Extended Reality and Internet of Things for Hyper-Connected Metaverse Environments

The Metaverse encompasses technologies related to the internet, virtual and augmented reality, and other domains toward smart interfaces that are hyper-connected, immersive, and engaging. However, Metaverse applications face inherent disconnects between virtual and physical components and interfaces. This work explores how an Extended Metaverse framework can be used to increase the seamless integration of interoperable agents between virtual and physical environments. It contributes an early theory and practice toward the synthesis of virtual and physical smart environments anticipating future designs and their potential for connected experiences.Comment: In Proceedings of 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), Christchurch, New Zealand, 202

Secure Cloud Computing based Energy Analytics Framework in Construction of Residential Buildings

The buildings are emanating a massive producer of data amidst being massive consumers of energy resources. Electrification of a region is seen as a breakthrough in fostering the economic development of the region. However, rapid urbanization has paved the way for the construction of huge buildings which is home to a large amount of population, which directly or indirectly contributes to energy consumption. Energy analytics is a form of energy conservation, especially in residential buildings, which is generally harnessed through cutting-edge computing technologies. This work proposed a comprehensive framework with five layers that collects data from the energy monitoring edge devices to build energy analytics by processing the data in the cloud platform. In addition to this, the framework uses a security score to monitor the illegitimate access of the cloud source by tracking the registered devices. This is a robust and generic framework that has the scope to include AI-based strategies that can be orchestrated in the cloud computing platform

Atmosphere: Context and situational-aware collaborative IoT architecture for edge-fog-cloud computing

The Internet of Things (IoT) has grown significantly in popularity, accompanied by increased capacity and lower cost of communications, and overwhelming development of technologies. At the same time, big data and real-time data analysis have taken on great importance and have been accompanied by unprecedented interest in sharing data among citizens, public administrations and other organisms, giving rise to what is known as the Collaborative Internet of Things. This growth in data and infrastructure must be accompanied by a software architecture that allows its exploitation. Although there are various proposals focused on the exploitation of the IoT at edge, fog and/or cloud levels, it is not easy to find a software solution that exploits the three tiers together, taking maximum advantage not only of the analysis of contextual and situational data at each tier, but also of two-way communications between adjacent ones. In this paper, we propose an architecture that solves these deficiencies by proposing novel technologies which are appropriate for managing the resources of each tier: edge, fog and cloud. In addition, the fact that two-way communications along the three tiers of the architecture is allowed considerably enriches the contextual and situational information in each layer, and substantially assists decision making in real time. The paper illustrates the proposed software architecture through a case study of respiratory disease surveillance in hospitals. As a result, the proposed architecture permits efficient communications between the different tiers responding to the needs of these types of IoT scenarios

Atmosphere: Context and situational-aware collaborative IoT architecture for edge-fog-cloud computing

The Internet of Things (IoT) has grown significantly in popularity, accompanied by increased capacity and lower cost of communications, and overwhelming development of technologies. At the same time, big data and realtime data analysis have taken on great importance and have been accompanied by unprecedented interest in sharing data among citizens, public administrations and other organisms, giving rise to what is known as the Collaborative Internet of Things. This growth in data and infrastructure must be accompanied by a software architecture that allows its exploitation. Although there are various proposals focused on the exploitation of the IoT at edge, fog and/or cloud levels, it is not easy to find a software solution that exploits the three tiers together, taking maximum advantage not only of the analysis of contextual and situational data at each tier, but also of two-way communications between adjacent ones. In this paper, we propose an architecture that solves these deficiencies by proposing novel technologies which are appropriate for managing the resources of each tier: edge, fog and cloud. In addition, the fact that two-way communications along the three tiers of the architecture is allowed considerably enriches the contextual and situational information in each layer, and substantially assists decision making in real time. The paper illustrates the proposed software architecture through a case study of respiratory disease surveillance in hospitals. As a result, the proposed architecture permits efficient communications between the different tiers responding to the needs of these types of IoT scenarios.This work was partially supported by the Spanish Ministry of Science and Innovation and the European Regional Development Fund (ERDF) under project FAME [RTI2018-093608-B-C33] and excellence network RCIS [RED2018-102654-T]. We also thank Carlos Llamas Jaén for his support with the setting up of the performance evaluation tests

