FreeDetector: Device-Free Occupancy Detection with Commodity WiFi

Zou, Han; Zhou, Yuxun; Yang, Jianfei; Gu, Weixi; Xie, Lihua; Spanos, Costas J.

doi:10.1109/seconw.2017.8011040

Cited by 52 publications

(27 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Including these factors too, the authors could check if these are important in the forecast. Random Forest has been proved to give the best results for classification in terms of efficiency and accuracy, for occupancy detection [10]. On the other hand, the drawback of running time aspect of the algorithm is not a concern in our application and type of situation, because we do not have a large number of features.…”

Section: State Of the Artmentioning

confidence: 99%

Privacy-Preserving Sensing and Two-Stage Building Occupancy Prediction using Random Forest Learning

Stâmâtescu¹,

Chitu²

2020

Preprint

View full text Add to dashboard Cite

Sensing and predicting occupancy in buildings is an important task that can lead to significant improvements in both energy efficiency and occupant comfort. Rich data streams are now available that allow for machine learning based algorithm implementation of direct and indirect occupancy estimation. We evaluate ensemble models, namely random forests, on data collected from an 8x8 PIR matrix thermopile sensor with the dual goal of predicting individual cell temperature values and subsequently detecting the occupancy status. Evaluation of the method is based on a real case study deployed in an IT Hub in Bucharest, for which we have collected over three weeks of ground data, analyzed and used it in order to predict occupancy in a room. Results show a 2-4% mean absolute percentage error for the temperature prediction and >99% accuracy for a threeclass model to detect human presence. The resulting outputs can be used by predictive building control models to optimize the commands to various subsystems. By separating the specific deployment from the system architecture and data structure, the application can be easily translated to other usage profiles and built environment entities. As compared to vision based systems, our solution preserves privacy with improved performance when compared to single PIR or indirect estimation.Index Terms-occupancy prediction, smart buildings, random forests, computational intelligence, data acquisition.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Privacy-Preserving Sensing and Two-Stage Building Occupancy Prediction using Random Forest Learning

Stâmâtescu¹,

Chitu²

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The Channel State Information (CSI) metric has become a popular localization metric over RSSI as it is more immune to the adverse effects of multipath propagation [32] and outperforms RSSI based methods [33]. Since CSI offers more fine-grained information than RSSI, it has been extensively utilized in machine learning based DFL approaches including shapelet learning [34], SVM [9], [35], Random Forest [36], HMM [37], and Deep Learning [38]. A shortcoming of CSI is that it is currently only accessible using modified drivers in legacy Intel 5300 [11], [39], Atheros ath9k [12] based devices, or by using Software Defined Radio (SDR) platforms like USRP [40] or WARP [41].…”

Section: Channel State Informationmentioning

confidence: 99%

SpringLoc: A Device-Free Localization Technique for Indoor Positioning and Tracking Using Adaptive RSSI Spring Relaxation

et al. 2019

View full text Add to dashboard Cite

Device-free localization (DFL) algorithms using the received signal strength indicator (RSSI) metrics have become a popular research focus in recent years as they allow for location-based service using commercial-off-the-shelf (COTS) wireless equipment. However, most existing DFL approaches have limited applicability in realistic smart home environments as they typically require extensive offline calibration, large node densities, or use technology that is not readily available in commercial smart homes. In this paper, we introduce SpringLoc and a DFL algorithm that relies on simple parameter tuning and does not require offline measurements. It localizes and tracks an entity using an adaptive spring relaxation approach. The anchor points of the artificial springs are placed in regions containing the links that are affected by the entity. The affected links are determined by comparing the kernel-based histogram distance of successive RSSI values. SpringLoc is benchmarked against existing algorithms in two diverse and realistic environments, showing significant improvement over the state-of-the-art, especially in situations with low-node deployment density. INDEX TERMS Device-free localization (DFL), histogram distance, indoor positioning systems (IPS), smart homes, spring-relaxation.

show abstract

“…Accurate human sensing is essential for context awareness to improve building management system and design of impact building. It also plays an important role in many e-Healthcare applications, such as infant monitoring in rooms, monitoring of the elderly at home, patient monitoring in hospitals, and safety management in offices [1][2][3][4][5][6]. Traditional approaches for indoor context awareness include vision [7], RFID [8], environment sensors [9], and passive infrared (PIR) sensors [10].…”

Section: Introductionmentioning

confidence: 99%

“…Yet, the RSS is limited by its poor sensing accuracy because it cannot measure the multipath effects from human motion [16]. On the other hand, channel state information (CSI), the fine-grained measurement at the physical (PHY) layer, is often used to describe the WiFi signal propagation property which contains impacts from human presence and movements [1,12,[14][15][16]. These approaches require specific network cards to exam the phase shifts from multiple radio frequency (RF) channels.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

WiFi‐based passive sensing system for human presence and activity event classification

Tan

Piechocki

2018

IET wirel. sens. syst.

View full text Add to dashboard Cite

Detection of human presence and activity event classification are of importance to a variety of context-awareness applications such as e-Healthcare, security, and low impact building. However, existing radio frequency identification tags, wearables, and passive infrared approaches require the user to carry dedicated electronic devices that suffer from problems of low detection accuracy and false alarms. This study proposes a novel system for non-invasive human sensing by analysing the Doppler information contained in the human reflections of WiFi signal. Doppler information is insensitive to stationary objects, thus there is no need for any scenario-specific calibration which makes it ideal for human sensing. We also introduce the timefrequency domain feature vectors of WiFi Doppler information for the support vector machine classifier towards activity event recognition. The proposed methodology is evaluated on a software defined radio system together with the experiment of five different events. The results indicate that the proposed system is sufficient for indoor context awareness, with 95.3% overall accuracy for event classification and 93.3% accuracy for human presence detection, which outperforms the traditional received signal strength approach where accuracy is 69.3% for event classification and 83.3% for human presence detection.

show abstract

FreeDetector: Device-Free Occupancy Detection with Commodity WiFi

Cited by 52 publications

References 21 publications

Privacy-Preserving Sensing and Two-Stage Building Occupancy Prediction using Random Forest Learning

Privacy-Preserving Sensing and Two-Stage Building Occupancy Prediction using Random Forest Learning

SpringLoc: A Device-Free Localization Technique for Indoor Positioning and Tracking Using Adaptive RSSI Spring Relaxation

WiFi‐based passive sensing system for human presence and activity event classification

Contact Info

Product

Resources

About