The Open Web of Things as a means to unlock the potential of the IoT

Abstract: There is a lot of hype around the Internet of Things along with talk about 100 billion devices within 10 years time. The promise of innovative new services and efficiency savings is fueling interest in a wide range of potential applications across many sectors including smart homes, healthcare, smart grids, smart cities, retail, and smart industry. However, the current reality is one of fragmentation and data silos. W3C is seeking to fix that by exposing IoT platforms through the Web with shared semantics and data formats as the basis for interoperability. This talk will address the abstractions needed to move from a Web of pages to a Web of things, and introduce the work that is being done on standards and on open source projects for a new breed of Web servers on microcontrollers to cloud based server farms. Speaker Biography -Dave Raggett : Dave has been involved at the heart of web standards since 1992, and part of the W3C Team since 1995. As well as working on standards, he likes to dabble with software, and more recently with IoT hardware. He has participated in a wide range of European research projects on behalf of W3C/ERCIM. He currently focuses on Web payments, and realising the potential for the Web of Things as an evolution from the Web of pages. Dave has a doctorate from the University of Oxford. He is a visiting professor at the University of the West of England, and lives in the UK in a small town near to Bath.

A Link Quality and Geographical-aware Routing Protocol for Video Transmission in Mobile IoT

Wireless mobile sensor networks are enlarging the Internet of Things (IoT) portfolio with a huge number of multimedia services for smart cities. Safety and environmental monitoring multimedia applications will be part of the Smart IoT systems, which aim to reduce emergency response time, while also predicting hazardous events. In these mobile and dynamic (possible disaster) scenarios, opportunistic routing allows routing decisions in a completely distributed manner, by using a hop- by-hop route decision based on protocol-specific characteristics, and a predefined end-to-end path is not a reliable solution. This enables the transmission of video flows of a monitored area/object with Quality of Experience (QoE) support to users, headquarters or IoT platforms. However, existing approaches rely on a single metric to make the candidate selection rule, including link quality or geographic information, which causes a high packet loss rate, and reduces the video perception from the human standpoint. This article proposes a cross-layer Link quality and Geographical-aware Opportunistic routing protocol (LinGO), which is designed for video dissemination in mobile multimedia IoT environments. LinGO improves routing decisions using multiple metrics, including link quality, geographic loca- tion, and energy. The simulation results show the benefits of LinGO compared with well-known routing solutions for video transmission with QoE support in mobile scenarios.

Quantitative comparison of SensibleThings and Microsoft Azure IoT Hub

With the development of the Internet-of-Things, more and more IoT platforms come up with different structures and characteristics. Making balance of their advantages and disadvantages, we should choose the suitable platform in differ- ent scenarios. For this project, I make comparison of a cloud-based centralized platform, Microsoft Azure IoT hub and a fully distributed platform, Sensi- bleThings. Quantitative comparison is made for performance by 2 scenarios, messages sending speed adds up, devices lie in different location. General com- parison is made for security, utilization and the storage. Finally I draw the con- clusion that SensibleThings performs more stable when a lot of messages push- es to the platform. Microsoft Azure has better geographic expansion. For gener- al comparison, Microsoft Azure IoT hub has better security. The requirement of local device for Microsoft Azure IoT hub is lower than SensibleThings. The SensibleThings are open source and free while Microsoft Azure follow the con- cept “pay as you go” with many throttling limitations for different editions. Microsoft is more user-friendly.

Theoretical and Quantitative Comparison of SensibleThings and GSN

This project is aimed at making comparison between current existing Internet- of-Things (IoT) platforms, SensibleThings (ST) and Global Sensors Networks (GSN). Project can be served as a further work of platforms’ investigation. Comparing and learning from each other aim to contribute to the improvement of future platforms development. Detailed comparison is mainly with the respect of platform feature, communication and data present-frequency performance under stress, and platform node scalability performance on one limited device. Study is conducted through developing applications on each platform, and making measuring performance under the same condition in household network environment. So far, all these respects have had results and been concluded. Qualitatively comparing, GSN performs better in the facets of node’s swift development and deployment, data management, node subscription and connection retry mechanism. Whereas, ST is superior in respects of network package encryption, platform reliability, session initializing latency, and degree of developing freedom. In quantitative comparison, nodes on GSN has better data push pressure resistence while ST nodes works with lower session latency. In terms of data present-frequency, ST node can reach higher updating frequency than GSN node. In the aspect of node sclability on one limited device, ST nodes take the advantage in averagely lower latency than GSN node when nodes number is less than 15 on limited device. But due to sharing mechanism of GSN, on one limited device, it's nodes shows more scalable if platform nodes have similar job.

