Towards Semantic Integration of Heterogeneous Sensor Data with Indigenous Knowledge for Drought Forecasting

In the Internet of Things (IoT) domain, various heterogeneous ubiquitous devices would be able to connect and communicate with each other seamlessly, irrespective of the domain. Semantic representation of data through detailed standardized annotation has shown to improve the integration of the interconnected heterogeneous devices. However, the semantic representation of these heterogeneous data sources for environmental monitoring systems is not yet well supported. To achieve the maximum benefits of IoT for drought forecasting, a dedicated semantic middleware solution is required. This research proposes a middleware that semantically represents and integrates heterogeneous data sources with indigenous knowledge based on a unified ontology for an accurate IoT-based drought early warning system (DEWS).Comment: 5 pages, 3 figures, In Proceedings of the Doctoral Symposium of the 16th International Middleware Conference (Middleware Doct Symposium 2015), Ivan Beschastnikh and Wouter Joosen (Eds.). ACM, New York, NY, US

The MASSIF platform : a modular and semantic platform for the development of flexible IoT services

In the Internet of Things (IoT), data-producing entities sense their environment and transmit these observations to a data processing platform for further analysis. Applications can have a notion of context awareness by combining this sensed data, or by processing the combined data. The processes of combining data can consist both of merging the dynamic sensed data, as well as fusing the sensed data with background and historical data. Semantics can aid in this task, as they have proven their use in data integration, knowledge exchange and reasoning. Semantic services performing reasoning on the integrated sensed data, combined with background knowledge, such as profile data, allow extracting useful information and support intelligent decision making. However, advanced reasoning on the combination of this sensed data and background knowledge is still hard to achieve. Furthermore, the collaboration between semantic services allows to reach complex decisions. The dynamic composition of such collaborative workflows that can adapt to the current context, has not received much attention yet. In this paper, we present MASSIF, a data-driven platform for the semantic annotation of and reasoning on IoT data. It allows the integration of multiple modular reasoning services that can collaborate in a flexible manner to facilitate complex decision-making processes. Data-driven workflows are enabled by letting services specify the data they would like to consume. After thorough processing, these services can decide to share their decisions with other consumers. By defining the data these services would like to consume, they can operate on a subset of data, improving reasoning efficiency. Furthermore, each of these services can integrate the consumed data with background knowledge in its own context model, for rapid intelligent decision making. To show the strengths of the platform, two use cases are detailed and thoroughly evaluated

Switch: a middleware for the development of IOT applications with voice-based interfaces

