Na Pang

WiseFi: Activity Localization and Recognition on Commodity Off-the-Shelf WiFi Devices

Most recently, activity localization and recognition has increasingly attracted significant attentions due to its broad range of applications to support smart devices. Pioneer systems based on WiFi signals usually require six to eight antennas to localize the activity while the commodity WiFi infrastructure does not meet this requirement. In addition, they also require the priori learning of wireless signals to recognize a pre-defined set of activities. In this paper, we present WiseFi, an activity localization and recognition system by leveraging fine-grained physical layer information on commodity off-the-shelf (COTS) WiFi devices. WiseFi harnesses the amplitude and the phase of Channel State Information (CSI), and the Angle-of-arrival (AOA) of blocked signals to localize and recognize human activity. The intuition behind WiseFi is that whenever the target occludes the incoming wireless signals, the power of AOA will drop in the same direction. Experimental results indicate that WiseFi can achieve comparable performance in activity localization and recognition on COTS WiFi devices.

WiN: Non-invasive Abnormal Activity Detection Leveraging Fine-Grained WiFi Signals

Abnormal activity detection has recently drawn significant research attention, because of its potential applications in providing critical and severe emergency information. Existing non-invasive activity detecting approaches rely on radio signals, wearable sensors or specialized hardware. Motivated by the observation that the amplitude and the phase information of channel state information CSI are highly sensitive to activity variation, we propose WiN, a non-invasive abnormal activity detection system, based on fine-grained physical layer channel state information, which is available from commercial off-the-shelf WiFi devices. We implement WiN and evaluate its performance in IEEE 802.11n devices. Extensive experiments in typical real-world environments demonstrate that WiN can achieve impressive performance in abnormal activity detection.

Analyzing Customer’s Product Preference Using Wireless Signals

Customer’s product preference provides how a customer collects products or prefers one collection over another. Understanding customer’s product preference can provide retail store owner and librarian valuable insight to adjust products and service. Current solutions offer a certain convenience over common approaches such as questionnaire and interviews. However, they either require video surveillance or need wearable sensor which are usually invasive or limited to additional device. Recently, researchers have exploited physical layer information of wireless signals for robust device-free human detection, ever since Channel State Information (CSI) was reported on commodity WiFi devices. Despite of a significant amount of progress achieved, there are few works studying customer’s product preference. In this paper, we propose a customer’s product preference analysis system, PreFi, based on Commercial Off-The-Shelf (COTS) WiFi-enabled devices. The key insight of PreFi is to extract the variance features of the fine-grained time-series CSI, which is sensitively affected by customer activity, to recognize what is the customer doing. First, we conduct Principal Component Analysis (PCA) to smooth the preprocessed CSI values since general denoising method is insufficient in removing the bursty and impulse noises. Second, a sliding window-based feature extraction method and majority voting scheme are adopted to compare the distribution of activity profiles to identify different activities. We prototype our system on COTS WiFi-enabled devices and extensively evaluate it in typical indoor scenarios. The results indicate that PreFi can recognize a few representative customer activity with satisfied accuracy and robustness.

NotiFi: A ubiquitous WiFi-based abnormal activity detection system

We build an ubiquitous abnormal activity detection system, namely NotiFi, for accurately detecting the abnormal activities on commercial off-the-shelf (COTS) IEEE 802.11 devices. In contrast to the traditional wearable sensor based and computer vision based systems which require additional sensors or enough lighting in line-of-sight (LoS) scenario, we proceed directly with abnormal activity characterization and activity modeling at the WiFi signal level based on Channel State Information (CSI). The intuition of NotiFi is that whenever the human body occludes the wireless signal transmitting from the access point to the receiver, the phase and the amplitude information of Channel State Information (CSI) will change sensitively. By creating a multiple hierarchical Dirichlet processes, NotiFi automatically learns the number of human body activity categories for abnormal detection. Experimental results in three typical indoor environments indicate that NotiFi can achieve satisfactory performance in accuracy, robustness and stability.

A Dynamic Credible Factory Reset Mechanism of Personal Data in Android Device

Due to plenty of confidential and private information stored on the phone, the security of it has become more prominent increasingly. The private information storing on Android devices can be recovered easily even if it is reset by traditional data factory reset process. It is extremely unsafe and unreliable especially for the phone lent to others. In this paper, we proposed a dynamic credible factory mechanism of personal data in Android device to protect privacy. Not only it can allow users to factory reset the system quickly and safely, but it also thoroughly crushes application data files while applications are not deleted. The mechanism is based on a private file recognition algorithm that checks the properties of file tree dynamically proposed in the paper and credible erase on data blocks pointed from special inode of private files on the flash storage under the condition of no remounting and no rebooting. It aims to provide a more secure, fast crush method in Android system to prevent individual private data being recovered maliciously. We implemented our credible factory mechanism to evaluate their performance.

