Advances in analytical models and applications for RFID, WSN and AmI systems

Experimentos llevados a cabo con el equipo de división de honor UCAM Volleyball Murcia.[SPA] Internet de las cosas (IoT) integra distintos elementos que actúan tanto como fuentes, como sumideros de información, a diferencia de la percepción que se ha tenido hasta ahora de Internet, centrado en las personas. Los avances en IoT engloban un amplio número de áreas y tecnologías, desde la adquisición de información hasta el desarrollo de nuevos protocolos y aplicaciones. Un concepto clave que subyace en el concepto de IoT, es el procesamiento de forma inteligente y autónoma de los flujos de información que se dispone. En este trabajo, estudiamos tres aspectos diferentes de IoT. En primer lugar, nos centraremos en la infraestructura de obtención de datos. Entre las diferentes tecnologías de obtención de datos disponibles en los sistemas IoT, la Identificación por Radio Frecuencia (RFID) es considerada como una de las tecnologías predominantes. RFID es la tecnología detrás de aplicaciones tales como control de acceso, seguimiento y rastreo de contenedores, gestión de archivos, clasificación de equipaje o localización de equipos. Con el auge de la tecnología RFID, muchas instalaciones empiezan a requerir la presencia de múltiples lectores RFID que operan próximos entre sí y conjuntamente. A estos escenarios se les conoce como dense reader environments (DREs). La coexistencia de varios lectores operando simultáneamente puede causar graves problemas de interferencias en el proceso de identificación. Uno de los aspectos claves a resolver en los RFID DREs consiste en lograr la coordinación entre los lectores. Estos problemas de coordinación son tratados en detalle en esta tesis doctoral. Además, dentro del área de obtención de datos relativa a IoT, las Redes de Sensores Inalámbricas (WSNs) desempeñan un papel fundamental. Durante la última década, las WSNs han sido estudiadas ampliamente de forma teórica, y la mayoría de problemas relacionados con la comunicación en este tipo de redes se han conseguido resolver de forma favorable. Sin embargo, con la implementación de WSNs en proyectos reales, han surgido nuevos problemas, siendo uno de ellos el desarrollo de estrategias realistas para desplegar las WSN. En este trabajo se estudian diferentes métodos que resuelven este problema, centrándonos en distintos criterios de optimización, y analizando las diferentes ventajas e inconvenientes que se producen al buscar una solución equilibrada. Por último, la Inteligencia Ambiental (AmI) forma parte del desarrollo de aplicaciones inteligentes en IoT. Hasta ahora, han sido las personas quienes han tenido que adaptarse al entorno, en cambio, AmI persigue crear entornos de obtención de datos capaces de anticipar y apoyar las acciones de las personas. AmI se está introduciendo progresivamente en diversos entornos reales tales como el sector de la educación y la salud, en viviendas, etc. En esta tesis se introduce un sistema AmI orientado al deporte que busca mejorar el entrenamiento de los atletas, siendo el objetivo prioritario el desarrollo de un asistente capaz de proporcionar órdenes de entrenamiento, basadas tanto en el entorno como en el rendimiento de los atletas. [ENG] Internet of Things (IoT) is being built upon many different elements acting as sources and sinks of information, rather than the previous human-centric Internet conception. Developments in IoT include a vast set of fields ranging from data sensing, to development of new protocols and applications. Indeed, a key concept underlying in the conception of IoT is the smart and autonomous processing of the new huge data flows available. In this work, we aim to study three different aspects within IoT. First, we will focus on the sensing infrastructure. Among the different kind of sensing technologies available to IoT systems, Radio Frequency Identification (RFID) is widely considered one of the leading technologies. RFID is the enabling technology behind applications such as access control, tracking and tracing of containers, file management, baggage sorting or equipment location. With the grow up of RFID, many facilities require multiple RFID readers usually operating close to each other. These are known as Dense Reader Environments (DREs). The co-existence of several readers operating concurrently is known to cause severe interferences on the identification process. One of the key aspects to solve in RFID DREs is achieving proper coordination among readers. This is the focus of the first part of this doctoral thesis. Unlike previous works based on heuristics, we address this problem through an optimization-based approach. The goal is identifying the maximum mean number of tags while network constraints are met. To be able to formulate these optimization problems, we have obtained analytically the mean number of identifications in a bounded -discrete or continuous- time period, an additional novel contribution of our work. Results show that our approach is overwhelmingly better than previous known methods. Along sensing technologies of IoT, Wireless Sensor Networks (WSNs) plays a fundamental role. WSNs have been largely and theoretically studied in the past decade, and many of their initial problems related to communication aspects have been successfully solved. However, with the adoption of WSNs in real-life projects, new issues have arisen, being one of them the development of realistic strategies to deploy WSNs. We have