Effective Use Methods for Continuous Sensor Data Streams in Manufacturing Quality Control

This work outlines an approach for managing sensor data streams of continuous numerical data in product manufacturing settings, emphasizing statistical process control, low computational and memory overhead, and saving information necessary to reduce the impact of nonconformance to quality specifications. While there is extensive literature, knowledge, and documentation about standard data sources and databases, the high volume and velocity of sensor data streams often makes traditional analysis unfeasible. To that end, an overview of data stream fundamentals is essential. An analysis of commonly used stream preprocessing and load shedding methods follows, succeeded by a discussion of aggregation procedures. Stream storage and querying systems are the next topics. Further, existing machine learning techniques for data streams are presented, with a focus on regression. Finally, the work describes a novel methodology for managing sensor data streams in which data stream management systems save and record aggregate data from small time intervals, and individual measurements from the stream that are nonconforming. The aggregates shall be continually entered into control charts and regressed on. To conserve memory, old data shall be periodically reaggregated at higher levels to reduce memory consumption

Middleware Architecture for Sensing as a Service

The Internet of Things is a concept that envisions the world as a smart space in which physical objects embedded with sensors, actuators, and network connectivity can communicate and react to their surroundings. Recent advancement in information and communication technologies make it possible to make the IoT vision a reality. However, IoT devices and consumers of data from these IoT devices can be owned by different entities which makes IoT data sharing a real challenge. Sensing as a Service is a concept that is influenced by the cloud computing term “Every Thing as a Service”. Sensing as a Service enables sensor data sharing. Sensing as a Service middleware enables IoT applications to access data generated by sensing devices owned by other entities. IoT applications are charged by the Sensing as a Service middleware for the amount of sensor data they use. This thesis addresses the architectural design of a cloud-based Sensing as Service middleware. The middleware enables sensor owners to sell their sensor data through the Internet. IoT applications can collect, and analyze sensors through the middleware API. We propose multitenancy algorithms for the middleware resource management. In addition, we propose a SQL-Like language that can be used by IoT applications for sensing service discovery, and sensor stream analytics. The evaluation of the middleware implementation shows the effectiveness of the algorithm

