Smart Spaces for Ubiquitously Smart Buildings

Främling, Kary; Oliver, Ian; Honkola, Jukka; Nyman, Jan

doi:10.1109/ubicomm.2009.62

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…Other case studies include a building automation case [4], a smart meeting room case [5], a home sensor network case [12], and a health care case developed by University of Bologna. 3 KPs may communicate over other shared communication mechanisms.…”

Section: Case Studies Using Smart-m3mentioning

confidence: 99%

Smart-M3 information sharing platform

Honkola

Laine

Brown

et al. 2010

The IEEE Symposium on Computers and Communications

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204

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Section: Case Studies Using Smart-m3mentioning

confidence: 99%

Smart-M3 information sharing platform

Honkola

Laine

Brown

et al. 2010

The IEEE Symposium on Computers and Communications

Self Cite

204

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“…management, among all kinds of application scenarios, benefits extensively from such a sensing infrastructure and inspires a variety of useful services regarding heating, cooling, illumination, safety and many other purposes. Residing on top of the sensing layer, service providers, either in-building or cloud-based, can be aware of building status and conditions through sensor measurements, and react properly and automatically to environmental variations, essentially creating a smart building [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive User Tracking via Passive Sensing

Wang

Tague

2014

Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop

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A large-scale sensing infrastructure can collect ample data to benefit many real-world applications. One promising application scenario is building management. However, exposure of the sensor data potentially reveals private details about building users. In this paper, we investigate indoor location privacy as a motivating example to manifest potential privacy risks in smart buildings. We apply inference techniques to reconstruct users' location traces from room-level occupancy data. Unlike other types of surveillance that are dedicated to explicit tracking such as security cameras, time-series occupancy traces, as aggregated environmental measurements, are typically deemed privacy-preserving. Unfortunately, it may still reveal some of the same sensitive information as privacy-invasive sensing such as video surveillance. We conduct experiments using a publicly available dataset and synthetic data. Our results demonstrate the underlying privacy leakage via occupancy data. We further show how our evaluation can enable adaptive privacy mechanisms to control the information leakage by the sensing system.

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“…The future trend is to increase the number of nodes, in order to increase the interaction with users as well as capturing more information from the surrounding environment. On the other hand, the use of building automation systems, especially oriented to home automation, is growing all over the world [17][18][19][20][21][22]. As time goes by more homes integrate an automation system for intelligently controlling elements such light, heating, and energy management.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of IEEE 802.15.4 Compliant Wireless Devices for Heterogeneous Indoor Home Automation Environments

Nazabal

López-Iturri

Azpilicueta

et al. 2012

International Journal of Antennas and Propagation

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The influence of topology as well as morphology of complex indoor scenarios in the deployment of wireless sensor networks and wireless systems applied to home and building automation systems is analyzed. The existence of loss mechanisms such as material absorption (walls, furniture, etc.) and strong multipath components as well as the increase in the number of wireless sensors within indoor scenarios increases the relevance in the configuration of the heterogeneous wireless systems. Simulation results by means of empirical-based models are compared with an in-house 3D ray launching code as well as measurement results from wireless sensor networks illustrate the strong influence of the indoor scenario in the overall performance. The use of adequate radioplanning strategies lead to optimal wireless network deployments in terms of capacity, quality of service, and reduced power consumption.

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Smart Spaces for Ubiquitously Smart Buildings

Abstract: Abstract

Cited by 10 publications

References 13 publications

Smart-M3 information sharing platform

Smart-M3 information sharing platform

Non-Invasive User Tracking via Passive Sensing

Performance Analysis of IEEE 802.15.4 Compliant Wireless Devices for Heterogeneous Indoor Home Automation Environments

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