Architecture Differences between Cloud and Fog Computing in Internet of Things

Internet adopted the two technologies namely cloud and IOT. To make revolution in the current period Cloud computing gives hope to IOT. FOG computing is basically an extension of cloud computing services. In this paper, we have introduced the topics I.e. cloud computing and FOG computing. After the introduction, we have discussed the architecture of IOT which includes both the three level architecture as well as the five level architecture. After the brief introduction of cloud computing, we have focused on the limitations of cloud computing that justifies why we shifted to the FOG computing which an extension to the cloud computing. Before concluding the paper, we have highlighted a few challenges that we might have to face in FOG computing according to the references that we have included in our paper

Interoperability for Industrial Internet of Things Based on Service-oriented Architecture

The new Industry 4.0 envisions a future for agile and effective integration of the physical operational technologies (OT) and the cyber information technologies (IT) as well as autonomous cooperation among them. However, the wide variety and heterogeneity of industrial systems and field devices -especially on the factory floor - increase integration complexity. To address these challenges, new technologies and concepts such as the Industrial Internet of Things (IIoT), Service-oriented Architecture (SoA), Semantic Technologies, Machine Learning and Artificial Intelligence are being introduced to the industrial environment. In this paper, we focus on how industrial automation systems and field devices can be integrated into the IIoT framework and coordinated to adapt to dynamic operating environment. Specifically, this paper proposed an interoperability solution that makes use of SoA and Semantic Technologies to achieve supervised coordination of IIoT application systems. To illustrate the potential of this approach, the Service-oriented Architecture-based Arrowhead Framework is used as the fundamental framework for the implementation of the approach.acceptedVersio

Energy Efficient Service Embedding In IoT over PON

In this paper, we present an energy efficient service embedding framework in Internet of Things (IoT) network using Mixed Integer Linear Programming (MILP). The framework enables an energy efficient smart road paradigm for different simultaneous applications supported by a passive optical network (PON) and wireless communication in the smart city. It optimizes the infrastructure's resources including the access network, IoT, fog and cloud computing. We consider an event-driven paradigm in a Service Oriented Architecture (SOA) in our framework in order to provide service abstraction of basic services which can be composed into complex services and exploited by the upper application layers. The framework results show that it is possible to reduce the power consumption by optimizing the selection of computing nodes and traffic distribution in the network while satisfying the service requirements

SysML modeling of service-oriented system-of-systems

The success of the ongoing fourth industrial revolution largely depends on our ways to cope with the novel design challenges arising from a combination of an enormous increase in process and product complexity, as well as the expected autonomy and self-organization of complex and diverse industrial hardware–software installments, often called systems-of-systems. In this paper, we employ the service-oriented architectural paradigm, as materialized in the Eclipse Arrowhead framework, to represent modern systems engineering principles and their open structural principles and, thus, relevance to flexible and adaptive systems. As for adequately capturing the structural aspect, we propose using model-based engineering techniques and, in particular, a SysML-based specialization of systems modeling. The approach is illustrated by a real-life use-case in industrial automation.publishedVersio

Dynamic Multilevel Workflow Management Concept for Industrial IoT Systems

Workflow management is implemented in manufacturing at many levels. The nature of processes variesat each level, hindering the use of a standard modeling orimplementation solution. The creation of a flexible workflow management framework that overarches the heterogeneous business process levels is challenging. Still, one of the promisesof the Industry 4.0 initiative is precisely this: to provideeasy-to-use models and solutions that enable efficient execution of enterprise targets. By addressing this challenge, this articleproposes a workflow execution model that integrates information and control flows of these levels while keeping their hierarchy. The overall model builds on the business process model andnotation (BPMN) for modeling at the enterprise level and recipemodeling based on colored Petri net (CPN) at the production level. Models produced with both alternatives are implemented and executed in a framework supported by an enterprise servicebus (ESB). Loosely coupled, late-bound system elements are connected through the arrowhead framework, which is builtupon the service-oriented architecture (SOA) concept. To proveits feasibility, this article presents the practical application ofthe model via an automotive production scenario

ARCA-IoT: An Attack-Resilient Cloud-Assisted IoT System

Putting trust in the world of the Internet of Things, where served and serving entities are often unknown, is very hard especially when personal and business information is often being exchanged for providing and consuming services. Moreover, the issues of interoperability and scalability of billions of heterogeneous things in IoT systems require more attention. A usercentric model of complex interconnected things must be designed in a way that not only makes things trustworthy for common people but it also provides the solution for interoperability and scalability. ARCA-IoT is such a system which not only identifies the attributes (including quality of service) essential for trust but also presents a user-centric model that is robust enough to tackle the attacks made by dishonest entities to manipulate the trustworthiness. For scalability and interoperability, a cloud-assisted environment is introduced in ARCA-IoT. An intuitive Naive Bayes approach is used to train ARCA-IoT in a way that it calculates the probabilities of the trustworthiness of the entities and then identifies various types of attacks with the support of three proposed algorithms. The approach is validated with a specifically designed simulated environment. Based on our simulation results, ARCA-IoT demonstrates the effectiveness in term of performance metrics such as accuracy, sensitivity, specificity, and precision. Besides this, the system outperforms the existing related approaches in terms of a qualitative analysis based on different parametric metrics such as interoperability, scalability, context-awareness, and a humanlike decision

