Designing a context-aware discovery service for IoT devices

Internet of Things (IoT) is in a continuous expansion phase, from millions of devices to tens of billions in upcoming years, which will have major impacts on infrastructure, business models, and industry standards throughout the entire IT ecosystem. It is expected that several diverse devices to invade by 2020. Depending on different application domains, IoT applications require devices, sensors, middlewares, networks and other enabling technologies to be integrated e.g., the high-level central control of IoT applications can be deployed on the cloud while others are running close to the "edge", forming a unified, scalable and feasible system. One of the important integration aspects in IoT ecosystem is discovering devices and sensors based on a particular context regardless of their heterogeneity. In this thesis, we propose Context-Aware Discovery Service for Internet of Things (CADsIoT) that deals with devices and sensors installed in IoT environments, streamlining the process of registration, management and dynamic discovery of devices based on contextual information. CADsIoT allows device and sensor registration, attaching them with particular context and leverage subscription features to enable dynamic discovery based on the attached context. Additionally, real-time notifications are triggered when new devices are discovered. For the validation of our concept, we discuss the requirements and a descriptive motivation scenario, which is followed by a discussion of the prototypical implementation. The prototype consists of CADsIoT Core, a Representational State Transfer (REST) based middleware and Navigator, an Android mobile application as a client

Cross-layer Peer-to-Peer Computing in Mobile Ad Hoc Networks

The future information society is expected to rely heavily on wireless technology. Mobile access to the Internet is steadily gaining ground, and could easily end up exceeding the number of connections from the fixed infrastructure. Picking just one example, ad hoc networking is a new paradigm of wireless communication for mobile devices. Initially, ad hoc networking targeted at military applications as well as stretching the access to the Internet beyond one wireless hop. As a matter of fact, it is now expected to be employed in a variety of civilian applications. For this reason, the issue of how to make these systems working efficiently keeps the ad hoc research community active on topics ranging from wireless technologies to networking and application systems. In contrast to traditional wire-line and wireless networks, ad hoc networks are expected to operate in an environment in which some or all the nodes are mobile, and might suddenly disappear from, or show up in, the network. The lack of any centralized point, leads to the necessity of distributing application services and responsibilities to all available nodes in the network, making the task of developing and deploying application a hard task, and highlighting the necessity of suitable middleware platforms. This thesis studies the properties and performance of peer-to-peer overlay management algorithms, employing them as communication layers in data sharing oriented middleware platforms. The work primarily develops from the observation that efficient overlays have to be aware of the physical network topology, in order to reduce (or avoid) negative impacts of application layer traffic on the network functioning. We argue that cross-layer cooperation between overlay management algorithms and the underlying layer-3 status and protocols, represents a viable alternative to engineer effective decentralized communication layers, or eventually re-engineer existing ones to foster the interconnection of ad hoc networks with Internet infrastructures. The presented approach is twofold. Firstly, we present an innovative network stack component that supports, at an OS level, the realization of cross-layer protocol interactions. Secondly, we exploit cross-layering to optimize overlay management algorithms in unstructured, structured, and publish/subscribe platforms

Data integrity for active web intermediaries

Master'sMASTER OF SCIENC

Enabling IoT in Manufacturing: from device to the cloud

Industrial automation platforms are experiencing a paradigm shift. With the new technol-ogies and strategies that are being applied to enable a synchronization of the digital and real world, including real-time access to sensorial information and advanced networking capabilities to actively cooperate and form a nervous system within the enterprise, the amount of data that can be collected from real world and processed at digital level is growing at an exponential rate. Indeed, in modern industry, a huge amount of data is coming through sensorial networks em-bedded in the production line, allowing to manage the production in real-time. This dissertation proposes a data collection framework for continuously collecting data from the device to the cloud, enabling resources at manufacturing industries shop floors to be handled seamlessly. The framework envisions to provide a robust solution that besides collecting, transforming and man-aging data through an IoT model, facilitates the detection of patterns using collected historical sensor data. Industrial usage of this framework, accomplished in the frame of the EU C2NET project, supports and automates collaborative business opportunities and real-time monitoring of the production lines

