IoT for measurements and measurements for IoT

The thesis is framed in the broad strand of the Internet of Things, providing two parallel paths. On one hand, it deals with the identification of operational scenarios in which the IoT paradigm could be innovative and preferable to pre-existing solutions, discussing in detail a couple of applications. On the other hand, the thesis presents methodologies to assess the performance of technologies and related enabling protocols for IoT systems, focusing mainly on metrics and parameters related to the functioning of the physical layer of the systems

IoT-based management platform for real-time spectrum and energy optimization of broadcasting networks

We investigate the feasibility of Internet of Things (IoT) technology to monitor and improve the energy efficiency and spectrum usage efficiency of broadcasting networks in the Ultra-High Frequency (UHF) band. Traditional broadcasting networks are designed with a fixed radiated power to guarantee a certain service availability. However, excessive fading margins often lead to inefficient spectrum usage, higher interference, and power consumption. We present an IoT-based management platform capable of dynamically adjusting the broadcasting network radiated power according to the current propagation conditions. We assess the performance and benchmark two IoT solutions (i.e., LoRa and NB-IoT). By means of the IoT management platform the broadcasting network with adaptive radiated power reduces the power consumption by 15% to 16.3% and increases the spectrum usage efficiency by 32% to 35% (depending on the IoT platform). The IoT feedback loop power consumption represents less than 2% of the system power consumption. In addition, white space spectrum availability for secondary wireless telecommunications services is increased by 34% during 90% of the time

Big data analytics for large-scale wireless networks: Challenges and opportunities

© 2019 Association for Computing Machinery. The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large-scale wireless networks. Big data of large-scale wireless networks has the key features of wide variety, high volume, real-time velocity, and huge value leading to the unique research challenges that are different from existing computing systems. In this article, we present a survey of the state-of-art big data analytics (BDA) approaches for large-scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage, and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large-scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area

Cognitive radio architecture for massive internet of things services with dynamic spectrum access

En esta investigación se propone una arquitectura cognitiva para servicios masivos de Internet de las cosas sobre Huecos Espectrales en Televisión. La propuesta seleccionó la banda de frecuencia de TVWS como la mejor para enfrentar el reto de escasez de espectro radioeléctrico para servicios masivos de IoT. La arquitectura provee la lista de canales disponibles a dispositivos IoT y tiene restricciones de Calidad de servicio (QoS). Definimos un mecanismo de acceso novedoso que se basa en políticas regulatorias al interactuar con TVWS Geolocation Base de datos (GLDB) a través del Protocolo de acceso a espacios en blanco (PAWS) para proporcionar la lista de canales disponibles para dispositivos IoT. Con respecto a restricciones de QoS, exploramos diferentes tipos de implementaciones y referencias áreas de cobertura considerando un modelo de probabilidad de pérdida de paquetes. Además, la investigación describe el proceso de optimización para obtener la máxima área de servicio mientras se mantiene una probabilidad de interrupción por debajo de un objetivo dado. Además, aplicamos un mecanismo de macro-diversidad para mejorar la probabilidad de pérdida de paquetes con respecto a nuestra propuesta y una topología con un solo dispositivo maestro. Podemos evidenciar que la probabilidad promedio de pérdida de paquetes es reducido en 26% cuando la carga es igual al 80% en nuestra propuesta.IMT AtlantiqueUniversidad Santo TomásCEA-IoT , Pontificia Universidad JaverianaThis research proposes a novel cognitive radio architecture for massive Internet of Things (IoT) services over TV White Spaces (TVWS). The proposal considers TVWS as suitable frequency bands for facing the limited spectrum problem for massive IoT services. The architecture provides the available list of channels to IoT devices, and its access mechanisms have Quality of Service (QoS) constrains. We define a novel access mechanism that is based on regulatory policies by interacting with TVWS Geolocation Database (GLDB) through the Protocol to Access White-Space (PAWS) for providing the available list of channels to IoT devices. Regarding QoS constraints, we explore different types of deployments and reference coverage areas considering a packet loss probability model. In addition, the research describes the optimization process to obtain the maximum service area while maintaining an outage probability below a given objective. Moreover, we applied a macro-diversity mechanism for improving the packet loss probability with respect to our proposal and one Master Device (MD) topology. We can evidence that the average packet loss probability is reduced in 26% when the load is equal to 80% in our proposal.Doctor en IngenieríaDoctoradohttps://orcid.org/0000-0002-9579-678Xhttps://scholar.google.es/citations?user=-VX8bMEAAAAJ&hl=eshttp://scienti.colciencias.gov.co:8081/cvlac/visualizador/generarCurriculoCv.do?cod_rh=000084496

