Browsing by Keyword "Context awareness"
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Item BETaaS: A Platform for Development and Execution of Machine-to-Machine Applications in the Internet of Things: A Platform for Development and Execution of Machine-to-Machine Applications in the Internet of Things(2016-04-01) Vallati, Carlo; Mingozzi, Enzo; Tanganelli, Giacomo; Buonaccorsi, Novella; Valdambrini, Nicola; Zonidis, Nikolaos; Martinez-Rodriguez, Belen; Mamelli, Alessandro; Sommacampagna, Davide; Anggorojati, Bayu; Kyriazakos, Sofoklis; Prasad, Neeli; Nieto, Francisco Javier; Barreto, Oliver; Rodriguez, Oliver Barreto; Tecnalia Research & Innovation; BIGDATAThe integration of everyday objects into the Internet represents the foundation of the forthcoming Internet of Things (IoT). Such “smart” objects will be the building blocks of the next generation of applications that will exploit interaction between machines to implement enhanced services with minimum or no human intervention in the loop. A crucial factor to enable Machine-to-Machine (M2M) applications is a horizontal service infrastructure that seamlessly integrates existing IoT heterogeneous systems. The authors present BETaaS, a framework that enables horizontal M2M deployments. BETaaS is based on a distributed service infrastructure built on top of an overlay network of gateways that allows seamless integration of existing IoT systems. The platform enables easy deployment of applications by exposing to developers a service oriented interface to access things (the Things-as-a-Service model) regardless of the technology and the physical infrastructure they belong.Item Semantic-based Context Modeling for Quality of Service Support in IoT Platforms(IEEE, 2016-06-21) Mingozzi, Enzo; Tanganelli, Giacomo; Vallati, Carlo; Martinez-Rodriguez, Belen; Mendia, Izaskun; Gonzalez-Rodriguez, M.; Tecnalia Research & Innovation; BIGDATA; SGThe Internet of Things (IoT) envisions billions of devices seamlessly connected to information systems, thus providing a sensing platform for applications. The availability of such a huge number of smart things will entail a multiplicity of devices collecting overlapping data and/or providing similar functionalities. In this scenario, efficient discovery and appropriate selection of things through proper context acquisition and management will represent a critical requirement and a challenge for future IoT platforms. In this work we present a practical approach to model and manage context, and how this information can be exploited to implement QoS-aware thing service selection. In particular, it is shown how context can be used to infer knowledge on the equivalence of thing services through semantic reasoning, and how such information can be exploited to allocate thing services to applications while meeting QoS requirements even in case of failures. The proposed approach is demonstrated through a simple yet illustrative experiment in a smart home scenario.Item A statistical recommendation model of mobile services based on contextual evidences(2012-01) Picón, Artzai; Rodríguez-Vaamonde, Sergio; Jaén, Javier; Mocholi, Jose Antonio; García, David; Cadenas, Alejandro; COMPUTER_VISION; Tecnalia Research & InnovationMobile devices are undergoing great advances in recent years allowing users to access an increasing number of services or personalized applications that can help them select the best restaurant, locate certain shops, choose the best way home or rent the best film. However this great quantity of services does not require the user to find and select those services needed for each specific situation. The classical approaches link some preferences to certain services, include the recommendations given by other users or even include certain fixed rules in order to choose the most appropriate services. However, since these methods assume that user needs can be modelled by fixed rules or preferences, they fail when modelling different users or makes them difficult to train. In this paper we propose a new algorithm that learns from the user's actions in different contextual situations, which allows to properly infer the most appropriate recommendations for a user in a specific contextual situation. This model, by using of a double knowledge diffusion approach, has been specifically designed to face the inherent lack of learning evidences, computational cost and continuous training requirements and, therefore, overcomes the performance and convergence rates offered by other learning methodologies.