Download Advances onto the Internet of Things: How Ontologies Make by Alessandra De Paola (auth.), Salvatore Gaglio, Giuseppe Lo PDF

By Alessandra De Paola (auth.), Salvatore Gaglio, Giuseppe Lo Re (eds.)

ISBN-10: 3319039911

ISBN-13: 9783319039916

ISBN-10: 331903992X

ISBN-13: 9783319039923

The name of this ebook is a pun at the use of the preposition “onto” with the purpose of recalling “Ontology”, the time period ordinarily followed within the desktop technology neighborhood to point the examine of the formal specification for organizing knowledge.

In the sphere of data engineering, Ontologies are used for modeling strategies and relationships on a few domain.

The yr 2013 celebrates the 20th anniversary of the realm broad net. the easy community of hypermedia has remodeled the realm of communications with huge, immense implications at the social relationships. even if, conventional world-wide-web is at the moment experiencing a demanding evolution towards the net of items (IoT), at the present time possible because of the mixing of pervasive applied sciences able to sensing the environment.

The most crucial contribution of IoT regards the potential for allowing extra effective machine-to-machine cooperation. To such objective, ontologies signify the main appropriate device to allow move and comprehension of knowledge between computing device purposes, even these designed and constructed via unrelated humans in several places.

This ebook proposes a set of contributions illustrating various functions following those instructions and which are the results of genuine stories built within the context of analysis projects.

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Extra resources for Advances onto the Internet of Things: How Ontologies Make the Internet of Things Meaningful

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However, when there are lots of input variables and the membership functions are complex, the method is particularly difficult to implement. A similar technique is used in [2], in which Chang and Chen initially determine the fuzzy rules using the C-Means algorithm, and the inferences are calculated through an interpolation scheme by a reassignment of the weights. As described in [12], rules can be learned through a reinforcement algorithm such as Q-Learning. This technique creates a table where each configuration is rewarded or penalized according to the approach of the target system.

In: Special Issue on Uncertainty-Based Technologies for Ambient Intelligence Systems, vol. 2, Issue number 2, May (2008) 6. : Sensor-based occupancy behavioral pattern recognition for energy and comfort management in intelligent buildings. In: Proceedings of Building Simulation ’2009, an IBPSA Conference. K. (2009) 7. : Saving, home energy, through a user profiling system based on wireless sensors. In: ACM Buildsys, : (in conjunction with SenSys 2009), Berkeley, CA. Nov. 3, (2009) 8. : A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments.

Whenever rules are generated, they are transferred to the controllers. Thus in our solution, we designed two different ad-hoc controllers, the first being responsible for the regulation of temperature and humidity and the second for the management of lighting. 1 Fuzzy Controllers Fuzzy logic differs from classical Boolean logic in that it can attribute to each proposition a degree of truth between 0 and 1. Properties are represented by functions called membership functions. Fuzzy logic is the basis of our Controller, which constitutes the reactive component of our system, and implements mapping between the actual sensory values and the actions to be performed.

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Advances onto the Internet of Things: How Ontologies Make the Internet of Things Meaningful by Alessandra De Paola (auth.), Salvatore Gaglio, Giuseppe Lo Re (eds.)

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