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

In recent years, due to the rapidly growing capacities of physical layer, device-free passive detection holds great importance for a broad range of application. Most recent works focus on motion detection, intrusion detection, and vital sign with commodity WiFi devices in the indoor environment. Conventional device-free motion detection techniques, which utilize received signal strength (RSS), may suffer from coarse granularity and high variability problems. In resorting to the finer-grained channel state information (CSI), we propose PhaseMode, a novel approach for device-free motion detection leveraging CSI phase difference data between adjacent antenna pairs. We implement our approach on commercial WiFi devices and validate its performance. We conduct experiments in different test periods of three indoor environments; the results show that the proposed scheme achieves an average accuracy over 99.4% of motion detection in different scenarios.

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A Robust Passive Intrusion Detection System with Commodity WiFi Devices

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.

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NotiFi: A ubiquitous WiFi-based abnormal activity detection system

Crowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI. INDEX TERMS Crowdsourced business intelligence, consumer behaviors, competitive intelligence, crowd intelligence, commercial site recommendation, brand trending.

https://hal.archives-ouvertes.fr/hal-02321021/document

Harnessing the Power of the General Public for Crowdsourced Business Intelligence: A Survey

In recent years, we have witnessed a surge in new retail, which aims to combine the best of physical and online retailing using Internet of things and artificial intelligence techniques. The unmanned store is a representative type of new retail, which leverages wireless sensing and machine learning techniques to recognize fine-grained in-store customer behaviors, infer their intents, and learn their preferences. This paper gives an overview of this emerging research area, presents its key techniques and applications, and discusses the open issues of this field.

DeepStore: Understanding Customer Behaviors in Unmanned Stores

The invention discloses an access control method and system for a wireless hotspot based on dynamic keys The method comprises that: after being connected with a terminal, a wireless hotspot end sends a first public key and a first private key to the terminal, and meanwhile, stores a second private key and a second public key corresponding to the first public key and the first private key respectively; after the terminal is connected with the wireless hotspot end, when the terminal does not receive the first public key and the first private key sent by the wireless hotspot end within a preset time period, the terminal is disconnected from the wireless hotspot end; after receiving a terminal disconnection instruction, the wireless hotspot end sends a request for acquiring an access password and an MAC (Multimedia Access Control) address to the terminal; and the wireless hotspot end receives the access password and the MAC address sent by the terminal, matches the access password and the MAC address with an access password and an MAC address in a preset database, and judges whether the terminal is allowed to access the network via the judgment result The method and the system solve the problem of data transmission security of an internal information system of a local area network

Access control method and system for wireless hotspot based on dynamic keys

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.

Analyzing Customer’s Product Preference Using Wireless Signals

In a RFID enabled system, tagged products is ordinarily required to be transferred from an owner to another, which makes the ownership of corresponding tags change for several times To overcome problem of temporary ownership transfer, a lightweight anonymous group ownership transfer protocol is proposed in this paper It involves ownership transfer and ownership recovery for group tags, which are all based upon mutual authentication Compared with most of previous protocols using hash functions and cryptographic suites, our scheme only utilizes simple operations, which are completely compliant with EPC Class-1 Generation-2 standard Through our scheme, the window problem in multi-owner environment is first solved Security analysis shows that our protocol can provide security and privacy preservation It is also demonstrated that it resists various attacks Performance comparisons indicate that our scheme can meet necessary security requirements and achieve better performance

Lightweight Anonymous RFID Group Ownership Transfer Protocol in Multi-owner Environment

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.

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

Large amounts of data processing bring parallel computing into sharp focus. In order to solve the sorting problem of large-scale data in the era of internet, a large-scale distributed sorting algorithm based on cloud computing is proposed. The algorithm uses the ideas of quick-sort and merge-sort to sort and integrate the data on each cloud, which making best of clouds’ computing and storage resources. Taking advantage of the idea of parallel computing, we can reduce the computing time and improve the efficiency of sorting. By evaluating algorithm’s time complexity and organizing a simulation test, the effectiveness was verified.

Wenjing Rong

Papers

Access control method and system for wireless hotspot based on dynamic keys

Analyzing Customer’s Product Preference Using Wireless Signals

Lightweight Anonymous RFID Group Ownership Transfer Protocol in Multi-owner Environment

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

A Large-Scale Distributed Sorting Algorithm Based on Cloud Computing