Exploiting the Human Factor in a WSN-Based System for Ambient Intelligence

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18 Citazioni (Scopus)

Abstract

Practical applications of ambient intelligence cannot leave aside requirements about ubiquity, scalability, and transparency to the user. An enabling technology to comply with this goal is represented by wireless sensor networks (WSNs); however, although capable of limited in-network processing, they lack the computational power to act as a comprehensive intelligent system. By taking inspiration from the sensory processing model of complex biological organisms, we propose here a cognitive architecture able to perceive, decide upon, and control the environment of which the system is part. WSNs act as a transparent interface that allows the system to understand human requirements through implicit feedback, and consequently adapt its behavior. A central unit will carry on symbolic reasoning based on the concepts extracted from sensory inputs collected and pre-processed by pervasively deployed WSNs.
Lingua originaleEnglish
Pagine748-753
Numero di pagine6
Stato di pubblicazionePublished - 2009

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Human engineering
Wireless sensor networks
Intelligent systems
Processing
Transparency
Scalability
Feedback
Ambient intelligence

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Software
  • Control and Systems Engineering

Cita questo

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title = "Exploiting the Human Factor in a WSN-Based System for Ambient Intelligence",
abstract = "Practical applications of ambient intelligence cannot leave aside requirements about ubiquity, scalability, and transparency to the user. An enabling technology to comply with this goal is represented by wireless sensor networks (WSNs); however, although capable of limited in-network processing, they lack the computational power to act as a comprehensive intelligent system. By taking inspiration from the sensory processing model of complex biological organisms, we propose here a cognitive architecture able to perceive, decide upon, and control the environment of which the system is part. WSNs act as a transparent interface that allows the system to understand human requirements through implicit feedback, and consequently adapt its behavior. A central unit will carry on symbolic reasoning based on the concepts extracted from sensory inputs collected and pre-processed by pervasively deployed WSNs.",
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AU - De Paola, Alessandra

AU - Gaglio, Salvatore

AU - Farruggia, Alfonso

PY - 2009

Y1 - 2009

N2 - Practical applications of ambient intelligence cannot leave aside requirements about ubiquity, scalability, and transparency to the user. An enabling technology to comply with this goal is represented by wireless sensor networks (WSNs); however, although capable of limited in-network processing, they lack the computational power to act as a comprehensive intelligent system. By taking inspiration from the sensory processing model of complex biological organisms, we propose here a cognitive architecture able to perceive, decide upon, and control the environment of which the system is part. WSNs act as a transparent interface that allows the system to understand human requirements through implicit feedback, and consequently adapt its behavior. A central unit will carry on symbolic reasoning based on the concepts extracted from sensory inputs collected and pre-processed by pervasively deployed WSNs.

AB - Practical applications of ambient intelligence cannot leave aside requirements about ubiquity, scalability, and transparency to the user. An enabling technology to comply with this goal is represented by wireless sensor networks (WSNs); however, although capable of limited in-network processing, they lack the computational power to act as a comprehensive intelligent system. By taking inspiration from the sensory processing model of complex biological organisms, we propose here a cognitive architecture able to perceive, decide upon, and control the environment of which the system is part. WSNs act as a transparent interface that allows the system to understand human requirements through implicit feedback, and consequently adapt its behavior. A central unit will carry on symbolic reasoning based on the concepts extracted from sensory inputs collected and pre-processed by pervasively deployed WSNs.

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KW - Wireless Sensor Networks

UR - http://hdl.handle.net/10447/38584

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ER -