WarnFi: Non-invasive wifi-based abnormal activity sensing using non-parametric model

Abnormal activity sensing has attracted increasing research attention in military surveillance, patient monitoring, and health care of children and elderly, etc. Researchers have exploited the characteristics of wireless signals to sense “keystrokes” and “human talks”, relieving the privacy invasion concern caused by mounting the surveillance cameras or wearing the smart devices. However, existing technologies usually require some specialized hardware, and can only sense a fixed set of pre-defined activities through a supervised learning from those wireless signals patterns. In this paper, we propose WarnFi, a non-invasive abnormal activity sensing system with only two commodity off-the-shelf (COTS) WiFi devices. The intuition of WarnFi is that whenever the human body occludes the wireless signal transmitting from the access point to the receiver, the time-series of Channel State Information (CSI) will experience a unique variation. By using a non-parametric model, WarnFi can dynamically cluster the human body activities for abnormal sensing. Extensive experiments in various scenarios demonstrate the satisfactory performance of WarnFi.

Device-Free Intruder Sensing Leveraging Fine-Grained Physical Layer Signatures

With the development of smart indoor spaces, intruder sensing has attracted great attention in the past decades. Realtime intruder sensing in intelligent video surveillance is challenging due to the various covariate factors such as walking surface, clothing, carrying condition. Gait recognition provides a feasible approach for human identification. Pioneer systems usually rely on computer vision or wearable sensors which pose unacceptable privacy risks or be limited to additional devices. In this paper, we present CareFi, a device-free intruder sensing system that can identify a stranger or a burglar based on Commercial Off-The-Shelf (COTS) WiFi-enabled devices. CareFi extracts the fine-grained physical layer Channel State Information (CSI) to analyze the distinguishing gait characteristics for intruder sensing. CareFi can identify the intruder under both line-of-sight (LOS) and non-line-of-sight (NLOS) situations. CareFi does not require any dedicated sensors or lighting and works in dark just as well as in light. We prototype CareFi using commercial off-the-shelf WiFi devices and experimental results in typical indoor scenarios show that it achieves more than \(87.2\%\) detection rate for intruder sensing.

Collaborative filtering via different preference structures

Recently, social network websites start to provide third-parity sign-in options via the OAuth 2.0 protocol. For example, users can login Netflix website using their Facebook accounts. By using this service, accounts of the same user are linked together, and so does their information. This fact provides an opportunity of creating more complete profiles of users, leading to improved recommender systems. However, user opinions distributed over different platforms are in different preference structures, such as ratings, rankings, pairwise comparisons, voting, etc. As existing collaborative filtering techniques assume the homogeneity of preference structure, it remains a challenge task of how to learn from different preference structures simultaneously. In this paper, we propose a fuzzy preference relation-based approach to enable collaborative filtering via different preference structures. Experiment results on public datasets demonstrate that our approach can effectively learn from different preference structures, and show strong resistance to noises and biases introduced by cross-structure preference learning.

Multi-attribute Counterfeiting Tag Identification Protocol in Large-Scale RFID System

Counterfeiting products identification is the main application of RFID technology. Among all the RFID security problems, counterfeiting tag identification is an urgent issue with rapid growth of counterfeiters. In this paper, a multi-attribute counterfeiting tag identification protocol based on multi-dimension dynamic bloom filter in large-scale RFID system is proposed. Dynamic bloom filters for tag’s attributes: identity information ID and location information angle value, are first brought as criterion of counterfeiting tag identification. Different from previous probabilistic approaches, our protocol not only identifies unknown tags, but also first solves problem that counterfeiters hold the same ID with genuine ones. Furthermore, our protocol can detect and verify counterfeiting tags’ identity. Performance analysis shows that especially with huge amount of tags, our protocol can achieve higher identification efficiency with reasonable time cost.

Opportunistic Probe: An Efficient Adaptive Detection Model for Collaborative Intrusion Detection

The number of network intrusions, such as large-scale stealthy scans, worms, and distributed denial-of-service (DDoS) attacks, has significantly increased. Collaborative intrusion detection system (CIDS) becomes an essential part for analyzing multiple network security simultaneously. The trust-based packet filter method using Bayesian inference tries to decrease the processing burden, but overhead network packets make that performance and accuracy are still open issues. In this paper, we propose an Opportunistic Probe model, which is a transport entity that carries encrypted characteristic attributes from trusted host to the checking host. A Detection Time Optimization Algorithm is proposed to determine the trusted period of hosts during which the unnecessary detection can be reduced. The case study and experimental analysis demonstrates the effectiveness, scalability and robustness of the proposed approach.