CEIoT: A Framework for Interlinking Smart Things in the Internet of Things

In the emerging Internet of Things (IoT) environment, things are interconnected but not interlinked. Interlinking relevant things offers great opportunities to discover implicit relationships and enable potential interactions among things. To achieve this goal, implicit correlations between things need to be discovered. However, little work has been done on this important direction and the lack of correlation discovery has inevitably limited the power of interlinking things in IoT. With the rapidly growing number of things that are connected to the Internet, there are increasing needs for correlations formation and discovery so as to support interlinking relevant things together effectively. In this paper, we propose a novel approach based on Multi-Agent Systems (MAS) architecture to extract correlations between smart things. Our MAS system is able to identify correlations on demand due to the autonomous behaviors of object agents. Specifically, we introduce a novel open-sourced framework, namely CEIoT, to extract correlations in the context of IoT. Based on the attributes of things our IoT dataset, we identify three types of correlations in our system and propose a new approach to extract and represent the correlations between things. We implement our architecture using Java Agent Development Framework (JADE) and conduct experimental studies on both synthetic and real-world datasets. The results demonstrate that our approach can extract the correlations at a much higher speed than the naive pairwise computation method

Modelo de referencia para la detección de eventos de contaminación industrial basado en una red de sensores

Industrial pollution is significantly affecting the sustainability of the planet. A data collection process allows us to review the contamination levels, and take actions in a timely manner. Nowadays wireless sensor networks support the collection of large amounts of environmental data. If these data are properly processed, they can be used in the detection of pollution sources, and in the design of preventive -or corrective- strategies to deal with it. However, this requires to effectively manage largeamount of data obtained from heterogeneous devices, including not only data from traditional sensors or motes of wireless sensor networks, but also from sensors within Smartphones. In order to handle this heterogeneity, we design a reference model that serves as a guide to combine the technologies of storage, processing and presentation of the data. This model can then be instantiated with specific hardware and software components that meet the needs of companies or government agencies interested in dealing with environmental pollution. In this article, the proposed reference model is presented, in conjunction with a prototype that can be used to solve problems of noise pollution in an automotive company located in Bogota.La contaminación industrial es un aspecto que está afectando en forma importante la sostenibilidad del planeta. Para poder manejar la contaminación, el primer paso es detectarla tan pronto como ésta ocurra, de ahí la importancia de la recolección oportuna de datos. Actualmente la tecnología nos da un gran soporte para esta tarea porque se cuenta con redes de sensores que, al estar en el ambiente, permiten recolectar grandes cantidades de datos. Si estos datos son procesados de forma adecuada, pueden ayudar en la detección temprana de focos de contaminación. No obstante, hay que enfrentar el problema de manejar de forma efectiva una gran cantidad de datos provenientes de dispositivos heterogéneos, que incluyen tanto a los sensores tradicionales o motas de las redes inalámbricas de sensores, como a los sensores ubicados en teléfonos inteligentes. El manejo de esta heterogeneidad motivó a diseñar un modelo de referencia que sirviera de guía para realizar las funciones de detección, almacenamiento, procesamiento y visualización/publicación de los datos, usando las tecnologías más adecuadas. La idea es que este modelo de referencia pueda posteriormente ser instanciado con componentes de hardware y software específicos, que respondan a las necesidades de determinadas empresas o entes gubernamentales, interesados en tomar acciones preventivas o correctivas sobre la contaminación ambiental. En este artículo se presenta el modelo de referencia propuesto y se describe un prototipo que instancia este modelo. El prototipo  desarrollado está enfocado en la medición de la contaminación auditiva en una empresa del sector automotriz en Bogotá. La información obtenida con el prototipo fue utilizada para tomar medidas preventivas para el control de focos de contaminación  auditiva por parte de la empresa.&nbsp

WikiSensing: A collaborative sensor management system with trust assessment for big data

Big Data for sensor networks and collaborative systems have become ever more important in the digital economy and is a focal point of technological interest while posing many noteworthy challenges. This research addresses some of the challenges in the areas of online collaboration and Big Data for sensor networks. This research demonstrates WikiSensing (www.wikisensing.org), a high performance, heterogeneous, collaborative data cloud for managing and analysis of real-time sensor data. The system is based on the Big Data architecture with comprehensive functionalities for smart city sensor data integration and analysis. The system is fully functional and served as the main data management platform for the 2013 UPLondon Hackathon. This system is unique as it introduced a novel methodology that incorporates online collaboration with sensor data. While there are other platforms available for sensor data management WikiSensing is one of the first platforms that enable online collaboration by providing services to store and query dynamic sensor information without any restriction of the type and format of sensor data. An emerging challenge of collaborative sensor systems is modelling and assessing the trustworthiness of sensors and their measurements. This is with direct relevance to WikiSensing as an open collaborative sensor data management system. Thus if the trustworthiness of the sensor data can be accurately assessed, WikiSensing will be more than just a collaborative data management system for sensor but also a platform that provides information to the users on the validity of its data. Hence this research presents a new generic framework for capturing and analysing sensor trustworthiness considering the different forms of evidence available to the user. It uses an extensible set of metrics that can represent such evidence and use Bayesian analysis to develop a trust classification model. Based on this work there are several publications and others are at the final stage of submission. Further improvement is also planned to make the platform serve as a cloud service accessible to any online user to build up a community of collaborators for smart city research.Open Acces