Trained-once device-free crowd counting and occupancy estimation using WiFi: A Doppler spectrum based approach

Domenico, Simone Di; Pecoraro, Giovanni; Cianea, Ernestina; Sanctis, Mauro De

doi:10.1109/wimob.2016.7763227

Cited by 77 publications

(46 citation statements)

References 13 publications

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“…Currently, Wi-Fi networks with full coverage are widely installed in office buildings and play an important role in communication between a building's facility management team and its residents [22,45]. Accordingly, Wi-Fi based tracking is currently considered as the predominant and widely implemented occupancy sensing method within office buildings [46][47][48][49][50][51][52][53][54]. In particular, since office building occupants have Wi-Fi enabled devices (e.g.…”

Section: Wi-fi-based Occupancy Sensing In Office Buildingsmentioning

confidence: 99%

Extracting occupants’ energy-use patterns from Wi-Fi networks in office buildings

Rafsanjani

Ghahramani

2019

Journal of Building Engineering

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Wi-Fi networks are currently considered as an efficient and economical tool for occupancy sensing in office buildings. Studies particularly indicated that these networks could be utilized to understand/predict occupants' energy-use patterns. Despite the value that investigating this possibility could provide for the current research, it has not been well explored how energy-use pattern information could be extracted from Wi-Fi system information. In response, this study utilizes statistical analyses to investigate the correlation of Wi-Fi flows with miscellaneous electric loads (MELs) in office buildings. MELs account for more than one-third of office-building energy consumption and are the best representative of occupants' energy-use patterns. In the pursuit of the objective, data from two offices were collected over a 3-month period of time. Results from the analyses show that an average 92 percent of MELs energy consumption could be predicted through the Wi-Fi flows in a building. This finding thereby demonstrates that occupants' energyuse patterns are highly positively correlated to Wi-Fi flows in a building and accordingly, the information of Wi-Fi networks could be utilized to understand/interpret these patterns. This significantly contributes to the current body of research and can be used to support efforts into understanding/enhancing occupants' energy-use behaviors. In addition, since Wi-Fi networks are a major subset of internet of things (IoT) hardware systems and IoT implementation for intelligent energy management in buildings significantly depends on occupant energy-use patterns, this research helps IoT-based efforts by displaying how these patterns could be extracted from IoT infrastructure.

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Section: Wi-fi-based Occupancy Sensing In Office Buildingsmentioning

confidence: 99%

Extracting occupants’ energy-use patterns from Wi-Fi networks in office buildings

Rafsanjani

Ghahramani

2019

Journal of Building Engineering

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“…Each feature is a number which is somehow related to the statistics of the RSRP (Table 2) or to the "shape" or statistics of the CSI (Table 3). A similar approach has already been successfully employed in [26] to perform device-free crowd counting and occupancy estimation by WiFi.…”

Section: Csi Fingerprinting Localizationmentioning

confidence: 99%

CSI-based fingerprinting for indoor localization using LTE Signals

Pecoraro

Domenico

Cianca

et al. 2018

EURASIP J. Adv. Signal Process.

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This paper addresses the use of channel state information (CSI) for Long Term Evolution (LTE) signal fingerprinting localization. In particular, the paper proposes a novel CSI-based signal fingerprinting approach, where fingerprints are descriptors of the "shape" of the channel frequency response (CFR) calculated on CSI vectors, rather than direct CSI vectors. Experiments have been carried out to prove the feasibility and the effectiveness of the proposed method and to study the impact on the localization performance of (i) the bandwidth of the available LTE signal and (ii) the availability of more LTE signals transmitted by different eNodeB (cell diversity). Comparisons with other signal fingerprinting approaches, such as the ones based on received signal strength indicator or reference signal received power, clearly show that using LTE CSI, and in particular, descriptors as fingerprints, can bring relevant performance improvement.

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“…Yan Wang et al [42] presented device-free location-oriented activity identification at home through the use of CSI. Domenico et al [43] presented a device-free crowd counting and occupancy estimation system by analyzing the shape of the Doppler spectrum of the received signal which was correlated to the number of people moving in the monitored environment. It can be seen that the detection of indoor human behavior detection using CSI signals has become a trend for the development of wireless sensor networks in the future.…”

Section: Novel Applications Based On Csimentioning

confidence: 99%

PCA-Kalman: device-free indoor human behavior detection with commodity Wi-Fi

Dang

Huang

Hao

et al. 2018

J Wireless Com Network

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Human behavior detection has become increasingly significant in various fields of application. In this paper, we propose a device-free indoor human behavior detection method with channel state information (CSI) and principal component analysis (PCA), respectively, in the line of sight environment, non-line-of-sight environment, and through the wall environment experiments. We divide this method into two parts. It begins with an online phase. A fingerprint database is established by collecting the original data packets of CSI in different time periods and using the characteristics of PCA algorithm to reduce the dimension of the original CSI data. Then, some outlier values are removed by Kalman filter algorithm, and we will get more stable data and fully prepared for the docking experiments. At the same time, the PCA algorithm's estimation results are corrected according to the detected real-time motion speed to reduce the mismatch target. Then, in the offline phase, the classification of data is collected in the real-time environment by using support vector machine (SVM) algorithm. This method not only reduces the time complexity of the algorithm but also improves the detection rate of the human's behavior and reduces the error. The processed data are matched with the data in the fingerprint database, and finally, the detection of different behaviors performed by humans in an indoor environment is finally achieved according to the matching results. We experimented repeatedly in three different scenarios, with an average 95% of human behavior detection rate in three different environments. In addition, we compare the method proposed in this paper with the existing methods in different aspects, such as the impact of the number of subcarriers, the impact of data packets, and the impact of the test area. The experimental results show that this method is superior to other algorithms in terms of average error and indoor activity recognition accuracy, which can more accurately identify indoor human motion behavior and improve the stability of the system.

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Trained-once device-free crowd counting and occupancy estimation using WiFi: A Doppler spectrum based approach

Cited by 77 publications

References 13 publications

Extracting occupants’ energy-use patterns from Wi-Fi networks in office buildings

Extracting occupants’ energy-use patterns from Wi-Fi networks in office buildings

CSI-based fingerprinting for indoor localization using LTE Signals

PCA-Kalman: device-free indoor human behavior detection with commodity Wi-Fi

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