Matrix Factorization Techniques for Recommender Systems

The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

With the high demand for wireless data traffic, WiFi networks have experienced very rapid growth, because they provide high throughput and are easy to deploy. Recently, Channel State Information (CSI) measured by WiFi networks is widely used for different sensing purposes. To get a better understanding of existing WiFi sensing technologies and future WiFi sensing trends, this survey gives a comprehensive review of the signal processing techniques, algorithms, applications, and performance results of WiFi sensing with CSI. Different WiFi sensing algorithms and signal processing techniques have their own advantages and limitations and are suitable for different WiFi sensing applications. The survey groups CSI-based WiFi sensing applications into three categories, detection, recognition, and estimation, depending on whether the outputs are binary/multi-class classifications or numerical values. With the development and deployment of new WiFi technologies, there will be more WiFi sensing opportunities wherein the targets may go beyond from humans to environments, animals, and objects. The survey highlights three challenges for WiFi sensing: robustness and generalization, privacy and security, and coexistence of WiFi sensing and networking. Finally, the survey presents three future WiFi sensing trends, i.e., integrating cross-layer network information, multi-device cooperation, and fusion of different sensors, for enhancing existing WiFi sensing capabilities and enabling new WiFi sensing opportunities.

WiFi Sensing with Channel State Information: A Survey

Recently, device-free human behavior recognition has become a hot research topic and has achieved significant progress in the field of ubiquitous computing. Among various implementation, behavior recognition based on WiFi CSI (channel state information) has drawn wide attention due to its major advantages. This paper investigates more than 100 latest CSI based behavior recognition applications within the last 6 years and presents a comprehensive survey from every aspect of human behavior recognition. Firstly, this paper reviews general behavior recognition applications using the WiFi signal and presents the basic concept of CSI and the fundamental principle of CSI-based behavior recognition. This paper analyzes the key components and core characteristics of the system architecture of human behavior recognition using CSI. Afterward, we divide the sensing procedures into many steps and summarize the typical studies from these steps, including base signal selection, signal preprocessing, and identification approaches. Next, based on the recognition technique, we classify the applications into three groups, including pattern-based, model-based, and deep learning-based approach. In every group, we categorize the state-of-the-art applications into three groups, including coarse-grained specific behavior recognition, fine-grained specific behavior recognition, and activity inference. It elaborates the typical behavior recognition applications from five aspects, including experimental equipment, experimental scenario, behavior, classifier, and system performance. Then, this paper presents comprehensive discussions about representative applications from the implementation view and outlines the major consideration when developing a recognition system. Finally, this article concludes by analyzing the open issues of CSI-based behavior recognition applications and pointing out future research directions.

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A Survey on Human Behavior Recognition Using Channel State Information

Recent years have witnessed increasing research interest in human behavior recognition as it provides attractive applications in various sensing scenarios. Among these encouraging implementations, device-free behavior recognition based on WiFi channel state information (CSI) has attracted significant attention due to the popularity of WiFi devices and abundant channel characteristics from CSI. Meanwhile, the CSI signal provides us with additional benefits because it can propagate through a wall. This through-the-wall and device-free scheme not only enables us to identify specific human actions but also to infer person activities by collecting the data from different rooms. This paper presents a survey on the state-of-art progresses in device-free through-the-wall human behavior recognition based on CSI. Specifically, this paper first introduces the basic concept of CSI and describes the signal variation caused by human behavior. Then, it illustrates that different human behaviors can cause signal transformation. Therefore, the unique map relationship between action and signal variation can be leveraged to recognize human behavior. Next, it provides the general architecture of through-the-wall behavior recognition and highlights its core characteristic. It investigates the state-of-art applications in various scenarios and analyzes specific design schemes and implementations. Afterward, it discusses the various across wall applications and makes a detailed comparison between non-through-the-wall and through-the-wall applications. Meanwhile, it analyzes many factors that affect recognition accuracy and emphasizes performance differences under across wall scenarios. Finally, this paper concludes by summarizing the issues and challenges faced and providing insights into the possible solution and future research trend.