studied different ways of solving this aspect by focusing on different optimality criteria and evaluating the different trade-offs that occur when a balanced solution must be selected. On the one hand, deterministic placements subject to conflicting goals have been addressed. Results can be obtained in the form of Pareto-frontiers, allowing proper solution selection. On the other hand, a number of situations correspond to deployments were the nodes¿ position is inherently random. We have analyzed these situations leading first to a theoretical model, which later has been particularized to a Moon WSN survey. Our work is the first considering a full model with realistic properties such as 3D topography, propellant consumptions or network lifetime and mass limitations. Furthermore, development of smart applications within IoT is the focus of the Ambient Intelligence (AmI) field. Rather than having people adapting to the surrounding environment, AmI pursues the development of sensitive environments able to anticipate support in people¿s actions. AmI is progressively being introduced in many real-life environments like education, homes, health and so forth. In this thesis we develop a sport-oriented AmI system designed to improve athletes training. The goal is developing an assistant able to provide real-time training orders based on both environment and athletes¿ biometry, which is aimed to control the aerobic and the technical-tactical training. Validation experiments with the honor league UCAM Volleyball Murcia team have shown the suitability of this approach.[ENG] Internet of Things (IoT) is being built upon many different elements acting as sources and sinks of information, rather than the previous human-centric Internet conception. Developments in IoT include a vast set of fields ranging from data sensing, to development of new protocols and applications. Indeed, a key concept underlying in the conception of IoT is the smart and autonomous processing of the new huge data flows available. In this work, we aim to study three different aspects within IoT. First, we will focus on the sensing infrastructure. Among the different kind of sensing technologies available to IoT systems, Radio Frequency Identification (RFID) is widely considered one of the leading technologies. RFID is the enabling technology behind applications such as access control, tracking and tracing of containers, file management, baggage sorting or equipment location. With the grow up of RFID, many facilities require multiple RFID readers usually operating close to each other. These are known as Dense Reader Environments (DREs). The co-existence of several readers operating concurrently is known to cause severe interferences on the identification process. One of the key aspects to solve in RFID DREs is achieving proper coordination among readers. This is the focus of the first part of this doctoral thesis. Unlike previous works based on heuristics, we address this problem through an optimization-based approach. The goal is identifying the maximum mean number of tags while network constraints are met. To be able to formulate these optimization problems, we have obtained analytically the mean number of identifications in a bounded -discrete or continuous- time period, an additional novel contribution of our work. Results show that our approach is overwhelmingly better than previous known methods. Along sensing technologies of IoT, Wireless Sensor Networks (WSNs) plays a fundamental role. WSNs have been largely and theoretically studied in the past decade, and many of their initial problems related to communication aspects have been successfully solved. However, with the adoption of WSNs in real-life projects, new issues have arisen, being one of them the development of realistic strategies to deploy WSNs. We have studied different ways of solving this aspect by focusing on different optimality criteria and evaluating the different trade-offs that occur when a balanced solution must be selected. On the one hand, deterministic placements subject to conflicting goals have been addressed. Results can be obtained in the form of Pareto-frontiers, allowing proper solution selection. On the other hand, a number of situations correspond to deployments were the nodes¿ position is inherently random. We have analyzed these situations leading first to a theoretical model, which later has been particularized to a Moon WSN survey. Our work is the first considering a full model with realistic properties such as 3D topography, propellant consumptions or network lifetime and mass limitations. Furthermore, development of smart applications within IoT is the focus of the Ambient Intelligence (AmI) field. Rather than having people adapting to the surrounding environment, AmI pursues the development of sensitive environments able to anticipate support in people¿s actions. AmI is progressively being introduced in many real-life environments like education, homes, health and so forth. In this thesis we develop a sport-oriented AmI system designed to improve athletes training. The goal is developing an assistant able to provide real-time training orders based on both environment and athletes¿ biometry, which is aimed to control the aerobic and the technical-tactical training. Validation experiments with the honor league UCAM Volleyball Murcia team have shown the suitability of this approach.Universidad Politécnica de CartagenaPrograma de doctorado en Tecnología de la Información y de las Comunicacione