Leveraging TV white apace to monitor game conservation environments

A Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Mobile Telecommunications and Innovation (MSc. MTI)Installation of camera-traps by the conservancies has been gaining interest in the recent years here in Kenya. This is due to the increased scientific need to carry out wildlife research and also monitor the movement patterns of the wild game as a way of helping to address issues such as human-wildlife conflict and poaching. This is also gaining traction by the safari camps to enhance customer experience. The implementation of these camera-traps poses a limitation of remotely accessing the camera feeds. This is majorly caused by a challenge of connectivity as many of these game environments are located in rural environments of Kenya. The focus of this study was to find out and establish the best approach of implementing a camera-trap that allows remote access of feeds in the game environments while leveraging on the connectivity that can be provided through deployment of Television (TV) White Space network. Through the use of questionnaires, an online survey was conducted in a select conservancy and a safari camp to investigate the challenges and the technology state within these environments that limit the adoption of networked game cameras. Various secondary sources were also studied to understand the existing connectivity technologies in the realm of the Internet of Things (IoT). The study used a combination of hardware and software technologies in realising the model in a TV White Space environment. A networked game camera prototype that delivers video feeds on a remote mobile interface was developed. The camera prototype utilised a programmed Raspberry Pi camera and the System-On-Chip to relay the gathered feeds in real-time to the mobile interface. The mobile interface developed in this case was an Android-based mobile-web. This was tested by ordinary users in a Wi-Fi environment, TV White Space connectivity experts and conservation officers

IoT network : design and implementation

Dissertação para obtenção do grau de mestre em Engenharia Eletrónica e de TelecomunicaçõesIn recent years a new concept known in the anglo-saxonic language as IoT (Internet of Things) has gained prominence in the world of technology. IoT's main objective is to allow various types of physical objects, such as cars, houses and cities to transmit the information they obtain autonomously through sensors, to platforms that receive and use them intelligently, forming a network of interconnected objects, without any kind of human intervention. To understand this concept, a study was made of the networks that underlie this concept, LPWA (Low Power Wide Area Networks), and in more detail to LoRa technology. In order to estimate the coverage of this technology, a theoretical planning was performed using the OH model (Okumura-Hata), and based on the results obtained, an electromagnetic simulator (CloudRF), was used, which allowed to estimate in more detail the coverage in the area of study. In order to validate the results obtained theoretically and by simulation, a set of meas-urements was made in the field in some points of the city of Aveiro. From the global analysis of the obtained results, it was concluded that LoRa technology is in fact quite feasible to be used in an implementation of an IoT network in an urban environ-ment. The OH model when adapted with the appropriate coverage margins for the type of study environment allows a good approximation in terms of outdoor coverage. Despite being very sensitive to movements, it was possible to obtain distances up to 2 km in a mostly urban prop-agation environment, and more than 5 km in a more open area with a greater line of sight.Nos últimos anos um novo conceito conhecido na linguagem anglo-saxónica como IoT (In-ternet of Things) ganhou destaque no mundo da tecnologia. A IoT tem como principal objetivo permitir que diversos tipos de objetos físicos, como por exemplo carros, casas e cidades consi-gam transmitir a informação que obtêm de forma autónoma através de sensores, para platafor-mas que as recebem e as utilizam de forma inteligente, moldando assim uma rede de objetos interligados, sem existir qualquer tipo de intervenção humana. Para se perceber este conceito, foi efetuado um estudo às redes que servem de base a este conceito, as redes LPWA (Low Power Wide Area), e em mais detalhe à tecnologia LoRa. De forma a estimar a cobertura desta tecnologia, foi efetuado um planeamento teórico utilizando o modelo de OH (Okumura-Hata), e com base nos resultados obtidos, recorreu-se a um simulador electromagnético, o CloudRF, que permitiu estimar mais em detalhe a cobertura para a zona de Aveiro. De forma a validar os resultados obtidos teoricamente e por simulação, foi efetuado um conjunto de medidas em campo em alguns pontos da cidade de Aveiro. Da análise global de resultados obtidos, concluiu-se que a tecnologia LoRa é de facto bas-tante viável para ser utilizada numa implementação de uma rede IoT num ambiente urbano. O modelo de OH quando adaptado com as margens de cobertura adequadas para o tipo de ambi-ente em estudo permite obter uma boa aproximação em termos de cobertura outdoor. Apesar de ser bastante sensível a movimentações, a tecnologia LoRa através das medidas realizadas permitiu obter coberturas até 2 km num ambiente de propagação maioritariamente urbano, e superiores a 5 km numa área mais aberta e com uma maior linha de vista.N/

Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions

This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Industrial Internet of Learning (IIoL): IIoT based Pervasive Knowledge Network for LPWAN – concept, framework and case studies

Industrial Internet of Things (IIoT) is performed based on the multiple sourced data collection, communication, management and analysis from the industrial environment. The data can be generated at every point in the manufacturing production process by real-time monitoring, connection and interaction in the industrial field through various data sensing devices, which creates a big data environment for the industry. To collect, transfer, store and analyse such a big data efficiently and economically, several challenges have imposed to the conventional big data solution, such as high unreliable latency, massive energy consumption, and inadequate security. In order to address these issues, edge computing, as an emerging technique, has been researched and developed in different industries. This paper aims to propose a novel framework for the intelligent IIoT, named Industrial Internet of Learning (IIoL). It is built using an industrial wireless communication network called Low-power wide-area network (LPWAN). By applying edge computing technologies in the LPWAN, the high-intensity computing load is distributed to edge sides, which integrates the computing resource of edge devices to lighten the computational complexity in the central. It cannot only reduce the energy consumption of processing and storing big data but also low the risk of cyber-attacks. Additionally, in the proposed framework, the information and knowledge are discovered and generated from different parts of the system, including smart sensors, smart gateways and cloud. Under this framework, a pervasive knowledge network can be established to improve all the devices in the system. Finally, the proposed concept and framework were validated by two real industrial cases, which were the health prognosis and management of a water plant and asset monitoring and management of an automobile factory