blockage, multipath or non line-of-sight (NLOS) propagation, enterprise or carrier-grade Wi-Fi networks can be opportunistically used for localization or “fused” with GNSS to improve the localization accuracy and precision. While GNSS-free localization systems are in the literature, a survey of vehicle localization from the perspective of a Wi-Fi anchor/infrastructure is limited. Consequently, this review seeks to investigate recent technological advances relating to positioning techniques between an ego vehicle and a vehicular network infrastructure. Also discussed in this paper is an analysis of the location accuracy, complexity and applicability of surveyed literature with respect to intelligent transportation system requirements for CAVs. It is envisaged that hybrid vehicular localization systems will enable pervasive localization services for CAVs as they travel through urban canyons, dense foliage or multi-story car parks