Processamento de eventos complexos como serviço em ambientes multi-nuvem

Orientadores: Luiz Fernando Bittencourt, Miriam Akemi Manabe CapretzTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O surgimento das tecnologias de dispositivos móveis e da Internet das Coisas, combinada com avanços das tecnologias Web, criou um novo mundo de Big Data em que o volume e a velocidade da geração de dados atingiu uma escala sem precedentes. Por ser uma tecnologia criada para processar fluxos contínuos de dados, o Processamento de Eventos Complexos (CEP, do inglês Complex Event Processing) tem sido frequentemente associado a Big Data e aplicado como uma ferramenta para obter informações em tempo real. Todavia, apesar desta onda de interesse, o mercado de CEP ainda é dominado por soluções proprietárias que requerem grandes investimentos para sua aquisição e não proveem a flexibilidade que os usuários necessitam. Como alternativa, algumas empresas adotam soluções de baixo nível que demandam intenso treinamento técnico e possuem alto custo operacional. A fim de solucionar esses problemas, esta pesquisa propõe a criação de um sistema de CEP que pode ser oferecido como serviço e usado através da Internet. Um sistema de CEP como Serviço (CEPaaS, do inglês CEP as a Service) oferece aos usuários as funcionalidades de CEP aliadas às vantagens do modelo de serviços, tais como redução do investimento inicial e baixo custo de manutenção. No entanto, a criação de tal serviço envolve inúmeros desafios que não são abordados no atual estado da arte de CEP. Em especial, esta pesquisa propõe soluções para três problemas em aberto que existem neste contexto. Em primeiro lugar, para o problema de entender e reusar a enorme variedade de procedimentos para gerência de sistemas CEP, esta pesquisa propõe o formalismo Reescrita de Grafos com Atributos para Gerência de Processamento de Eventos Complexos (AGeCEP, do inglês Attributed Graph Rewriting for Complex Event Processing Management). Este formalismo inclui modelos para consultas CEP e transformações de consultas que são independentes de tecnologia e linguagem. Em segundo lugar, para o problema de avaliar estratégias de gerência e processamento de consultas CEP, esta pesquisa apresenta CEPSim, um simulador de sistemas CEP baseado em nuvem. Por fim, esta pesquisa também descreve um sistema CEPaaS fundamentado em ambientes multi-nuvem, sistemas de gerência de contêineres e um design multiusuário baseado em AGeCEP. Para demonstrar sua viabilidade, o formalismo AGeCEP foi usado para projetar um gerente autônomo e um conjunto de políticas de auto-gerenciamento para sistemas CEP. Além disso, o simulador CEPSim foi minuciosamente avaliado através de experimentos que demonstram sua capacidade de simular sistemas CEP com acurácia e baixo custo adicional de processamento. Por fim, experimentos adicionais validaram o sistema CEPaaS e demonstraram que o objetivo de oferecer funcionalidades CEP como um serviço escalável e tolerante a falhas foi atingido. Em conjunto, esses resultados confirmam que esta pesquisa avança significantemente o estado da arte e também oferece novas ferramentas e metodologias que podem ser aplicadas à pesquisa em CEPAbstract: The rise of mobile technologies and the Internet of Things, combined with advances in Web technologies, have created a new Big Data world in which the volume and velocity of data generation have achieved an unprecedented scale. As a technology created to process continuous streams of data, Complex Event Processing (CEP) has been often related to Big Data and used as a tool to obtain real-time insights. However, despite this recent surge of interest, the CEP market is still dominated by solutions that are costly and inflexible or too low-level and hard to operate. To address these problems, this research proposes the creation of a CEP system that can be offered as a service and used over the Internet. Such a CEP as a Service (CEPaaS) system would give its users CEP functionalities associated with the advantages of the services model, such as no up-front investment and low maintenance cost. Nevertheless, creating such a service involves challenges that are not addressed by current CEP systems. This research proposes solutions for three open problems that exist in this context. First, to address the problem of understanding and reusing existing CEP management procedures, this research introduces the Attributed Graph Rewriting for Complex Event Processing Management (AGeCEP) formalism as a technology- and language-agnostic representation of queries and their reconfigurations. Second, to address the problem of evaluating CEP query management and processing strategies, this research introduces CEPSim, a simulator of cloud-based CEP systems. Finally, this research also introduces a CEPaaS system based on a multi-cloud architecture, container management systems, and an AGeCEP-based multi-tenant design. To demonstrate its feasibility, AGeCEP was used to design an autonomic manager and a selected set of self-management policies. Moreover, CEPSim was thoroughly evaluated by experiments that showed it can simulate existing systems with accuracy and low execution overhead. Finally, additional experiments validated the CEPaaS system and demonstrated it achieves the goal of offering CEP functionalities as a scalable and fault-tolerant service. In tandem, these results confirm this research significantly advances the CEP state of the art and provides novel tools and methodologies that can be applied to CEP researchDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação140920/2012-9CNP

Solar: An Open Platform for Context-Aware Mobile Applications

Emerging pervasive computing technologies transform the way we live and work by embedding computation in our surrounding environment. To avoid increasing complexity, and allow the user to concentrate on her tasks, applications in a pervasive computing environment must automatically adapt to their changing \em context, including the user state and the physical and computational environment in which they run. Solar is a middleware platform to help these “context-aware” applications aggregate desired context from heterogeneous sources and to locate environmental services depending on the current context. By moving most of the context computation into the infrastructure, Solar allows applications to run on thin mobile clients more effectively. By providing an open framework to enable dynamic injection of context processing modules, Solar shares these modules across many applications, reducing application development cost and network traffic. By distributing these modules across network nodes and reconfiguring the distribution at runtime, Solar achieves parallelism and online load balancing

4Sensing - decentralized processing for participatory sensing data

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática.Participatory sensing is a new application paradigm, stemming from both technical and social drives, which is currently gaining momentum as a research domain. It leverages the growing adoption of mobile phones equipped with sensors, such as camera, GPS and accelerometer, enabling users to collect and aggregate data, covering a wide area without incurring in the costs associated with a large-scale sensor network. Related research in participatory sensing usually proposes an architecture based on a centralized back-end. Centralized solutions raise a set of issues. On one side, there is the implications of having a centralized repository hosting privacy sensitive information. On the other side, this centralized model has financial costs that can discourage grassroots initiatives. This dissertation focuses on the data management aspects of a decentralized infrastructure for the support of participatory sensing applications, leveraging the body of work on participatory sensing and related areas, such as wireless and internet-wide sensor networks, peer-to-peer data management and stream processing. It proposes a framework covering a common set of data management requirements - from data acquisition, to processing, storage and querying - with the goal of lowering the barrier for the development and deployment of applications. Alternative architectural approaches - RTree, QTree and NTree - are proposed and evaluated experimentally in the context of a case-study application - SpeedSense - supporting the monitoring and prediction of traffic conditions, through the collection of speed and location samples in an urban setting, using GPS equipped mobile phones