Versatility Of Low-Power Wide-Area Network Applications

Low-Power Wide-Area Network (LPWAN) is regarded as the leading communication technology for wide-area Internet-of-Things (IoT) applications. It offers low-power, long-range, and low-cost communication. With different communication requirements for varying IoT applications, many competing LPWAN technologies operating in both licensed (e.g., NB-IoT, LTE-M, and 5G) and unlicensed (e.g., LoRa and SigFox) bands have emerged. LPWANs are designed to support applications with low-power and low data rate operations. They are not well-designed to host applications that involve high mobility, high traffic, or real-time communication (e.g., volcano monitoring and control applications).With the increasing number of mobile devices in many IoT domains (e.g., agricultural IoT and smart city), mobility support is not well-addressed in LPWAN. Cellular-based/licensed LPWAN relies on the wired infrastructure to enable mobility. On the other hand, most unlicensed LPWANs operate on the crowded ISM band or are required to duty cycle, making handling mobility a challenge. In this dissertation, we first identify the key opportunities of LPWAN, highlight the challenges, and show potential directions for future research. We then enable the versatility of LPWAN applications first by enabling applications involving mobility over LPWAN. Specifically, we propose to handle mobility in LPWAN over white space considering Sensor Network Over White Space (SNOW). SNOW is a highly scalable and energy-efficient LPWAN operating over the TV white spaces. TV white spaces are the allocated but locally unused available TV channels (54 - 698 MHz in the US). We proposed a dynamic Carrier Frequency Offset (CFO) estimation and compensation technique that considers the impact of the Doppler shift due to mobility. Also, we design energy-efficient and fast BS discovery and association approaches. Finally, we demonstrate the feasibility of our approach through experiments in different deployments. Finally, we present a collision detection and recovery technique called RnR (Reverse & Replace Decoding) that applies to LPWANs. Additionally, we discuss future work to enable handling burst transmission over LPWAN and localization in mobile LPWAN

Designing Intelligent Energy Efficient Scheduling Algorithm To Support Massive IoT Communication In LoRa Networks

We are about to enter a new world with sixth sense ability – “Network as a sensor -6G”. The driving force behind digital sensing abilities is IoT. Due to their capacity to work in high frequency, 6G devices have voracious energy demand. Hence there is a growing need to work on green solutions to support the underlying 6G network by making it more energy efficient. Low cost, low energy, and long-range communication capability make LoRa the most adopted and promising network for IoT devices. Since LoRaWAN uses ALOHA for multi-access of channels, collision management is an important task. Moreover, in massive IoT, due to the increased number of devices and their Adhoc transmissions, collision becomes and concern. Furthermore, in long-range communication, such as in forests, agriculture, and remote locations, the IoT devices need to be powered using a battery and cannot be attached to an energy grid. LoRaWAN originally has a star network wherein IoT devices communicated to a single gateway. Massive IoT causes increased traffic at a single gateway. To address Massive IoT issues of collision and gateway load handling, we have designed a reinforcement learning-based scheduling algorithm, a Deep Deterministic policy gradient algorithm with channel activity detection (CAD) to optimize the energy efficiency of LoRaWAN in cross-layer architecture in massive IoT with star topology. We also design a CAD-based simulator for evaluating any algorithms with channel sensing. We compare energy efficiency, packet delivery ratio, latency, and signal strength with existing state of art algorithms and prove that our proposed solution is efficient for massive IoT LoRaWAN with star topology

A survey of RFID readers anticollision protocols

International audienceWhile RFID technology is gaining increased attention from industrial community deploying different RFID-based applications, it still suffers from reading collisions. As such, many proposals were made by the scientific community to try and alleviate that issue using different techniques either centralized or distributed, monochannel or multichannels, TDMA or CSMA. However, the wide range of solutions and their diversity make it hard to have a clear and fair overview of the different works. This paper surveys the most relevant and recent known state-of-the-art anti-collision for RFID protocols. It provides a classification and performance evaluation taking into consideration different criteria as well as a guide to choose the best protocol for given applications depending on their constraints or requirements but also in regard to their deployment environments