COLLECT: COLLaborativE ConText-aware service oriented architecture for intelligent decision-making in the Internet of Things
Metrics and citations
MetadataShow full item record
DepartmentIngeniería de Sistemas y Automática, Tecnología Electrónica y Electrónica; Ingeniería Informática
SourceGarcia-de-Prado, A., Ortiz, G., & Boubeta-Puig, J. (2017). COLLECT: COLLaborativE ConText-aware service oriented architecture for intelligent decision-making in the Internet of Things. Expert Systems with Applications, 85, 231-248. https://doi.org/10.1016/j.eswa.2017.05.034
Internet of Things (IoT) has radically transformed the world; currently, every device can be connected to the Internet and provide valuable information for decision-making. In spite of the fast evolution of technologies accompanying the grow of IoT, we are still faced with the challenge of providing a service oriented architecture, which facilitates the inclusion of data coming together from several IoT devices, data delivery among a system’s agents, real-time data processing and service provision to users. Furthermore, context-aware data processing and architectures still pose a challenge, in spite of being key requirements in order to get stronger IoT architectures. To face this challenge, we propose a COLLaborative ConText Aware Service Oriented Architecture (COLLECT), which facilitates both the integration of IoT heterogeneous domain context data — through the use of a light message broker — and easy data delivery among several agents and collaborative participants in the system — making use of an enterprise service bus —. In addition, this architecture provides real-time data processing thanks to the use of a complex event processing engine as well as services and intelligent decision-making procedures to users according to the needs of the domain in question. As a result, COLLECT has a great impact on context-aware decentralized and collaborative reasoning for IoT, promoting context-aware intelligent decision making in such scope. Since context-awareness is key for a wide range of recommender and intelligent systems, the presented novel solution improves decision making in a large number of fields where such systems require to promptly process a variety of ubiquitous collaborative and context-aware data.