By Alessandra De Paola (auth.), Salvatore Gaglio, Giuseppe Lo Re (eds.)
The identify of this publication is a pun at the use of the preposition “onto” with the purpose of recalling “Ontology”, the time period often followed within the desktop technology neighborhood to point the research of the formal specification for organizing knowledge.
In the sphere of information engineering, Ontologies are used for modeling thoughts and relationships on a few domain.
The yr 2013 celebrates the 20 th anniversary of the realm vast net. the straightforward community of hypermedia has reworked the area of communications with huge, immense implications at the social relationships. notwithstanding, conventional world-wide-web is at the moment experiencing a hard evolution towards the web of items (IoT), at the present time possible due to the mixing of pervasive applied sciences in a position to sensing the environment.
The most crucial contribution of IoT regards the potential for permitting extra effective machine-to-machine cooperation. To such goal, ontologies characterize the main compatible software to let move and comprehension of knowledge between laptop purposes, even these designed and built by means of unrelated humans in several places.
This publication proposes a set of contributions illustrating diverse functions following those instructions and which are the results of actual reviews built within the context of study projects.
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Additional resources for Advances onto the Internet of Things: How Ontologies Make the Internet of Things Meaningful
8 Light101 relevant events: the curve represents the trend of light, while squares represent the recognized events Fig. 9 Probabilist reasoning on a recognized event Acknowledgments This work has been partially supported by the PO FESR 2007/2013 grant G73F11000130004 funding the SmartBuildings project. Detection of User Activities in Intelligent Environments 31 Fig. 10 Relation between users presence and temperatures and lights values in a day References 1. : A survey on sensor networks. IEEE Commun.
In a home scenario, an AmI system controls the environment according to a user’s lifestyle and daily routine. To achieve this goal, one fundamental task is to recognize the user’s activities in order to generate his daily activities profile. In this chapter, we present a simple AMI system for a home scenario to recognize and predict users’ activities. With this predictive capability, it is possible to anticipate their actions and improve their quality of life. Our approach uses a Hidden Markov Model (HMM) to recognize activities and deal with the intrinsic uncertainty of sensory information.
The experimental results are reported in Sect. 4 and finally, conclusions are discussed in Sect. 5. 2 Related Works Activity recognition is a growing research area and many different approaches have been proposed. Many papers in the literature suggest vision-based video processing techniques to recognize activities. However, the use of a camera could represent a privacy violation, because people generally do not agree to be monitored by cameras, especially at their own homes. A survey of vision-based approaches for activity recognition can be found in [19, 20].