Comparison between SensibleThings and Kaa platform

With the Internet of Things becoming more and more popular, and a prediction that there will be more than 50 million devices connected to the Internet in 2020, the quantity of IoT platforms on the market is rapidly growing. Facing so many platforms to choose, the object of this thesis is to give some suggestions for reference by performing a quantitative comparison between two platforms: SensibleThings and Kaa. These two platforms have difference architectures so may suitable in different scenes. The comparison includes some measurement and evaluation under two designed scenarios and a general contrast in theory. Two scenarios cover cases of message delivery between two endpoints at different rates and multiple endpoints pushing log data continually. The result of measurement together with the theoretical analysis draw out the following conclusion. SensibleThings platform is more suitable for simple and small-scale message delivery between endpoints, like home environment with few devices. And Kaa platform is more suitable for large-scale and complicated application for data collection and processing, like meteorology field with huge amount of sensors and data.

An Image Recognition Software for Identification of Vehicle Homologation Tags

In recent years, we have witnessed great changes in the industrial environment as a result of the innovations introduced by Industry 4.0, especially in the integration of Internet of Things, Automation and Robotics in the manufacturing field. The project presented in this thesis lies within this innovation context and describes the implementation of an Image Recognition application focused on the automotive field. The project aims at helping the supply chain operator to perform an effective and efficient check of the homologation tags present on vehicles. The user contribution consists in taking a picture of the tag and the application will automatically, exploiting Amazon Web Services, return the result of the control about the correctness of the tag, the correct positioning within the vehicle and the presence of faults or defects on the tag. To implement this application we ombined two IoT platforms widely used in industrial field: Amazon Web Services(AWS) and ThingWorx. AWS exploits Convolutional Neural Networks to perform Text Detection and Image Recognition, while PTC ThingWorx manages the user interface and the data manipulation.

Communications with QoS in IoT&Cloud environment

Dissertação de mestrado integrado em Engenharia de Telecomunicações e Informática

Application Platforms for the Internet of Things: Theory, Architecture, Protocols, Data Formats, and Privacy

The Internet of Things (IoT) is the next industrial revolution: we will interact naturally with real and virtual devices as a key part of our daily life. This technology shift is expected to be greater than the Web and Mobile combined. As extremely different technologies are needed to build connected devices, the Internet of Things field is a junction between electronics, telecommunications and software engineering. Internet of Things application development happens in silos, often using proprietary and closed communication protocols. There is the common belief that only if we can solve the interoperability problem we can have a real Internet of Things. After a deep analysis of the IoT protocols, we identified a set of primitives for IoT applications. We argue that each IoT protocol can be expressed in term of those primitives, thus solving the interoperability problem at the application protocol level. Moreover, the primitives are network and transport independent and make no assumption in that regard. This dissertation presents our implementation of an IoT platform: the Ponte project. Privacy issues follows the rise of the Internet of Things: it is clear that the IoT must ensure resilience to attacks, data authentication, access control and client privacy. We argue that it is not possible to solve the privacy issue without solving the interoperability problem: enforcing privacy rules implies the need to limit and filter the data delivery process. However, filtering data require knowledge of how the format and the semantics of the data: after an analysis of the possible data formats and representations for the IoT, we identify JSON-LD and the Semantic Web as the best solution for IoT applications. Then, this dissertation present our approach to increase the throughput of filtering semantic data by a factor of ten.