Internet se está desarrollando como un nuevo paradigma conocido como Internet de las Cosas (en inglés, Internet of Things - IoT) donde las personas y cosas cotidianas se conectan a Internet. Las cosas necesitan de interfaces digitales para facilitar la comunicación entre humanos y maquinas. Las interfaces (mundo virtual) se deben proporcionar haciendo uso de una amplia gama de aplicaciones que abordan necesidades específicas de los diferentes dominios de aplicación. Sin embargo, al ser IoT un paradigma complejo, el desarrollo de estas aplicaciones se convierte en un desafío tecnológico. Actualmente, IoT está impactando la forma como se vive, pero la interacción entre hombre-máquina y máquina-máquina todavía está lejos de ser no intrusiva para el ser humano debido a que no se relacionan de manera natural. Para lograr esto, es necesario hacer uso de las capacidades básicas humanas como por ejemplo la voz, la cual ocurre naturalmente, pero aún no es ampliamente utilizada como parte del paradigma IoT. Con base en lo anterior, se propuso el diseño de SWITCH, una plataforma middleware con potencial de investigación que oculta la complejidad en el desarrollo de aplicaciones IoT, abordando los requisitos funcionales y no funcionales básicos que IoT demanda. SWITCH contiene módulos para el reconocimiento del habla, los cuales a través de las aplicaciones proveen interfaces de voz a los usuarios para facilitar la interacción natural con las cosas cotidianas.1. INTRODUCCIÓN 15 1.1 PROBLEMA DE INVESTIGACIÓN 20 1.1.1 Complejidad en el desarrollo de aplicaciones IoT 20 1.1.2 Las cosas no tienen interfaces digitales 21 1.2 MOTIVACIÓN 21 1.3 PREGUNTA E HIPÓTESIS DE INVESTIGACIÓN 24 1.4 OBJETIVOS 24 1.5 ORGANIZACIÓN DEL DOCUMENTO 25 2 MARCO REFERENCIAL 26 2.1 MARCO CONCEPTUAL 26 2.2 MARCO TEÓRICO 28 2.2.1 Ingeniería del software 29 2.2.2 Internet de las Cosas 30 2.2.3 Middleware 32 2.2.4 Reconocimiento del habla 32 2.3 ESTADO DEL ARTE 35 2.3.1 Planeación. 35 2.3.2 Conducción. 36 2.3.3 Reporte. 37 2.4 MARCO CONTEXTUAL 46 2.5 MARCO LEGAL Y POLÍTICO 47 2.5.1 ISO/IEC/IEEE 24765:2010(E) 47 2.5.2 ISO/IEC 25010:2011 48 2.6 CONSIDERACIONES FINALES DEL CAPÍTULO 48 3 ASPECTOS METODOLÓGICOS 50 3.1 TIPO Y ENFOQUE DE INVESTIGACIÓN 50 3.2 TÉCNICAS E INSTRUMENTOS DE RECOLECCIÓN DE INFORMACIÓN 50 3.3 FASES Y ACTIVIDADES 51 3.3.1 Fase 1: Análisis 51 3.3.2 Fase 2: Modelado 52 3.3.3 Fase 3: Evaluación 53 4 ANÁLISIS DE REQUISITOS 54 4.1 ARQUITECTURAS DE REFERENCIA PARA IOT 54 4.1.1 Planeación 54 4.1.2 Conducción. 55 4.1.3 Reporte 60 4.1.4 Conclusiones de las arquitecturas de referencia para IoT 72 4.2 ARQUITECTURAS MIDDLEWARE PARA IOT 73 4.2.1 Middleware basado en eventos 73 4.2.2 Middleware orientado a servicios 74 4.2.3 Middleware basado en agentes 74 4.2.4 Middleware basado en la nube 75 4.2.5 Middleware basado en actores 76 4.2.6 Conclusiones de las arquitecturas de referencia para IoT 76 4.3 SISTEMAS PARA EL RECONOCIMIENTO DEL HABLA - ASR 77 4.3.1 Planeación. 77 4.3.2 Conducción 78 4.3.3 Reporte 78 4.3.4 Conclusiones de los sistemas para el reconocimiento del habla 85 4.4 REQUISITOS FUNCIONALES Y NO FUNCIONALES DE UN MIDDLEWARE GENÉRICO PARA IOT 87 4.4.1 Requisitos funcionales 87 4.4.2 Requisitos no funcionales 89 4.5 REQUISITOS FUNCIONALES Y NO FUNCIONALES DEL MIDDLEWARE SWITCH 93 5 MODELADO DE LOS REQUISITOS DE SWITCH 97 5.1 MODELADO DEL DOMINIO DE SWITCH 97 5.1.1 Conceptos del modelado del dominio 98 5.1.2 Relaciones del modelado del dominio 99 5.2 ARQUITECTURA DE SWITCH 101 5.3 MODELADO DE LOS COMPONENTES DEL SOFTWARE 103 5.3.1 Vista funcional de SWITCH 103 5.3.2 Vista de servicios de SWITCH 107 5.3.3 Vista de procesos de SWITCH 109 5.3.4 Interfaz gráfica de usuario 113 5.4 MODELADO DE LOS COMPONENTES DEL HARDWARE 117 6 EVALUACIÓN DEL DISEÑO DE SWITCH 119 6.1 PRUEBA DE CONCEPTO 119 6.2 ANÁLISIS COMPARATIVO 122 6.3 INSTRUMENTO DE EVALUACIÓN 124 7. CONCLUSIONES 131 7.1 CONTRIBUCIONES REALIZADAS 132 7.2 TRABAJO FUTURO 133 REFERENCIAS 134MaestríaInternet is being developed as a new paradigm known as Internet of things where people and daily things are connecting to Internet. Things need digital interfaces to facilitate communication between human-machine. The interfaces (virtual world) must be provided making use of a wide range of applications that address specific needs for different domains. However, since IoT is a complex paradigm, the development of these applications becomes a challenging task. Currently, IoT is impacting the way how we live but the interaction between humanmachine and machine-machine is still far from being non-intrusive for people because they are not related in a natural way. To achieve this concern, it is necessary to make use of basic human capabilities such as voice, which occurs naturally, but is not yet widely used as part of the IoT paradigm. Based on the above, the SWITCH design was proposed, a middleware platform with research potential for hiding the complexity in the development of IoT applications, addressing the basic functional and non-functional requirements that IoT demands. SWITCH contains modules for speech recognition for providing voice interfaces to facilitate natural interaction with things

Ontology-based context-aware model for event processing in an IoT environment

The Internet of Things (IoT) is more and more becoming one of the fundamental sources of data. The observations produced by these sources are made accessible with heterogeneous vocabularies, models and data formats. The heterogeneity factor in such an enormous environment complicates the task of sharing and reusing this data in a more intelligent way (other than the purposes it was initially set up for). In this research, we investigate these challenges, considering how we can transform raw sensor data into a more meaningful information. This raw data will be modelled using ontology-based information that is accessible through continuous queries for sensor streaming data.Interoperability among heterogeneous entities is an important issue in an IoT environment. Semantic modelling is a key element to support interoperability. Most of the current ontologies for IoT mainly focus on resources and services information. This research builds upon the current state-of-the-art ontologies to provide contextual information and facilitate sensor data querying. In this research, we present an Ontology to represent an IoT environment, with emphasis on temporal and geospatial context enrichment. Furthermore, the Ontology is used alongside a proposed syntax based on Description Logic to build an Event Processing Model. The aim of this model is to interconnect ontology-based reasoning with event processing. This model enables to perform event processing over high-level ontological concepts.The Ontology was developed using the NeOn methodology, which emphasises on the reuse and modularisation. The Competency Questions techniques was used to develop the requirements of this Ontology. This was later evaluated by domain experts in software engineering and cloud computing. The ontology was evaluated based on its completeness, conciseness, consistency and expandability, over 70% of the domain experts agreed on the core modules, concepts and relationships within the ontology. The resulted Ontology provides a core IoT ontology that could be used for further development within a specific IoT domain. IIThe proposed Ontology-Based Context-Aware model for Event-Processing in an IoT environment “OCEM-IoT”, implements all the time operators used in complex event processing engines. Throughput and latency were used as performance comparison metrics for the syntax evaluation; the results obtained show an improved performance over existing event processing languages