Mobile Edge Computing Empowers Internet of Things

In this paper, we propose a Mobile Edge Internet of Things (MEIoT) architecture by leveraging the fiber-wireless access technology, the cloudlet concept, and the software defined networking framework. The MEIoT architecture brings computing and storage resources close to Internet of Things (IoT) devices in order to speed up IoT data sharing and analytics. Specifically, the IoT devices (belonging to the same user) are associated to a specific proxy Virtual Machine (VM) in the nearby cloudlet. The proxy VM stores and analyzes the IoT data (generated by its IoT devices) in real-time. Moreover, we introduce the semantic and social IoT technology in the context of MEIoT to solve the interoperability and inefficient access control problem in the IoT system. In addition, we propose two dynamic proxy VM migration methods to minimize the end-to-end delay between proxy VMs and their IoT devices and to minimize the total on-grid energy consumption of the cloudlets, respectively. Performance of the proposed methods are validated via extensive simulations

Decentralized Control and Adaptation in Distributed Applications via Web and Semantic Web Technologies

The presented work provides an approach and an implementation for enabling decentralized control in distributed applications composed of heterogeneous components by benefiting from the interoperability provided by the Web stack and relying on semantic technologies for enabling data integration. In particular, the concept of Smart Components enables adaptability at runtime through an adaptation layer and is complemented by a reference architecture as well as a prototypical implementation

Security Management Framework for the Internet of Things

The increase in the design and development of wireless communication technologies offers multiple opportunities for the management and control of cyber-physical systems with connections between smart and autonomous devices, which provide the delivery of simplified data through the use of cloud computing. Given this relationship with the Internet of Things (IoT), it established the concept of pervasive computing that allows any object to communicate with services, sensors, people, and objects without human intervention. However, the rapid growth of connectivity with smart applications through autonomous systems connected to the internet has allowed the exposure of numerous vulnerabilities in IoT systems by malicious users. This dissertation developed a novel ontology-based cybersecurity framework to improve security in IoT systems using an ontological analysis to adapt appropriate security services addressed to threats. The composition of this proposal explores two approaches: (1) design time, which offers a dynamic method to build security services through the application of a methodology directed to models considering existing business processes; and (2) execution time, which involves monitoring the IoT environment, classifying vulnerabilities and threats, and acting in the environment, ensuring the correct adaptation of existing services. The validation approach was used to demonstrate the feasibility of implementing the proposed cybersecurity framework. It implies the evaluation of the ontology to offer a qualitative evaluation based on the analysis of several criteria and also a proof of concept implemented and tested using specific industrial scenarios. This dissertation has been verified by adopting a methodology that follows the acceptance in the research community through technical validation in the application of the concept in an industrial setting.O aumento no projeto e desenvolvimento de tecnologias de comunicação sem fio oferece múltiplas oportunidades para a gestão e controle de sistemas ciber-físicos com conexões entre dispositivos inteligentes e autônomos, os quais proporcionam a entrega de dados simplificados através do uso da computação em nuvem. Diante dessa relação com a Internet das Coisas (IoT) estabeleceu-se o conceito de computação pervasiva que permite que qualquer objeto possa comunicar com os serviços, sensores, pessoas e objetos sem intervenção humana. Entretanto, o rápido crescimento da conectividade com as aplicações inteligentes através de sistemas autônomos conectados com a internet permitiu a exposição de inúmeras vulnerabilidades dos sistemas IoT para usuários maliciosos. Esta dissertação desenvolveu um novo framework de cibersegurança baseada em ontologia para melhorar a segurança em sistemas IoT usando uma análise ontológica para a adaptação de serviços de segurança apropriados endereçados para as ameaças. A composição dessa proposta explora duas abordagens: (1) tempo de projeto, o qual oferece um método dinâmico para construir serviços de segurança através da aplicação de uma metodologia dirigida a modelos, considerando processos empresariais existentes; e (2) tempo de execução, o qual envolve o monitoramento do ambiente IoT, a classificação de vulnerabilidades e ameaças, e a atuação no ambiente garantindo a correta adaptação dos serviços existentes. Duas abordagens de validação foram utilizadas para demonstrar a viabilidade da implementação do framework de cibersegurança proposto. Isto implica na avaliação da ontologia para oferecer uma avaliação qualitativa baseada na análise de diversos critérios e também uma prova de conceito implementada e testada usando cenários específicos. Esta dissertação foi validada adotando uma metodologia que segue a validação na comunidade científica através da validação técnica na aplicação do nosso conceito em um cenário industrial

Prototyping and Evaluation of Sensor Data Integration in Cloud Platforms

The SFI Smart Ocean centre has initiated a long-running project which consists of developing a wireless and autonomous marine observation system for monitoring of underwater environments and structures. The increasing popularity of integrating the Internet of Things (IoT) with Cloud Computing has led to promising infrastructures that could realize Smart Ocean's goals. The project will utilize underwater wireless sensor networks (UWSNs) for collecting data in the marine environments and develop a cloud-based platform for retrieving, processing, and storing all the sensor data. Currently, the project is in its early stages and the collaborating partners are researching approaches and technologies that can potentially be utilized. This thesis contributes to the centre's ongoing research, focusing on the aspect of how sensor data can be integrated into three different cloud platforms: Microsoft Azure, Amazon Web Services, and the Google Cloud Platform. The goals were to develop prototypes that could successfully send data to the chosen cloud platforms and evaluate their applicability in context of the Smart Ocean project. In order to determine the most suitable option, each platform was evaluated based on set of defined criteria, focusing on their sensor data integration capabilities. The thesis has also investigated the cloud platforms' supported protocol bindings, as well as several candidate technologies for metadata standards and compared them in surveys. Our evaluation results shows that all three cloud platforms handle sensor data integration in very similar ways, offering a set of cloud services relevant for creating diverse IoT solutions. However, the Google Cloud Platform ranks at the bottom due to the lack of IoT focus on their platform, with less service options, features, and capabilities compared to the other two. Both Microsoft Azure and Amazon Web Services rank very close to each other, as they provide many of the same sensor data integration capabilities, making them the most applicable options.Masteroppgave i Programutvikling samarbeid med HVLPROG399MAMN-PRO

Programming patterns and development guidelines for Semantic Sensor Grids (SemSorGrid4Env)

The web of Linked Data holds great potential for the creation of semantic applications that can combine self-describing structured data from many sources including sensor networks. Such applications build upon the success of an earlier generation of 'rapidly developed' applications that utilised RESTful APIs. This deliverable details experience, best practice, and design patterns for developing high-level web-based APIs in support of semantic web applications and mashups for sensor grids. Its main contributions are a proposal for combining Linked Data with RESTful application development summarised through a set of design principles; and the application of these design principles to Semantic Sensor Grids through the development of a High-Level API for Observations. These are supported by implementations of the High-Level API for Observations in software, and example semantic mashups that utilise the API