Occupancy Counting With Burst and Intermittent Signals in Smart Buildings

Çiftler, Bekir Sait; Dikmese, Sener; Güvenç, İsmail; Akkaya, Kemal; Kadri, Abdullah

doi:10.1109/jiot.2017.2756689

Cited by 47 publications

(31 citation statements)

References 31 publications

(33 reference statements)

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“…Since the coefficients in (12) do not depend on the exact expected error of the models, we will refer to them as the fixed coefficients in the rest of the paper. The fixed coefficients in (12) do not use all the information about how accurate each model is. Since we know the expected errors for each model, we can use this information to calculate better η(j) coefficients.…”

Section: Coefficients Of the Adaptive Filtermentioning

confidence: 99%

Adaptive Kalman Tracking for Indoor Visible Light Positioning

Eroglu

Erden

Güvenç

2019

MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)

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Visible light communication (VLC) utilizes lightemitting diodes (LEDs) to transmit wireless data. A VLC network can also be used to localize mobile users in indoor environments, where the global positioning system (GPS) signals are weak. However, the line-of-sight (LOS) links of mobile VLC devices can be blocked easily, which decreases the accuracy of localization. In this paper, we study tracking a VLC user when the availability of VLC access point (AP) link changes over the user's route. We propose a localization method for a single available AP and use known estimation methods when a larger number of APs are available. Tracking mobile users with Kalman filter can increase the accuracy of the positioning, but the generic Kalman filter does not consider instant changes in the measurement method. In order to include this information in the position estimation, we implement an adaptive Kalman filter by modifying the filter parameters based on the availability of APs to the user. Simulation results show that the implemented method decreases the root-mean-square error (RMSE) of the localization down to 30%-50% of the original estimation. Index Terms-Adaptive Kalman filter, light-fidelity (Li-Fi), localization, optical wireless communications (OWC), visible light communications (VLC).

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Section: Coefficients Of the Adaptive Filtermentioning

confidence: 99%

Adaptive Kalman Tracking for Indoor Visible Light Positioning

Eroglu

Erden

Güvenç

2019

MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)

Self Cite

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“…This offers the opportunity to obtain location information related to mobile users. Acquiring such information is useful in a diverse range of applications such as occupancy estimation [5]- [7], traffic flow monitoring [8], crowd mobility analysis [9]- [13] and building management optimization [14]- [16].…”

Section: Introductionmentioning

confidence: 99%

A Case Study of WiFi Sniffing Performance Evaluation

Barthélemy

Sun³

et al. 2020

IEEE Access

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Mobile devices regularly broadcast WiFi probe requests in order to discover available proximal WiFi access points for connection. A probe request, sent automatically in the active scanning mode, consisting of the MAC address of the device expresses an advertisement of its presence. A realtime wireless sniffing system is able to sense WiFi packets and analyse wireless traffic. This provides an opportunity to obtain insights into the interaction between the humans carrying the mobile devices and the environment. Susceptibility to loss of the wireless data transmission is an important limitation on this idea, and this is complicated by the lack of a standard specification for real deployment of WiFi sniffers. In this paper, we present an experimental analysis of sniffing performance under different wireless environments using off-the-shelf products. Our objective is to identify the possible factors including channel settings and access point configurations that affect sniffing behaviours and performances, thereby enabling the design of a protocol for a WiFi sniffing system under the optimal monitoring strategy in a real deployment. Our preliminary results show that four main factors affect the sniffing performance: the number of access points and their corresponding operating channels, the signal strength of the access point and the number of devices in the vicinity. In terms of a real field deployment, we propose assignment of one sniffing device to each specific sub-region based on the local access point signal strength and coverage area and fixing the monitoring channel belongs to the local strongest access point.

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“…Multiple researchers have proposed methods to leverage Wi-Fi infrastructure to infer occupant count [33], [34], [35], [36], [37], [38]. Despite the rapid technology development and promising application potential, the reported methods using Wi-Fi data to infer occupant count have two limitations: (1) some technologies require installing extra apps on the Access Point or end-use devices [33], [34], [37], [38]; and (2) the other require recording the MAC addresses of connecting devices [35], [36], which would raise privacy concerns. For instance, Wang et al applied location filter and MAC address filter to enhance detection accuracy, which needs to record the calibrated Received Signal Strength and MAC address [39].…”

Section: Introductionmentioning

confidence: 99%

Inferring occupant counts from Wi-Fi data in buildings through machine learning

Wang

Hong

Piette

et al. 2019

Building and Environment

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An important approach to curtail building energy consumption is to optimize building control based on occupancy information. Various studies proposed to estimate occupant counts through different approaches and sensors. However, high cost and privacy concerns remain as major barriers, restricting the practice of occupant count detection. In this study, we propose a novel method utilizing data from widely deployed Wi-Fi infrastructure to infer occupant counts through machine learning. Compared with the current indirect measurement methods, our method improves the performance of estimating people count: (1) we avoid privacy concerns by anonymizing and reshuffling the MAC addresses on a daily basis; (2) we adopted a heuristic feature engineer approach to cluster connected devices into different types based on their daily connection duration. We tested the method in an office building located in California. In an area with an average occupancy of 22-27 people and a peak occupancy of 48-74 people, the root square mean error on the test set is less than four people. The error is within two people counts for more than 70% of estimations, and less than six counts for more than 90% of estimations, indicating a relatively high accuracy. The major contribution of this study is proposing a novel and accurate approach to detect occupant counts in a non-intrusive way, i.e., utilizing existing Wi-Fi infrastructure in buildings without requiring the installation of extra hardware or sensors. The method we proposed is generic and could be applied to other commercial buildings to infer occupant counts for energy efficient building control.

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Occupancy Counting With Burst and Intermittent Signals in Smart Buildings

Cited by 47 publications

References 31 publications

Adaptive Kalman Tracking for Indoor Visible Light Positioning

Adaptive Kalman Tracking for Indoor Visible Light Positioning

A Case Study of WiFi Sniffing Performance Evaluation

Inferring occupant counts from Wi-Fi data in buildings through machine learning

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