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.
In this paper, we propose a novel vehicle positioning method for commodity multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) WIFI systems in indoor parking lots. ...To address the limitation of the small number of WIFI antennas, the proposed method first utilizes signal model rearrangement techniques, in which the abundant carrier frequency resources of WIFI are expanded into space resources. Then a rearranged off-grid sparse Bayesian learning (ROG-SBL) algorithm is developed for parameters estimation to achieve vehicle positioning. Specifically, by resorting to the Bayesian inference and Newton method, the position-related parameters are estimated iteratively by fitting the channel state information (CSI) measurement model, and thus the vehicle positioning is realized according to the geometric relationship. Moreover, we derive the Cramér-Rao bound (CRB) as a performance reference for the proposed algorithm. Compared with the existing algorithms, the proposed one improves the positioning performance of the vehicle with fewer carrier numbers and has more stable performance. Simulation results show that the performance curves of the proposed algorithm for parameters estimation are close to the corresponding CRBs, and the proposed algorithm can cope with more challenging cases when the line-of-sight (LOS) path does not exist.
Setting up the physical parameters of a simulator is key to achieving realistic results. Setting those parameters requires an understanding of how they affect the simulation results. Packet loss is a ...key performance metric in communication and networks fields. In this paper, we present an overview of how Veins VANET simulator simulates WiFi packet loss. This work explains the physical parameters set by the user in Veins and how Veins then uses these parameters to simulate the effects of attenuation and packet loss.
With the rapid development of wireless communication technology, various indoor location-based services (ILBSs) have gradually penetrated into daily life. Although many other methods have been ...proposed to be applied to ILBS in the past decade, WiFi-based positioning techniques with a wide range of infrastructure have attracted attention in the field of wireless transmission. In this survey, the authors divide WiFi-based indoor positioning techniques into the active positioning technique and the passive positioning technique based on whether the target carries certain devices. After reviewing a large number of excellent papers in the related field, the authors make a detailed summary of these two types of positioning techniques. In addition, they also analyse the challenges and future development trends in the current technological environment.
In many countries (including Japan), birthrates are declining and populations are aging. The declining 18-year-old population is a problem for university education. High-quality university education ...is essential for the development of a country's economic and technological strengths. Many studies have been conducted on the learning of active students. However, the number of students with various problems is expected to increase in the future. Nevertheless, the numbers of faculty and staff might remain the same. Therefore, supporting programs will be required for certain individual students. As such, universities should analyze activity-tracking data to identify the students requiring additional support. In this study, we introduce agents for monitoring each student's activities such as attending classes, eating, going to the library, and visiting a club. The agents monitor the students’ smartphones connected to campus WiFi for accessing the internet or other contents for campus students without using background smartphone applications. Using experiments, we confirm that we can always locate the students.
In the paper, an effective random statistical method is proposed for Indoor Positioning System (IPS) using WiFi fingerprinting. The proposed method consists of two phases: the offline handling ...process and the online positioning process. The offline handling process is used to collect a large number of WiFi signals at each indoor reference point and then create an offline database. This process handles the noise of WiFi signals and normalizes the database about location fingerprints for IPS. To further improve the accuracy of indoor positioning, the Mahalanobis distance is utilized to determine the indoor location for the online positioning process. Compared to the Weighted K-Nearest Neighbor (WKNN) algorithm based on Euclidean distance, experimental results show that it can improve the positioning accuracy using the proposed random statistical method. For the proposed random statistical method, the maximum positioning error is less than 0.75 meters. However, the average positioning error is 1.5 meters using the WKNN algorithm. In addition, it can effectively handle the noise of WiFi signals using the proposed random statistical method in different indoor environments.
Today, indoor localization technology based on WiFi signals has become more and more popular and applicable. It not only facilitates people's lives but also creates enormous economic value. However, ...during the propagation of the WiFi signal, it is easily interfered by obstacles, and the signal fluctuation is significant, resulting in low accuracy of positioning. To overcome these problems, we reduce the influence of environmental factors firstly. Then the positioning accuracy is improved by using the SVM model to distinguish the NLOS or LOS environment and employing the capsule networks to derive the users' positions with the WiFi 2.4G and 5G signals. As we all know, the WiFi 2.4G signal has excellent penetrability and is less affected by obstacles, while the WiFi 5G signal has excellent stability and small fluctuations. Therefore, we use the advantages of these two kinds of signals to derive the optimal suggestion by the capsule neural network, which is the learning system with minimum data sets needed. The experimental results show that the positioning effect of the two signals simultaneously is better than the positioning effect of a single signal. We also compare with the traditional indoor positioning methods and use the simulation data to carry out the robustness test, and the positioning accuracy reached 0.99 m in the field environment finally.
The locations of WiFi access points (APs) are important for WiFi positioning, especially when a propagation model is used. The parameters for the propagation model, such as the pathloss exponent and ...noise variance, usually are not available when localising APs in a new environment. A crowdsourcing-based prototype system is introduced that automatically generates WiFi databases using the uploaded data during normal usage of smartphones. In this system, the adjustment algorithm is originally used for the estimation of AP localisation and propagation parameters. Preliminary experiments show that the average AP localisation error of the prototype system is about 4.0 m in a typical indoor environment with considerably reduced time and labour costs compared with traditional methods.
WiFi offloading is a promising technique for addressing the mobile data explosion problem. In this paper, we consider two types of WiFi offloading techniques: 1) opportunistic WiFi offloading, where ...data offloading is conducted only when a mobile node opportunistically meets WiFi access points (APs); and 2) delayed WiFi offloading, where data transfer is delayed with the expectation of future AP contacts. We developed analytical models on WiFi offloading efficiency, which is defined as the ratio of the amount of offloaded data to the total amount of data. Extensive simulation results are given to validate the analytical models and to demonstrate the effects of the average cellular residence time, the delay bound, the variability of the WiFi residence time, the WiFi data rate, and the average session duration on WiFi offloading efficiency.
Human sensing using WiFi signal transmissions is attracting significant attention for future applications in e-healthcare, security, and the Internet of Things (IoT). The majority of WiFi sensing ...systems are based around processing of channel state information (CSI) data which originates from commodity WiFi access points (APs) that have been primed to transmit high data-rate signals with high repetition frequencies. However, in reality, WiFi APs do not transmit in such a continuous uninterrupted fashion, especially when there are no users on the communication network. To this end, we have developed a passive WiFi radar system for human sensing which exploits WiFi signals irrespective of whether the WiFi AP is transmitting continuous high data-rate Orthogonal Frequency-Division Multiplexing (OFDM) signals, or periodic WiFi beacon signals while in an idle status (no users on the WiFi network). In a data transmission phase, we employ the standard cross ambiguity function (CAF) processing to extract Doppler information relating to the target, while a modified version is used for lower data-rate signals. In addition, we investigate the utility of an external device that has been developed to stimulate idle WiFi APs to transmit usable signals without requiring any type of user authentication on the WiFi network. In this article, we present experimental data which verifies our proposed methods for using any type of signal transmission from a standalone WiFi device, and demonstrate the capability for human activity sensing.
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