Design and implementation of an efficient data stream processing system

In standard database scenarios, an end-user assumes that all data (e.g., sensor readings) is stored in a database. Therefore, one can simply submit any arbitrary complex processing in the form of SQL queries or stored procedures to a database server. Data stream oriented applications are typically dealing with huge volumes of data. Storing data and performing off-line processing on this huge dataset can be costly, time consuming and impractical. This work describes our research results while designing and implementing an efficient data management system for online and off-line processing of data streams in the field of environmental monitoring. Our target data sources are wireless sensor networks. Although our focus is on a specific application domain, the results of this thesis are designed in a generic way, so that they can be applied to wide variety of data stream oriented applications. This thesis starts by first presenting the state-of-the-art in data stream processing research specifically window processing concepts, continuous queries, stream filtering query languages and in-network data processing (particular focus on TinyOS-based approaches). We present key existing data stream processing engines, their internal architecture and how they are compared to our platform, namely Global Sensor Network (GSN) middleware. GSN middleware enables fast and flexible deployment and interconnection of sensor networks. It provides simple and uniform access to a comprehensive set of heterogeneous technologies. Additionally, GSN offers zero-programming deployment and data-oriented integration of sensor networks and supports dynamic re-configuration and adaptation at runtime. We present the virtual sensor concept, which offers a high-level view of arbitrary stream data sources, its powerful declarative specification and query tools. Furthermore, we describe design, conceptual, architectural and optimization decisions of GSN platform in detail. In order to achieve high efficiency while processing large volumes of streaming data using window-based continuous queries, we present a set of optimization algorithms and techniques to intelligently group and process different types of continuous queries. While adapting GSN to large scale sensor network deployments, we have encountered several performance bottlenecks. One of the challenges we faced was related to scalable delivery of streaming data for high data rate streams. We found out that we could dramatically improve the performance of a query processor by performing simple grouping of user queries hence sharing both the processing and memory costs among similar queries. Moreover, we encountered a similar performance issue while scheduling continuous queries. Problem of efficiently scheduling the execution of continuous queries with window and sliding parameters is not addressed in depth in literature. This problem becomes severe when one considers large volumes of high data rate streams. In these cases, an efficient query scheduler not only increases the performance at least by an order of magnitude but also, decreases the response time and memory requirements. Finally, we present how our GSN platform can get integrated with an external data sharing and visualization framework namely Microsoft's SenseWeb platform. Microsoft's SenseWeb platform, provides a sensor network data gathering and visualization infrastructure which is globally accessible to the end users. This integration (which is initiated by the Swiss Experiment project and demanded by GSN users) not only shows the scalability of GSN platform when combined with optimized algorithms, but also demonstrates its flexibility

Complex Event Processing as a Service in Multi-Cloud Environments

The rise of mobile technologies and the Internet of Things, combined with advances in Web technologies, have created a new Big Data world in which the volume and velocity of data generation have achieved an unprecedented scale. As a technology created to process continuous streams of data, Complex Event Processing (CEP) has been often related to Big Data and used as a tool to obtain real-time insights. However, despite this recent surge of interest, the CEP market is still dominated by solutions that are costly and inflexible or too low-level and hard to operate. To address these problems, this research proposes the creation of a CEP system that can be offered as a service and used over the Internet. Such a CEP as a Service (CEPaaS) system would give its users CEP functionalities associated with the advantages of the services model, such as no up-front investment and low maintenance cost. Nevertheless, creating such a service involves challenges that are not addressed by current CEP systems. This research proposes solutions for three open problems that exist in this context. First, to address the problem of understanding and reusing existing CEP management procedures, this research introduces the Attributed Graph Rewriting for Complex Event Processing Management (AGeCEP) formalism as a technology- and language-agnostic representation of queries and their reconfigurations. Second, to address the problem of evaluating CEP query management and processing strategies, this research introduces CEPSim, a simulator of cloud-based CEP systems. Finally, this research also introduces a CEPaaS system based on a multi-cloud architecture, container management systems, and an AGeCEP-based multi-tenant design. To demonstrate its feasibility, AGeCEP was used to design an autonomic manager and a selected set of self-management policies. Moreover, CEPSim was thoroughly evaluated by experiments that showed it can simulate existing systems with accuracy and low execution overhead. Finally, additional experiments validated the CEPaaS system and demonstrated it achieves the goal of offering CEP functionalities as a scalable and fault-tolerant service. In tandem, these results confirm this research significantly advances the CEP state of the art and provides novel tools and methodologies that can be applied to CEP research