A framework for feeding Linked Data to Complex Event Processing engines

A huge volume of Linked Data has been published on the Web, yet is not processable by Complex Event Processing (CEP) or Event Stream Processing (ESP) engines. This paper presents a frame-work to bridge this gap, under which Linked Data are first translated into events conforming to a lightweight ontology, and then fed to CEP engines. The event processing results will also be published back onto the Web of Data. In this way, CEP engines are connected to the Web of Data, and the ontological reasoning is integrated with event processing. Finally, the implementation method and a case study of the framework are presented

Архитектура системы параллельной обработки больших данных для мониторинга безопасности сетей Интернета вещей

Internet-of-Things networks are applied in many areas of people life now. A cornerstone in a issue of a possibility of further distribution and use of these networks is the aspect of security support. However, the features of these networks complicate the use of traditional means and systems of computer protection in them. One of such features is the need to analyze very large volumes of data, heterogeneous by the nature, in real time and with the minimum computing expenses. Taking into account the features of computational capabilities of Internet-of-Things networks the architecture of the system for parallel big data processing based on the data processing technology named as Complex Event Processing and the parallel computing platform Hadoop is offered. The issues directly connected to the architecture of the system and with implementation of its principal components are considered. These components are: data collection component, data storage component, data normalization and analysis component, and data visualization component. An interconnection between components is provided by means of the Hadoop Distributed File System that is a basis for creation of the distributed data storage. The data collection component organizes the distributed data acquisition and their storage in the data storage component. The data normalization and analysis component transforms data to a uniform format and processes them by means of correlation rules. The data visualization component presents data in a graphical form more suitable for further perception by the operator. The results of the experimental evaluation of the system performance confirming a conclusion about its high performance are discussed.Сети Интернета вещей в настоящее время находят свое применение во многих областях жизни людей. Краеугольным камнем в вопросе возможности дальнейшего распространения и использования таких сетей является аспект обеспечения их безопасности. Однако особенности сетей данного вида таковы, что использование в них традиционных средств и систем компьютерной защиты затруднено или невозможно. Одной из таких особенностей является необходимость в режиме реального времени и с минимальными вычислительными затратами анализировать очень большие объемы данных, разнородных по своей природе. С учетом особенностей вычислительных мощностей сети Интернета вещей предлагается архитектура системы параллельной обработки больших данных, основанная на использовании технологии обработки потоков данных Complex Event Processing и платформы параллельных вычислений Hadoop. Рассматриваются вопросы, непосредственно связанные с архитектурой системы, а также с реализацией следующих ее основных компонентов: сбора данных, хранения данных, нормализации и анализа данных и визуализации данных. Взаимосвязь между компонентами обеспечивается с помощью распределенной файловой системы Hadoop, которая является основой для построения распределенного хранилища данных. Компонент сбора данных организует распределенный прием данных и их хранение в компоненте хранилища данных. Компонент нормализации и анализа данных преобразует их к единому формату и обрабатывает с помощью правил корреляции. Компонент визуализации данных представляет данные в графическом виде, более удобном для дальнейшего восприятия оператором. Обсуждаются результаты экспериментальной оценки производительности системы, подтверждающие вывод о ее высокой эффективности

Architecting Social Internet of Things

In the new era of the Internet of Things (IoT), most of the devices we interact with daily are connected to the Internet. From tiny sensors, lamps, home appliances, home security systems and health-care devices, to complex heating, ventilation and air conditioning (HVAC) systems at home, myriad devices have network connectivity and provide smart applications. The Social Internet of Things (SIoT) is a new paradigm where IoT merges with social networks, allowing people and connected devices as well as the devices themselves to interact within a social network framework to support a new social navigation. Smart homes is one of the domains that can fully leverage this new paradigm, which will enable people and devices, even in different homes, to actively and mostly automatically collaborate to discover and share new information and services. Unfortunately the heterogeneous nature of the devices around the home prohibits seamless communication in the (S)IoT. Furthermore, the state-of-the-art solutions in smart homes offer little, if any, support for collaborating users and devices. This dissertation describes a new, scalable approach to connect, interact and share useful information through devices and users with common interests. The dissertation has three contributions. First, it proposes a holistic and extensible smart home gateway architecture that seamlessly integrates heterogeneous protocol-- and vendor-- specific devices and services and provides fine-grained access controls. Second, it defines an interoperable, scalable and extensible software architecture for a novel cloud-based collaboration framework for a large number of devices and users in many different smart homes. Third, it provides a reasoning framework to enable automated decisions based on the discovered information and knowledge created and shared by end users. The developed architecture and solutions are implemented in real systems, which integrate with many different devices from different manufacturers and run multiple categories of rules created by end users. The architectural evaluation results show the developed systems are interoperable, scalable and extensible