RESTful framework for collaborative internet of things based on IEC 61850

El contenido de los capítulos 2 y 3 está sujeto a confidencialidad 161 p.En 1991, Mark Weiser formuló el paradigma de Computación Ubicua definiendo el concepto de Entorno Inteligente como un espacio físico repleto de dispositivos, muy integrados en el entorno, y con capacidades de identificación, sensorización y actuación. Internet de las Cosas (IoT) expande el ámbito de localización de estos dispositivos y servicios ubicuos, representados como cosas, de un entorno local a internet como red global. Para la implementación de estos escenarios de aplicación, la colaboración entre las cosas es uno de los principales retos de investigación. El objetivo de esta colaboración es ser capaces de satisfacer necesidades globales mediante la combinación de servicios individuales. Esta Tesis propone una arquitectura colaborativa entre las cosas desplegadas en internet.Las tecnologías alrededor de los Servicios Web SOAP/XML, adecuadas para IoT, soportan aspectos claves para un sistema colaborativo como la publicación, descubrimiento, control y gestión de eventos de los dispositivos. Como alternativa, REST ha ganado terreno en este ámbito por ser considerada una opción más ligera, sencilla y natural para la comunicación en internet. Sin embargo, no existen protocolos para descubrimiento y gestión de eventos para recursos REST. Esta Tesis aborda dicha carencia proponiendo una especificación de estos protocolos para arquitecturas REST. Otro aspecto importante es la representación, a nivel de aplicación, de las cosas distribuidas. Entre las propuestas para la estandarización de los modelos de información y comunicación en este dominio que podrían aplicarse, de manera similar, a IoT, destaca el estándar IEC 61850. Sin embargo, los protocolos de comunicación definidos por el estándar no son adecuados para IoT. Esta Tesis analiza la idoneidad del IEC 61850 para escenarios IoT y propone un protocolo de comunicación REST para sus servicios.Por último, se trata la problemática asociada a la confiabilidad que debe proporcionar una arquitectura IoT para dominios de aplicación relacionados con la salud o sistemas de seguridad funcional (Safety)

Extending the Metaverse: Exploring Generative Objects with Extended Reality Environments and Adaptive Context Awareness

The metaverse, with the internet, virtual and augmented reality, and other domains for an immersive environment, has been considered mainstream in recent years. However, the current metaverse platforms have a gap in the physical space, leading to reduced engagement in these applications. This thesis project explores an extended metaverse framework with generative content and the design of a seamless interface to increase the connection between the metaverse and the physical environment and create coherence and efficiency between them. The extended metaverse agent helps prevent this from happening by improving the interaction, embodiment, and agency that dynamically engage humans in mixed reality (MR) environments. This thesis project will design and prototype MR objects and environments with the research through design (RTD) and speculative design methodology, whereby future applications are imagined, assuming plausibility of smart glasses being commonplace to help users visualize the coherence of virtual and physical spaces in simultaneity. To summarize, this thesis project provides an extended metaverse framework and agent that generates from physical contexts to describe the coherence of virtual and physical environments

Internet of Things (IoT) for Automated and Smart Applications

Internet of Things (IoT) is a recent technology paradigm that creates a global network of machines and devices that are capable of communicating with each other. Security cameras, sensors, vehicles, buildings, and software are examples of devices that can exchange data between each other. IoT is recognized as one of the most important areas of future technologies and is gaining vast recognition in a wide range of applications and fields related to smart homes and cities, military, education, hospitals, homeland security systems, transportation and autonomous connected cars, agriculture, intelligent shopping systems, and other modern technologies. This book explores the most important IoT automated and smart applications to help the reader understand the principle of using IoT in such applications