Sensae Console - Platforma de support para serviços baseados em IoT

Today there are more smart devices than people. The number of devices worldwide is forecast to almost triple from 8.74 billion in 2020 to more than 25.4 billion devices in 2030. The Internet of Things (IoT) is the connection of millions of smart devices and sensors connected to the Internet. These connected devices and sensors collect and share data for use and analysis by many organizations. Some examples of intelligent connected sensors are: GPS asset tracking, parking spots, refrigerator thermostats, soil condition and many others. The limit of different objects that can become intelligent sensors is limited only by our imagination. But these devices are mostly useless without a platform to analyze, store and present the aggregated data into business-oriented information. Recently, several platforms have emerged to address this need and help companies/governments to increase efficiency, cut on operational costs and improve safety. Sadly, most of these platforms are tailor made for the devices that the company offers. This dissertation presents the (Sensae Console) platform that enables and promotes the development of IoT-based business-oriented applications. This platform attempts to be device-neutral, IoT middleware-neutral and provide flexible upstream integration and hosting options while providing a simple and concise data streaming Application Programming Interface (API). Three IoT-based business-oriented applications built on top of the Sensae Console platform are presented as Proof of Concept (PoC) of its capabilities.Atualmente, existem mais sensores inteligentes do que pessoas. O número de sensores em todo o mundo deve quase triplicar de 8,74 bilhões em 2020 para mais de 25,4 bilhões em 2030. O conceito de IoT está relacionado com a interação entre milhões de dispositivos inteligentes através da Internet. Estes dispositivos e sensores conectados recolhem e disponibilizam dados para uso e análise por parte de muitas organizações. Alguns exemplos de sensores inteligentes e seus usos são: dispositivos GPS para rastreamento de ativos, monitorização de vagas de estacionamento, termostatos em arcas frigoríficas, condição do solo e muitos outros. O número de diferentes objetos que podem vir-se a tornar sensores inteligentes é limitado apenas pela nossa imaginação. Mas estes dispositivos são praticamente inúteis sem uma plataforma para analisar, armazenar e apresentar os dados agregados em informação relevante para o negócio em questão. Recentemente, várias plataformas surgiram para responder a essa necessidade e ajudar empresas/governos a aumentar a sua eficiência, reduzir custos operacionais e melhorar a segurança dos espaços e negócios. Infelizmente, a maioria dessas plataformas é feita à medida para os dispositivos que a empresa em questão oferece. Esta tese apresenta uma plataforma (Sensae Console) focada em que propiciar a criação de aplicações relacionados com IoT para negócios específicos. Esta plataforma procura ser agnóstica em relação aos dispositivos inteligentes e middleware de IoT usados por terceiros, oferece variadas e flexíveis opções de integração e hosting como também uma API de streaming simples e concisa. Três aplicações relacionadas com IoT, orientadas ao seu negócio e construídas com base na plataforma Sensae Console são apresentadas como provas de conceito das capacidades da plataforma

Event processing in web of things

The incoming digital revolution has the potential to drastically improve our productivity, reduce operational costs and improve the quality of the products. However, the realization of these promises requires the convergence of technologies — from edge computing to cloud, artificial intelligence, and the Internet of Things — blurring the lines between the physical and digital worlds. Although these technologies evolved independently over time, they are increasingly becoming intertwined. Their convergence will create an unprecedented level of automation, achieved via massive machine-to-machine interactions whose cornerstone are event processing tasks. This thesis explores the intersection of these technologies by making an in-depth analysis of their role in the life-cycle of event processing tasks, including their creation, placement and execution. First, it surveys currently existing Web standards, Internet drafts, and design patterns that are used in the creation of cloud-based event processing. Then, it investigates the reasons for event processing to start shifting towards the edge, alongside with the standards that are necessary for a smooth transition to occur. Finally, this work proposes the use of deep reinforcement learning methods for the placement and distribution of event processing tasks at the edge. Obtained results show that the proposed neural-based event placement method is capable of obtaining (near) optimal solutions in several scenarios and provide hints about future research directions.A nova revolução digital promete melhorar drasticamente a nossa produtividade, reduzir os custos operacionais e melhorar a qualidade dos produtos. A concretizac¸ ˜ao dessas promessas requer a convergˆencia de tecnologias – desde edge computing à cloud, inteligência artificial e Internet das coisas (IoT) – atenuando a linha que separa o mundo físico do digital. Embora as quatro tecnologias mencionadas tenham evoluído de forma independente ao longo do tempo, atualmente elas estão cada vez mais interligadas. A convergência destas tecnologias irá criar um nível de automatização sem precedentes.The research published in this work was supported by the Portuguese Foundation for Science and Technology (FCT) through CEOT (Center for Electronic, Optoelectronic and Telecommunications) funding (UID/MULTI/00631/2020) and by FCT Ph.D grant to Andriy Mazayev (SFRH/BD/138836/2018)

RLOps:Development Life-cycle of Reinforcement Learning Aided Open RAN

Radio access network (RAN) technologies continue to witness massive growth, with Open RAN gaining the most recent momentum. In the O-RAN specifications, the RAN intelligent controller (RIC) serves as an automation host. This article introduces principles for machine learning (ML), in particular, reinforcement learning (RL) relevant for the O-RAN stack. Furthermore, we review state-of-the-art research in wireless networks and cast it onto the RAN framework and the hierarchy of the O-RAN architecture. We provide a taxonomy of the challenges faced by ML/RL models throughout the development life-cycle: from the system specification to production deployment (data acquisition, model design, testing and management, etc.). To address the challenges, we integrate a set of existing MLOps principles with unique characteristics when RL agents are considered. This paper discusses a systematic life-cycle model development, testing and validation pipeline, termed: RLOps. We discuss all fundamental parts of RLOps, which include: model specification, development and distillation, production environment serving, operations monitoring, safety/security and data engineering platform. Based on these principles, we propose the best practices for RLOps to achieve an automated and reproducible model development process.Comment: 17 pages, 6 figrue

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