A Survey on CSI-Based Human Behavior Recognition in Through-the-Wall Scenario

The invention provides a determination method of a pseudo base station and a mobile terminal. The method comprises the steps of sending a base station parameter acquiring request to a base station of the local community through the mobile terminal; receiving the base station parameters from the base station through the mobile terminal; comparing the base station parameters with the preset base station parameters of an operator; if the base station parameters are the same as the base station parameters of the operator, acquiring electric signal strength of the base station; comparing the variable of the electric signal strength of the base station within the preset time quantum with the preset strength variation value; if the variable of the electric signal strength of the base station within the preset time quantum is less than the preset strength variation value, comparing the electric signal strength of the base station with the preset electric signal strength; if the electric signal strength of the base station is more than the preset electric signal strength, determining that the base station is the pseudo base station. With the adoption of the determination method of the pseudo base station and the mobile terminal, the pseudo base station can be quickly and conveniently recognized, and meanwhile, the signaling load of the base station can be reduced; the privacy can be prevented from disclosing; in addition, the pseudo base station can be timely positioned.

Determination method of pseudo base station and mobile terminal

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.

/pdf/notifi-a-ubiquitous-wifi-based-abnormal-activity-detection-4eal3r3y0m.pdf

NotiFi: A ubiquitous WiFi-based abnormal activity detection system

The invention provides a protection method against wifi (wireless fidelity) phishing. The method comprises the steps of detecting an AP (access point) parameter of a wireless AP connected with a terminal, judging judge the AP is in a preset white list according to the AP parameter, if not, modifying an AP parameter of a first AP in the white list into the AP parameter of the AP connected with the terminal to disconnect the terminal from the AP, recovering the AP parameter of the first AP, and reassociating the terminal to the new AP till the AP is in the white list. The invention further provides a protection system against the wifi phishing, which comprises a judgment unit, a modification unit and a detection unit. The method and the system can detect and protect the wireless access point connected with the mobile terminal in real time, and effectively avoids a potential safety hazard of stealing important information by using the wireless AP.

Protection method and system against wifi (wireless fidelity) phishing

The invention provides a method for distinguishing a pseudo base station in real time The method comprises steps as follows: when a mobile terminal detects that an LAC (location area code) varies, acquiring the LAC and latitude and longitude coordinates; if the variation of the LAC from an initial LAC exceeds a first preset value or the variation of the latitude and longitude coordinates from initial latitude and longitude coordinates exceeds a second preset value, determining that a base station connected with the mobile terminal is the pseudo base station; positioning the pseudo base station according to a radio lateral positioning technology The invention further provides a system for distinguishing the pseudo base station in real time The system comprises an acquisition unit, a determination unit and a positioning unit With the adoption of the method and the system, the pseudo base station can be distinguished before junk short messages are received, privacy disclosure can be effectively prevented, and the pseudo base station can be detected in real time and distinguished accurately and quickly

Method and system for distinguishing pseudo base station in real time

The invention provides a real-time pseudo base station determining method based on a short message The method comprises steps as follows: a mobile terminal analyzes the received short message to acquire a short message service center number; whether the short message service center number is identical with an initialized short message service center number or not is judged, and if the short message service center number is not identical with the initialized short message service center number, the short message is determined to be sent by a pseudo base station; the short message is intercepted, and the short message is displayed as a short message sent by the pseudo base station The invention further provides a real-time pseudo base station determining system based on the short message The system comprises an analysis module, a determination module and an interception module With the adoption of the method and the system, short messages of pseudo base stations can be intercepted in real time, and privacy disclosure can be effectively prevented

Na Pang

Papers

Determination method of pseudo base station and mobile terminal

NotiFi: A ubiquitous WiFi-based abnormal activity detection system

Protection method and system against wifi (wireless fidelity) phishing

Method and system for distinguishing pseudo base station in real time

Real-time pseudo base station determining method and system based on short message