A scalable Bluetooth Low Energy approach to identify occupancy patterns and profiles in office spaces

Tekler, Zeynep Duygu; Low, Raymond; Gunay, H. Burak; Andersen, Rune Korsholm; Blessing, Luciënne

doi:10.1016/j.buildenv.2020.106681

Cited by 140 publications

(102 citation statements)

References 27 publications

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“…This technology has been widely adopted among mobile device manufacturers, where it enables phones to communicate with small task-specific devices, such as Bluetooth headsets. Compared to ordinary Bluetooth, BLE requires considerably less power and is optimized for transferring small data packages (Tekler et al, 2020). Common deployments of BLE for indoor positioning require a number of beacons or tags to be placed in the location of interest.…”

Section: Bluetooth Low Energymentioning

confidence: 99%

“…The server has a list of predefined fingerprints, corresponding to specific locations with the associated RSSI received at that spot. The k-nearest neighbour algorithm is often used to establish at which fingerprint the user is located (Tekler et al, 2020). Another method is to have the device estimate its position based on the signals received from beacons, however this is commonly applied for indoor localization systems instead.…”

Section: Bluetooth Low Energymentioning

confidence: 99%

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Where to Go? Smart Guidance Based on Iot Sensor-Data

Effing¹,

Brouwer²,

Iliev³

et al. 2020

Proceedings of the International Conferences on ICT, Society and Human Beings (ICT 2020), Connected Smart Cities (CSC 2020) An

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There is an increasing interest in indoor occupation and guidance information for business and societal purposes. Scientific literature has paid attention to various ways of detecting occupation using different sensors as data source including various algorithms for estimating occupation rates from this data. Gaining meaningful insights from the data still faces challenges because the potential benefits are not well understood. This study presents a proof-of-concept of an indoor occupation information system, following the design science methodology. We review various types of sensor data that are typically available or easy-to-install in buildings such as offices, classrooms and meeting rooms. This study contributes to current research by incorporating business requirements taken from expert interviews and tackling one of the main barriers for business by designing an affordable system on a common existing infrastructure. We believe that occupation information systems call for further research, in particular also in the context of social distancing because of covid19.

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Section: Bluetooth Low Energymentioning

confidence: 99%

Section: Bluetooth Low Energymentioning

confidence: 99%

Where to Go? Smart Guidance Based on Iot Sensor-Data

Effing¹,

Brouwer²,

Iliev³

et al. 2020

Proceedings of the International Conferences on ICT, Society and Human Beings (ICT 2020), Connected Smart Cities (CSC 2020) An

View full text Add to dashboard Cite

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“…Also, radio-frequency identification devices including portable sensors (Fierro et al, 2012;Lazik et al, 2015), embedded Wi-Fi (Zeng et al, 2016;Lee et al, 2020) and Bluetooth beacons (Feldmann et al, 2003;Lazik et al, 2015) have been used for occupant localization. However, these sensing techniques require clear spaces and a dense deployment of sensors to provide accurate localization of occupants and above all undermine the privacy of building occupants (Tekler et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…A limitation of CO 2 -based approaches has been related to the slow spreading of CO 2 within an indoor space. Also, air ventilation may compromise interpretation of CO 2 concentrations inside buildings, leading to ambiguous results related to occupancy levels (Tekler et al, 2020). Acoustic-based methods have been found to be sensitive to ambient audible noise (Alpert and Allen, 2010;Geiger et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Uncertainties in Structural Behavior for Model-Based Occupant Localization Using Floor Vibrations

Drira

Pai

Smith

2021

Front. Built Environ.

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In sensed buildings, information related to occupant movement helps optimize functions such as security, energy management, and caregiving. Due to privacy needs, non-intrusive sensing approaches for tracking occupants inside buildings, such as vibration sensors, are often preferred over intrusive strategies that involve use of cameras and wearable devices. Current sensor-based occupant-localization approaches are data-driven techniques that do not account for structural behavior and limited to slabs on grade. Varying-rigidity floors and inherent variability in walking gaits lead to ambiguous interpretations of floor vibrations when performing model-free occupant localization. In this paper, an extensive analysis of vibrations induced by a range of occupants is described. Firstly, the need for a structural-behavior model for occupant localization is assessed using two full-scale case studies. Structural behavior is found to significantly influence floor vibrations induced by footstep impacts. Since a simple relationship between distances from footstep-impact to sensor locations cannot be assured, the use of physics-based models is necessary for accurate occupant localization. Secondly, measured data are interpreted using physics-based models and information related to uncertainties from multiple sources. There are two types of uncertainties: modelling uncertainties and measurement uncertainties, including variability in walking gaits. Error-domain model falsification (EDMF) and residual minimization (RM) are model-based approaches for data interpretation. Unlike RM, EDMF explicitly accounts for the presence of systematic errors in parameters and overall model bias. In this paper, model-based occupant localization is carried out using EDMF and RM on a full-scale case study. By explicitly accounting for the presence of uncertainties and the influence of structural behavior, EDMF, unlike RM, accurately reveals possible occupant locations on floor slabs.

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