Dapeng Man

An adaptive wireless passive human detection via fine-grained physical layer information

Wireless device-free passive human detection is a key enabler for a range of indoor location-based services such as asset security, emergency responses, privacy-preserving children and elderly monitoring, etc. Since the feature of received signal varies with different multipath propagation conditions, an labor-intensive on-site calibration procedure is almost indispensable to decide the optimal scenario-specific threshold for human detection. Such overhead, however, impedes readily and fast deployment of wireless device-free human detection systems in practical indoor environments. In this work, we explore PHY layer multipath profiling information to extract a novel quantitative metric Ks as an indicator for link sensitivity, and further exploit a linear detection threshold prediction model. We design an adaptive device-free human detection scheme that automatically predicts the detection threshold according to the richness of multipath propagation within monitored areas. We implement our scheme with commodity WiFi infrastructure and evaluate it in typical office environments. Extensive experimental results show that our scheme yields comparative performance with the state-of-the-art, yet requires no on-site threshold calibration.

Enhancing the performance of indoor device-free passive localization

Device-free passive localization (DFPL) has been an emerging application with fast increasing development. Channel State Information- (CSI-) based DFPL is recently paid more attention to for fine-granularity and stability of CSI. However, lots of dead spots exist in the area of interest. And the accuracy of localization is not still completely satisfactory, especially for outside of the first Fresnel zone. In our paper, we put forward a new metric to estimate the sensitivity of a receiver to changes in the detecting area. In our experiment, we observe that the performance of DFPL can be raised when the receiver is placed at the location with high receiver sensitivity. Hence, we develop a new high-performance indoor device-free passive localization (HiDFPL), which employs a Bayesian a posteriori approach and possesses high receiver sensitivity. The experiment results demonstrate the outstanding performance of the proposed scheme.

FRID: Indoor Fine-Grained Real-Time Passive Human Motion Detection

With the eruptible popularity of wireless sensing, wireless device-free passive human detection has received widespread attention. Indoor fine-grained device-free passive human motion detection based on the PHY layer information is rapidly developed. Since the received signal features can vary under different multipath propagation conditions, in the paper, we propose a lightweight and real-time passive human motion via physical layer phase information, which is independent of the indoor scenarios and needs no re-calibration. We firstly obtain available phase feature by a linear transformation on the raw channel state information(CSI). The real-time human motion detection is implemented based on two developed schemes: short-term averaged variance ratio (SVR) and long-term averaged variance ratio (LVR). We realize the design with commercial WiFi devices and evaluate it in typical multipath-rich indoor scenarios. As demonstrated in the experiments, our approach can achieve high detection rate and low false positive rate.

WiFi-Based Real-Time Calibration-Free Passive Human Motion Detection †

With the rapid development of WLAN technology, wireless device-free passive human detection becomes a newly-developing technique and holds more potential to worldwide and ubiquitous smart applications. Recently, indoor fine-grained device-free passive human motion detection based on the PHY layer information is rapidly developed. Previous wireless device-free passive human detection systems either rely on deploying specialized systems with dense transmitter-receiver links or elaborate off-line training process, which blocks rapid deployment and weakens system robustness. In the paper, we explore to research a novel fine-grained real-time calibration-free device-free passive human motion via physical layer information, which is independent of indoor scenarios and needs no prior-calibration and normal profile. We investigate sensitivities of amplitude and phase to human motion, and discover that phase feature is more sensitive to human motion, especially to slow human motion. Aiming at lightweight and robust device-free passive human motion detection, we develop two novel and practical schemes: short-term averaged variance ratio (SVR) and long-term averaged variance ratio (LVR). We realize system design with commercial WiFi devices and evaluate it in typical multipath-rich indoor scenarios. As demonstrated in the experiments, our approach can achieve a high detection rate and low false positive rate.

Robust WLAN-Based Indoor Fine-Grained Intrusion Detection

Intrusion detection plays a critical role in security of peoples possessions. Approaches such as video-based, infrared-based, RFID, UWB, etc. can provide satisfying detection accuracy. However, they all require specialized hardware deployment and strict using conditions which hinder their wide deployment. Beyond communication, WLANs can also act as generalized sensor networks and there are several researches working on motion detection via WLAN due to its advantages in deployment flexibility, coverage, and cost efficiency. Nevertheless, they are unsuitable for intrusion detection as none of them can accurately detect human motion when the moving speed is very slow. This paper proposes SIED as an accurate method for Speed Independent device-free Entity Detection which is suitable for intrusion detection even when the entitys moving speed is very slow. The influence becomes much smaller when the entity is moving with a very slow speed. Previous methods have the limitations in that their performance downgrades sharply when the entitys moving speed is very slow. Recently, it has been shown that Channel State Information (CSI) at PHY layer of wireless network has the potential to detect moving entities more accurately. In this paper we leverage CSI of 802.11n wireless network and probability technique to detect entities of different moving speeds. SIED captures the variance of variances of amplitudes of each CSI subcarrier, and combines Hidden Markov Model (HMM) to make entity detection a probability problem. We implement SIED using commercial WiFi devices and evaluate our method using two typical testbeds and show that SIED can achieve an average detection accuracy of greater than 98% under different entity moving speed.

