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Aggregate Programming for Customized Building Management and Users Preference Implementation

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IoT Edge Solutions for Cognitive Buildings

Part of the book series: Internet of Things ((ITTCC))

Abstract

Cognitive buildings are equipped with sensors and actuators to customize the indoor conditions according to users’ needs and preferences. The eLUX lab, at the Smart Campus of the University of Brescia (Italy), is the first national cognitive building where educational spaces are monitored and dashboards promote the users’ awareness. There, a fixed IoT network allows gathering data to perform analytics for prompt fault detection and fine-tuning of the conditions and possibly of the energy management. However, the users’ involvement is paramount for correct tuning and customization, as indoor conditions can be measured by a sensor (e.g., temperature, humidity, illuminance, CO2 concentration) located in a single point in the indoor space, while the parameters can strongly vary in the presence of, e.g., a fan coil or the distance from the windows and doors. To address this issue, an RTLS (Real-Time Location System) can be connected to the users’ direct and indirect feedback to support a precise definition of the indoor conditions and boost the improvement of the cognitive behaviour (i.e., automatic window opening or lighting dimming). In this chapter, we illustrate how the eLUX Lab can be enhanced to support the aggregate programming paradigm for offering resilient distributed services (exploiting an RTLS) that run on wearable devices without relying on the connection to a central server.

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Notes

  1. 1.

    See https://elux.unibs.it/.

  2. 2.

    See https://elux.unibs.it/project-s-cu-o-l-a/.

  3. 3.

    See http://es3.unibs.it/elux/.

  4. 4.

    In Field Calculus, a neighbouring field always has at least a value for the current node; thus, folding is well-defined.

  5. 5.

    See https://github.com/contiki-ng/contiki-ng.

  6. 6.

    https://github.com/fcpp-experiments/room-users-control.

  7. 7.

    The exact formula for the probability of communication failure pr depending on the relative distance r has been experimentally interpolated by the formula \(p_r = [(\frac {6792093}{29701})^{\frac {r_{50} - r}{r_{99} - r_{50}}} \cdot 7 + 1 ]^{-\frac {1}{3}}\), where r99 = 4m and r50 = 3.2m.

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Authors and Affiliations

  1. Università di Torino, Torino, Italy

    Giorgio Audrito, Ferruccio Damiani, Lavinia Chiara Tagliabue, Lorenzo Testa & Gianluca Torta

  2. Università di Brescia, Brescia, Italy

    Stefano Rinaldi

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  2. ICAR-CNR, Rende, Cosenza, Italy

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  3. ICAR-CNR, Rende, Cosenza, Italy

    Andrea Vinci

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Audrito, G., Damiani, F., Rinaldi, S., Tagliabue, L.C., Testa, L., Torta, G. (2023). Aggregate Programming for Customized Building Management and Users Preference Implementation. In: Cicirelli, F., Guerrieri, A., Vinci, A., Spezzano, G. (eds) IoT Edge Solutions for Cognitive Buildings. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-031-15160-6_7

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