Architecture for IoT Node and Platform

Only recently, during the past five years, consumer electronics has been evolving rapidly. Many products have started to include “smart home” capabilities, enabling communication and interoperability of various smart devices. Even more devices and sensors can be remote controlled and monitored through cloud services. While the smart home systems have become very affordable to average consumer compared to the early solutions decades ago, there are still many issues and things that need to be fixed or improved upon: energy efficiency, connectivity with other devices and applications, security and privacy concerns, reliability, and response time. This paper focuses on designing Internet of Things (IoT) node and platform architectures that take these issues into account, notes other currently used solutions, and selects technologies in order to provide better solution. The node architecture aims for energy efficiency and modularity, while the platform architecture goals are in scalability, portability, maintainability, performance, and modularity. Moreover, the platform architecture attempts to improve user experience by providing higher reliability and lower response time compared to the alternative platforms. The architectures were developed iteratively using a development process involving research, planning, design, implementation, testing, and analysis. Additionally, they were documented using Kruchten’s 4+1 view model, which is used to describe the use cases and different views of the architectures. The node architecture consisted of energy efficient hardware, FC3180 microprocessor and CC2520 RF transceiver, modular operating system, Contiki, and a communication protocol, AllJoyn, used for providing better interoperability with other IoT devices and applications. The platform architecture provided reliable low response time control, monitoring, and initial setup capabilities by utilizing web technologies on various devices such as smart phones, tablets, and computers. Furthermore, an optional cloud service was provided in order to control devices and monitor sensors remotely by utilizing scalable high performance technologies in the backend enabling low response time and high reliability.

Healthcare Computer Reasoning Addressing Chronically Ill Societies Using IoT: Deep Learning AI to the Rescue of Home-Based Healthcare

The authors present a proposal to develop intelligent assisted living environments for home based healthcare. These environments unite the chronical patient clinical history sematic representation with the ability of monitoring the living conditions and events recurring to a fully managed Semantic Web of Things (SWoT). Several levels of acquired knowledge and the case based reasoning that is possible by knowledge representation of the health-disease history and acquisition of the scientific evidence will deliver, through various voice based natural interfaces, the adequate support systems for disease auto management but prominently by activating the less differentiated caregiver for any specific need. With these capabilities at hand, home based healthcare providing becomes a viable possibility reducing the institutionalization needs. The resulting integrated healthcare framework will provide significant savings while improving the generality of health and satisfaction indicators.

Flexible Computing Systems For AI Acceleration At The Extreme Edge Of The IoT

Embedding intelligence in extreme edge devices allows distilling raw data acquired from sensors into actionable information, directly on IoT end-nodes. This computing paradigm, in which end-nodes no longer depend entirely on the Cloud, offers undeniable benefits, driving a large research area (TinyML) to deploy leading Machine Learning (ML) algorithms on micro-controller class of devices. To fit the limited memory storage capability of these tiny platforms, full-precision Deep Neural Networks (DNNs) are compressed by representing their data down to byte and sub-byte formats, in the integer domain. However, the current generation of micro-controller systems can barely cope with the computing requirements of QNNs. This thesis tackles the challenge from many perspectives, presenting solutions both at software and hardware levels, exploiting parallelism, heterogeneity and software programmability to guarantee high flexibility and high energy-performance proportionality. The first contribution, PULP-NN, is an optimized software computing library for QNN inference on parallel ultra-low-power (PULP) clusters of RISC-V processors, showing one order of magnitude improvements in performance and energy efficiency, compared to current State-of-the-Art (SoA) STM32 micro-controller systems (MCUs) based on ARM Cortex-M cores. The second contribution is XpulpNN, a set of RISC-V domain specific instruction set architecture (ISA) extensions to deal with sub-byte integer arithmetic computation. The solution, including the ISA extensions and the micro-architecture to support them, achieves energy efficiency comparable with dedicated DNN accelerators and surpasses the efficiency of SoA ARM Cortex-M based MCUs, such as the low-end STM32M4 and the high-end STM32H7 devices, by up to three orders of magnitude. To overcome the Von Neumann bottleneck while guaranteeing the highest flexibility, the final contribution integrates an Analog In-Memory Computing accelerator into the PULP cluster, creating a fully programmable heterogeneous fabric that demonstrates end-to-end inference capabilities of SoA MobileNetV2 models, showing two orders of magnitude performance improvements over current SoA analog/digital solutions.