WiSal: Ubiquitous WiFi-Based Device-Free Passive Subarea Localization without Intensive Site-Survey

WiFi-based device-free passive localization is an emerging technique and holds great potentials to ubiquitous location-based service and security-critical applications. Recently, channel state information (CSI) can be expediently extracted from commercial WiFi NICs, which offers a chance for fine-grained and high-accuracy device-free passive localization. Current device-free passive localization systems either rely on deploying specialized systems with dense transmitter-receiver links with high economic cost or elaborating intensive training process with high labor cost. In this study, we explore the possibility of a ubiquitous WiFi-based device-free passive subarea localization(WiSal) via physical-layer channel response features without intensive site-survey based on a single link at the expense of some localization accuracy. The location of human can be estimated by leveraging K-Nearest Neighbor(KNN) algorithm based on signal change features of different subareas that are achieved by a hierarchical clustering algorithm. WiSal can greatly reduce the cost of site survey on manpower and time, and meet requirements of numerous applications. Extensive experiments illustrate that our scheme can achieve great performance in terms of localization accuracy under a single link.

Mathematical Performance Evaluation Model for Mobile Network Firewall Based on Queuing

While mobile networks provide many opportunities for people, they face security problems huge enough that a firewall is essential. The firewall in mobile networks offers a secure intranet through which all traffic is handled and processed. Furthermore, due to the limited resources in mobile networks, the firewall execution can impact the quality of communication between the intranet and the Internet. In this paper, a performance evaluation mathematical model for firewall system of mobile networks is developed using queuing theory for a multihierarchy firewall with multiple concurrent services. In addition, the throughput and the package loss rate are employed as performance evaluation indicators, and discrete-event simulated experiments are conducted for further verification. Lastly, experimental results are compared to theoretically obtained values to identify a resource allocation scheme that provides optimal firewall performance and can offer a better quality of service (QoS) in mobile networks.

Identification of unknown operating system type of Internet of Things terminal device based on RIPPER

Due to the vast popularity of sensors, cloud computing, mobile computing, and intelligent devices, the Internet of Things has seen tremendous growth in recent years. Operating system type recognition is the core technology of network security assessment. Due to inherit security problems of Internet of Things such as the situation of risk and threat of information, the operating system recognition seeks research attention for Internet of Things network security. In view of the current identification method of active operating system, it is prone to be detected by intrusion detection system. The operating system identification technology based on transmission control protocol/Internet protocol fingerprint library is more complicated than to distinguish the operating system types of unknown fingerprints. In this work, a passive operating system identification method based on RIPPER model is proposed. Also, it is compared with the existing support vector machine and C45 decision tree classification algorithms. Experiments reveal that RIPPER-based algorithm has better recognition accuracy and recognition efficiency.

Two-Stage Mixed Queuing Model for Web Security Gateway Performance Evaluation

Web Security Gateway (WSG) is a new type of network security product that maintains the security of trusted networks. In this paper, a WSG model for evaluating WSG performance is presented. This paper advances discussion of previous studies on series services under multiple service windows. The proposed model consists of a two-stage queuing system. The first stage is a network layer simulation. The second stage is thus similar to a parallel hyper-Erlang distribution model. The results of a simulation test verified the feasibility and performance of the proposed model.

Device-Free Passive Identity Identification via WiFi Signals

Device-free passive identity identification attracts much attention in recent years, and it is a representative application in sensorless sensing. It can be used in many applications such as intrusion detection and smart building. Previous studies show the sensing potential of WiFi signals in a device-free passive manner. It is confirmed that human’s gait is unique from each other similar to fingerprint and iris. However, the identification accuracy of existing approaches is not satisfactory in practice. In this paper, we present Wii, a device-free WiFi-based Identity Identification approach utilizing human’s gait based on Channel State Information (CSI) of WiFi signals. Principle Component Analysis (PCA) and low pass filter are applied to remove the noises in the signals. We then extract several entities’ gait features from both time and frequency domain, and select the most effective features according to information gain. Based on these features, Wii realizes stranger recognition through Gaussian Mixture Model (GMM) and identity identification through a Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. It is implemented using commercial WiFi devices and evaluated on a dataset with more than 1500 gait instances collected from eight subjects walking in a room. The results indicate that Wii can effectively recognize strangers and can achieves high identification accuracy with low computational cost. As a result, Wii has the potential to work in typical home security systems.