Internet of Things (IoT) is expected to substantially support sustainable developments of future smart cities. However, the heterogeneity among connected objects (i.e., things) and the unreliable nature of their associated services may prevent IoT from playing this crucial role. To enable the possibility of dynamically (re-)configuring real world objects at run-time, we propose to represent them as Virtual Objects (VOs). VOs are semantic descriptions of physical objects and of the phenomena they observe and include software modules to expose the object functionalities as IoT services. In order to provide self configuration functionality at VO level, we propose to use a cognitive management framework that wisely tunes key application parameters. Finally, we present a practical environmental monitoring application exploiting wireless sensor nodes, to show how the proposed cognitive framework can be useful to select the most appropriate compression algorithm so as to reduce the overall energy consumption of the sampling devices.
Reconfiguration of environmental data compression parameters through cognitive IoT technologies
VECCHIO, MASSIMO;
2013-01-01
Abstract
Internet of Things (IoT) is expected to substantially support sustainable developments of future smart cities. However, the heterogeneity among connected objects (i.e., things) and the unreliable nature of their associated services may prevent IoT from playing this crucial role. To enable the possibility of dynamically (re-)configuring real world objects at run-time, we propose to represent them as Virtual Objects (VOs). VOs are semantic descriptions of physical objects and of the phenomena they observe and include software modules to expose the object functionalities as IoT services. In order to provide self configuration functionality at VO level, we propose to use a cognitive management framework that wisely tunes key application parameters. Finally, we present a practical environmental monitoring application exploiting wireless sensor nodes, to show how the proposed cognitive framework can be useful to select the most appropriate compression algorithm so as to reduce the overall energy consumption of the sampling devices.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