DESIGN OF CONCRETE PLATFORMS OFFSHORE USING THE THREE-LAYER SHELL THEORY

The concrete offshore platforms, which are subjected a several loading combinations and, thus, requires an analysis more generic possible, can be designed using the concepts adopted to shell elements, but the resistance must be verify in particular cross-sections to shear forces. This work about design of shell elements will be make using the three-layer shell theory. The elements are subject to combined loading of membrane and plate, totalizing eight components of internal forces, which are three membrane forces, three moments (two out-of-plane bending moments and one in-plane, or torsion, moment) and two shear forces. The design method adopted, utilizing the iterative process proposed by Lourenco & Figueiras (1993) obtained from equations of equilibrium developed by Gupta (1896) , will be compared to results of experimentally tested shell elements found in the literature using the program DIANA.

Rheological, mechanical and mucoadhesive properties of thermoresponsive, bioadhesive binary mixtures composed of poloxamer 407 and carbopol 974P designed as platforms for implantable drug delivery systems for use in the oral cavity

This study described the formulation and characterisation of the viscoelastic, mechanical and mucoadhesive properties of thermoresponsive, binary polymeric systems composed of poloxamer (P407) and poly(acrylic acid, C974P) that were designed for use as a drug delivery platform within the oral cavity. Monopolymeric and binary polymeric formulations were prepared containing 10, 15 and 20% (w/w) poloxamer (407) and 0.10-0.25% (w/w) poly(acrylic acid, 934P). The flow theological and viscoelastic properties of the formulations were determined using controlled stress and oscillatory rheometry, respectively, the latter as a function of temperature. The mechanical and mucoadhesive properties (namely the force required to break the bond between the formulation and a pre-hydrated mucin disc) were determined using compression and tensile analysis, respectively. Binary systems composed of 10% (w/w) P407 and C934P were elastoviscous, were easily deformed under stress and did not exhibit mucoadhesion. Formulations containing 15 or 20% (w/w) Pluronic P407 and C934P exhibited a sol-gel temperature T(sol/gel), were viscoelastic and offered high elasticity and resistance to deformation at 37 degrees C. Conversely these formulations were elastoviscous and easily deformed at temperatures below the sol-gel transition temperature. The sol-gel transition temperatures of systems containing 15% (w/w) P407 were unaffected by the presence of C934P; however, increasing the concentration of C934P decreased the T(sol/gel) in formulations containing 20%(w/w) P407. Rheological synergy between P407 and C934P at 37 degrees C was observed and was accredited to secondary interactions between these polymers, in addition to hydrophobic interactions between P407 micelles. Importantly, formulations composed of 20% (w/w) P407 and C934P exhibited pronounced mucoadhesive properties. The ease of administration (below the T(sol/gel)) in conjunction with the viscoelastic (notably high elasticity) and mucoadhesive properties (at body temperature) render the formulations composed of 20% (w/w) P407 and C934P as potentially useful platforms for mucoadhesive, controlled topical drug delivery within the oral cavity. (c) 2009 Published by Elsevier B.V.

Participatory Online Platforms and the Construction of Citizen’s Autonomy in Health Issues

The goal of the present study is mapping the nature of possible contributions of participatory online platforms in citizen actions that may contribute in the fight against cancer and its associated consequences. The research is based on the analysis of online solidarity networks, namely the ones residing on Facebook and the blogosphere, that citizens have been gradually resorting to. The research is also based on the development of newer and more efficient solutions that provide the individual (directly or indirectly affected by issues of oncology) with the means to overcome feelings of impotence and fatality. In this chapter, the authors summarize the processes of usage of these decentralized, freer participatory platforms by citizens and institutions, while attempting to unravel existing hype and stigma; the authors also provide a first survey of the importance and the role of institutions in this kind of endeavor; lastly, they present a prototype, developed in the context of the present study that is specifically dedicated to addressing oncology through social media.

Intelligent recommendation system for e-learning platforms

As more and more digital resources are available, finding the appropriate document becomes harder. Thus, a new kind of tools, able to recommend the more appropriated resources according the user needs, becomes even more necessary. The current project implements an intelligent recommendation system for elearning platforms. The recommendations are based on one hand, the performance of the user during the training process and on the other hand, the requests made by the user in the form of search queries. All information necessary for decision-making process of recommendation will be represented in the user model. This model will be updated throughout the target user interaction with the platform.