By Alessandra De Paola (auth.), Salvatore Gaglio, Giuseppe Lo Re (eds.)
The name of this booklet is a pun at the use of the preposition “onto” with the purpose of recalling “Ontology”, the time period normally followed within the computing device technological know-how neighborhood to point the research of the formal specification for organizing knowledge.
In the sector of information engineering, Ontologies are used for modeling strategies and relationships on a few domain.
The 12 months 2013 celebrates the 20 th anniversary of the realm large internet. the straightforward community of hypermedia has remodeled the realm of communications with huge, immense implications at the social relationships. notwithstanding, conventional world-wide-web is at present experiencing a demanding evolution towards the web of items (IoT), this present day possible due to the mixing of pervasive applied sciences able to sensing the environment.
The most crucial contribution of IoT regards the potential for permitting extra effective machine-to-machine cooperation. To such target, ontologies characterize the main compatible software to allow move and comprehension of knowledge between laptop functions, even these designed and built by means of unrelated humans in several places.
This e-book proposes a set of contributions illustrating varied purposes following those instructions and which are the results of actual reports built within the context of study projects.
Read or Download Advances onto the Internet of Things: How Ontologies Make the Internet of Things Meaningful PDF
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Additional info for Advances onto the Internet of Things: How Ontologies Make the Internet of Things Meaningful
The middleware layer handles the sensor heterogeneity, gathering data from sensors and inserting 38 S. Gaglio and G. Martorella Fig. 3 Flow diagram of the on-line learning phase. This is the initial phase which allows the sensorbased recognizer to work in unknown scenarios. User feedback is required in order to update the parameters with greater certainty. The system switches to the Adaptive phase once the current percentage of error falls below a certain threshold them in a database. Higher level modules include our sensor-based recognizer, the profiler, the camera-based recognizer and a module for interacting with the user.
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 mechanism by which they evolve over time. In particular, the time series obtained from sensor 22 A. Augello and S. Gaglio readings can be decomposed into a set of components: a trend, a seasonal and a remainder component . The trend component Tt defines the long-term trend of the variable and can be considered as the tendency to increase, decrease or remain constant over a long period of time. The seasonal or periodic component St is given by one or more periodic components, taking the same or similar values at a fixed distance in time.