A Comprehensive Review on Sentiment Analysis: Tasks, Approaches and Applications

Sentiment analysis (SA) is an emerging field in text mining. It is the process of computationally identifying and categorizing opinions expressed in a piece of text over different social media platforms. Social media plays an essential role in knowing the customer mindset towards a product, services, and the latest market trends. Most organizations depend on the customer's response and feedback to upgrade their offered products and services. SA or opinion mining seems to be a promising research area for various domains. It plays a vital role in analyzing big data generated daily in structured and unstructured formats over the internet. This survey paper defines sentiment and its recent research and development in different domains, including voice, images, videos, and text. The challenges and opportunities of sentiment analysis are also discussed in the paper. \keywords{Sentiment Analysis, Machine Learning, Lexicon-based approach, Deep Learning, Natural Language Processing

Enabling Privacy and Trust in Edge AI Systems

Recent advances in mobile computing and the Internet of Things (IoT) enable the global integration of heterogeneous smart devices via wireless networks. A common characteristic across these modern day systems is their ability to collect and communicate streaming data, making machine learning (ML) appealing for processing, reasoning, and predicting about the environment. More recently, low network latency requirements have made offloading intelligence to the cloud undesirable. These novel requirements have led to the emergence of edge computing, an approach that brings computation closer to the device with low latency, high throughput, and enhanced reliability. Together, they enable ML-powered information processing and control pipelines spanning end devices, edge computing, and cloud environments. However, continuous collaboration between cloud, edge and device is susceptible to information leakage and loss, leading to insecure and unreliable operation. This raises an important question: how can we design, develop, and evaluate high-performing ML systems that are trustworthy and privacy-preserving in resource-constrained edge environments? In this thesis, I address this question by designing and implementing privacy-preserving and trustworthy ML systems for distributed applications. I first introduce a system that establishes trust in the explanations generated from a popular visualization technique, saliency maps, using counterfactual reasoning. Through the proposed evaluation system, I assess the degree to which hypothesized explanations correspond to the semantics of edge-based reinforcement learning environments. Second, I examine the privacy implications of personalized models in distributed mobile services by proposing time-series based model inversion attacks. To thwart such attacks, I present a distributed framework, Pelican, that learns and deploys transfer learning-based personalized ML models in a privacy preserving manner on resource-constrained mobile devices. Third, I investigate ML models that are deployed on local devices for inference and highlight the ease with which proprietary information embedded in these models can be exposed. For mitigating such attacks, I present a secure on-device application framework, SODA, which is supported by real-time adversarial detection. Finally, I present an end-to-end privacy-aware system for a real-world application to model group interaction behavior via mobility sensing. The proposed system, W4-Groups, distributes computation across device, edge, and cloud resources to strengthen its privacy and trustworthiness guarantees

An Evidence-based Roadmap for IoT Software Systems Engineering

Context: The Internet of Things (IoT) has brought expectations for software inclusion in everyday objects. However, it has challenges and requires multidisciplinary technical knowledge involving different areas that should be combined to enable IoT software systems engineering. Goal: To present an evidence-based roadmap for IoT development to support developers in specifying, designing, and implementing IoT systems. Method: An iterative approach based on experimental studies to acquire evidence to define the IoT Roadmap. Next, the Systems Engineering Body of Knowledge life cycle was used to organize the roadmap and set temporal dimensions for IoT software systems engineering. Results: The studies revealed seven IoT Facets influencing IoT development. The IoT Roadmap comprises 117 items organized into 29 categories representing different concerns for each Facet. In addition, an experimental study was conducted observing a real case of a healthcare IoT project, indicating the roadmap applicability. Conclusions: The IoT Roadmap can be a feasible instrument to assist IoT software systems engineering because it can (a) support researchers and practitioners in understanding and characterizing the IoT and (b) provide a checklist to identify the applicable recommendations for engineering IoT software systems