The growing use of sensors in smart environments applications like smart homes, hospitals, public transportation, emergency services, education, and workplaces not only generates constantly increasing of sensor data, but also rises the complexity of integration of heterogeneous data and hardware devices. Existing infrastructures should be reused under different application domain requirements, applications should be able to manage data coming from different devices without knowing the intrinsic characteristics of the sensing devices, and, finally, the introduction of new devices should be completely transparent to the existing applications. The paper proposes a set of architectural abstractions aimed at representing sensors' measurements that are independent from the sensors' technology. Such a set can reduce the effort for data fusion and interpretation, moreover it enforces both the reuse of existing infrastructure and the openness of the sensing layer by providing a common framework for representing sensors' readings. The abstractions rely on the concepts of space. Data is localized both in a positing and in a measurement space that are subjective with respect to the entity that is observing the data. Mapping functions allow data to be mapped into different spaces so that different entities relying on different spaces can reason on data.

Micucci, D., Mobilio, M., Tisato, F. (2016). Spaces: Subjective spaces architecture for contextualizing heterogeneous sources. In 10th International Joint Conference on Software Technologies, ICSOFT 2015; Colmar; France; 20-22 July 2015 (pp. 415-429). Springer Verlag [10.1007/978-3-319-30142-6_23].

Spaces: Subjective spaces architecture for contextualizing heterogeneous sources

MICUCCI, DANIELA
Primo
;
MOBILIO, MARCO
Secondo
;
TISATO, FRANCESCO
Ultimo
2016

Abstract

The growing use of sensors in smart environments applications like smart homes, hospitals, public transportation, emergency services, education, and workplaces not only generates constantly increasing of sensor data, but also rises the complexity of integration of heterogeneous data and hardware devices. Existing infrastructures should be reused under different application domain requirements, applications should be able to manage data coming from different devices without knowing the intrinsic characteristics of the sensing devices, and, finally, the introduction of new devices should be completely transparent to the existing applications. The paper proposes a set of architectural abstractions aimed at representing sensors' measurements that are independent from the sensors' technology. Such a set can reduce the effort for data fusion and interpretation, moreover it enforces both the reuse of existing infrastructure and the openness of the sensing layer by providing a common framework for representing sensors' readings. The abstractions rely on the concepts of space. Data is localized both in a positing and in a measurement space that are subjective with respect to the entity that is observing the data. Mapping functions allow data to be mapped into different spaces so that different entities relying on different spaces can reason on data.
Capitolo o saggio
Sensor heterogeneity, modularisation, software architecture, knowledge representation
English
10th International Joint Conference on Software Technologies, ICSOFT 2015; Colmar; France; 20-22 July 2015
9783319301419
Micucci, D., Mobilio, M., Tisato, F. (2016). Spaces: Subjective spaces architecture for contextualizing heterogeneous sources. In 10th International Joint Conference on Software Technologies, ICSOFT 2015; Colmar; France; 20-22 July 2015 (pp. 415-429). Springer Verlag [10.1007/978-3-319-30142-6_23].
Micucci, D; Mobilio, M; Tisato, F
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/132432
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