Automação e otimização da produção de cerveja artesanal para a prospecção de técnicas sustentáveis

Tese apresentada ao Programa de Pós-Graduação Interdisciplinar em Energia e Sustentabilidade da Universidade Federal da Integração Latino-Americana, como requisito parcial à obtenção do título de Doutor em Energia e Sustentabilidade.Para atender a um amplo mercado consumidor, a indústria cervejeira utiliza grandes plantas automatizadas. Isso permite produzir cervejas de forma otimizada, garantindo repetibilidade na obtenção de um produto com características consistentes e minimização dos custos. Por outro lado, microcervejarias possuem processos predominantemente manuais, priorizando a elaboração de produtos diferenciados e maior valor agregado, para um público mais restrito. A produção de cervejas diferenciadas e que eventualmente podem utilizar matérias-primas locais demandam experimentos que avaliem a sua viabilidade. Nesse contexto experimental, reproduzir os métodos das microcervejarias torna-se desafiador devido à escala de produção e os equipamentos disponíveis. Esse aspecto é amplificado ao considerar que a produção sustentável se tornou ponto central no processo produtivo. Em relação ao consumo de água e energia, as grandes cervejarias têm processos automatizados e eficientes para coleta de dados, enquanto microcervejarias e ambientes experimentais tendem a operar manualmente, o que dificulta a obtenção desses dados. Por isso, o monitoramento automatizado e rigoroso dos recursos é uma ferramenta importante para cervejeiros artesanais e pesquisadores. O controle da temperatura em fases como a mosturação e fervura, assim como o registro do consumo de água e energia, são frequentemente baseados em estimativas, dada a falta de técnicas de medição integradas e precisas. Para atender a essa demanda, foi implementado um protótipo para produção de cerveja em escala de bancada, que realiza a coleta, monitoria e armazenamento de dados produtivos, consumo de água e energia. Suas operações são executadas através de um módulo de interpretação de instruções que possibilita ao pesquisador parametrizar os experimentos de acordo com sua finalidade. O protótipo consiste em três componentes principais: uma planta para aquecimento e processamento do mosto cervejeiro; um módulo de controle que gerencia a recepção de dados de sensores; e um módulo de acionamento para automatização das operações. O software envia as instruções programáveis parametrizadas para realizar as operações, e os dados coletados são transmitidos e armazenados na nuvem. Utilizando o protótipo e a metodologia de superfície de resposta em um planejamento experimental de 23, com 4 pontos centrais e 6 pontos axiais, foram obtidos os dados de consumo energético, gravidade original e produtividade. Após a realização da análise experimental, foram aplicadas funções de desejabilidade para determinar uma resposta única para a otimização. Os testes com o protótipo mostraram variabilidade experimental com relação a gravidade original de 0,2%, com uma margem de erro de 0,1%. Para otimização utilizando múltiplas variáveis de resposta (kWh, gravidade original e produtividade), o modelo preditivo construído utilizando as funções de desejabilidade determinou um ponto de otimização, validado por experimentos posteriores que apresentaram uma variação inferior a 5%. Portanto, os testes com o protótipo confirmaram sua capacidade de executar tarefas programadas, orientar o experimento e coletar dados precisos. Adicionalmente, os resultados obtidos através do planejamento experimental evidenciaram o potencial de otimização da produção de cerveja em escala reduzida, conciliando produtividade e sustentabilidade. Posteriormente, outros planejamentos experimentais serão realizados através do protótipo, o que irá ampliar a base de conhecimento e possibilitar novas análises

Enabling IoT automation using local clouds

Various forms of cloud computing principles and technologies are becoming important recently. This paper ad- dresses cloud computing for automation and control applications. It’s argued that the open Internet cloud idea has such limitations that its not appropriate for automation. Since automation is physically and geographically local, it is inevitable to introduce the concept of local automation clouds. It’s here proposed that local automation clouds should be self contained an be able to execute the intended automation func- tionalities without any external resources. Thus providing a fence at the rim of the local cloud preventing any inbound or outbound communication. Such a local cloud provides possibilities to address key requirements of both todays and future automation solutions. Adding mechanisms for secure inter-cloud administra- tion and data tranfere enables local automation cloud to meet IoT automation system requirements as: 1) Interoperability of a wide range of IoT and legacy devices 2) Automation requirement on latency guarantee/prediction for communication and control computations. 3) Scalability of automation systems enabling very large integrated automation systems 4) Security and related safety of automation systems 5) Ease of application engineering 6) Multi stakeholder integration and operations agility. How these requirements can be met in such a local automation cloud is discussed with references to proposed solutions. The local automation cloud concept is further verified for a compartment climate control application. The control application included an IoT controller, four IoT sensors and actuators, and a physical layer communication gateway. The gateway acted as host for local cloud core functionalities. The climate control application has successfully been implemented using the open source Arrowhead Framework and its supports for design and implementation of self contained local automation clouds.Arrowhea