Single Wi-Fi Access Point Research Articles

2-D Localization System for Mobile IoT Devices Using a Single Wi-Fi Access Point With a Passive Frequency-Scanned Antenna

A novel two-dimensional indoor location system for IoT devices employing a single Wi-Fi Access Point is presented. The x, y position is estimated with a combination of Directionof-Arrival (DoA) and Time-of-Flight (ToF) algorithms. The azimuthal DoA is estimated using a modified MUSIC technique, which requires the acquisition of the Received Signal Strength Indicator (RSSI) using different Wi-Fi frequency channels in the 2.4 GHz band, together with a dual-port frequency-scanning antenna (FSA). This allows a wide Field-of-View (FoV) of 180: for the DoA. To estimate the ranging distance, an algorithm based in the 802.11mc Fine Time Measurement (FTM) protocol is employed to consider the effects of the channel hopping. Therefore, the proposed system does not rely on Channel State Information (CSI) or complex IQ information, which is not available in commercial Wi-Fi APs. The proposed system has been fully implemented with a commercial Wi-Fi AP and tested in a practical scenario, proving a root-mean-square error (RMSE) when estimating the localization of mobile devices of only 1.43m, in a wide area of 12 m x 12 m.

Device-Free Human Motion Detection Using Single Link WiFi Channel Measurements for Building Energy Management

Human motion is a vital parameter signalling indoor occupancy in occupant centric building energy management systems. In this letter, a WiFi based device free sensing model employing ESP32-a low cost embedded IoT device, is proposed as a suitable retrofit for human motion sensing in new and existing buildings. A low complexity feature set derived from WiFi received signal strength indicator and channel state information samples collected by ESP32 is utilized for training ensemble machine learning models. The proposed model shows an accuracy of up to 99.4% in classifying human motion in 3 different rooms of a residential building, with a single WiFi access point. The proposed model can be employed for motion driven smart load control and for a typical residential occupancy scenario, this approach exhibits a potential monthly electricity savings of up to 66.26% and a three times reduction in CO2 emissions.

Design of LTE-Wi-Fi Aggregation with Multiple Wi-Fi APs for Heterogenous Network

The growth in mobile data traffic is forcing network operators to find ways to deliver higher bandwidth, better coverage, and better quality of service (QoS) at lower development costs. One method that can be used to meet this need is the aggregation technique. Aggregation techniques that run on several different technologies are called heterogeneous networks (HetNet). Recently, some advanced technologies have been proposed to aggregate different long-term evolution (LTE) carriers or different radio access technologies (RAT) to generate higher bandwidth. Many efforts have been made to improve the throughput of heterogeneous networks. One of the factors that affects the maximum LTE-WLAN aggregation (LWA) throughput is the Wi-Fi access point (AP) signal strength. Since the power of small cells is very low, the LWA range is not very large, so the LWA range needs to be increased. This paper proposes the solution that can be implemented by using multiple Wi-Fi Aps scheme. From the experiments, it found that LWA with multiple Wi-Fi APs has better signal strength than LWA with a single Wi-Fi AP. LWA with multiple Wi-Fi APs further increase the coverage of the network.

Indoor Fingerprinting With Bimodal CSI Tensors: A Deep Residual Sharing Learning Approach

Wi-Fi-based indoor fingerprinting is attracting increasing interest in the research community due to the ubiquitous access in indoor environments. In this article, we propose ResLoc, a deep residual sharing learning-based system for indoor fingerprinting using bimodal channel state information (CSI) tensor data. The proposed ResLoc system employs CSI tensor data, including the angle of arrival and amplitude, collected from a small set of training locations with known coordinates to train the proposed dual-channel deep residual sharing learning model. The proposed new model extends the traditional deep residual learning model by incorporating two or more channels and let the channels exchange their residual signals after each residual block. Unlike prior deep-learning-based fingerprinting schemes, ResLoc only requires for training one group of weights for all the training locations. The proposed ResLoc system is implemented with commodity Wi-Fi devices and evaluated with extensive experiments in three representative indoor environments. The experimental results validate that the proposed ResLoc system can achieve high localization accuracy using a single Wi-Fi access point in indoor environments.

Wi-Fi-Inertial Indoor Pose Estimation for Microaerial Vehicles

This article presents an indoor pose estimation system for microaerial vehicles (MAVs) with a single Wi-Fi access point. Conventional approaches based on computer vision are limited by illumination conditions and environmental texture. Our system is free of visual limitations and instantly deployable, working upon existing Wi-Fi infrastructure without any deployment cost. Our system consists of two coupled modules. First, we propose an angle-of-arrival (AoA) estimation algorithm to estimate MAV attitudes and disentangle the AoA for positioning. Second, we formulate a Wi-Fi-inertial sensor fusion model that fuses the AoA and the odometry measured by inertial sensors to optimize MAV poses. Considering the practicality of MAVs, our system is designed to be real-time and initialization-free for the need of agile flight in unknown environments. The indoor experiments show that our system achieves the accuracy of pose estimation with the position error of 61.7 cm and the attitude error of $0.92^{\circ }$ .

Indoor Localization With a Single Wi-Fi Access Point Based on OFDM-MIMO

In this paper, we propose S-Phaser, an indoor localization system that uses a single Wi-Fi access point (AP) to locate terminals. Compared to traditional indoor localization systems based on the fingerprint positioning technology, S-Phaser does not need to deploy a large number of APs and has better localization accuracy. S-Phaser utilizes channel state information (CSI) to compute the direct path length between a single AP and terminals, thereby efficiently improving the accuracy in the line-of-sight (LOS) scenario. S-Phaser is still able to get satisfied accuracy in the non-LOS scenario even not as good as in the LOS scenario. Using the feature of multiple channels in 802.11n, we design an interpolation-based algorithm, named as the interpolation elimination method to calibrate signal phases from CSI, and then we use two different algorithms, the Chinese residue theorem and broadband angle ranging, to compute the real distance of the direct path from the calibrated phases. Then, S-Phaser uses a geometric positioning algorithm to determine the user's location. To verify the practicability of the proposed S-Phaser, we set up a localization system in an actual environment. S-Phaser can improve the median localization error to 1.5 m with a single Wi-Fi AP.

Practical opportunistic content dissemination performance in dense network segments

Many of the existing opportunistic networking systems have been designed assuming a small number links per node and have trouble scaling to large numbers of potential concurrent communication partners. In the real world we often find wireless local area networks with large numbers of connected users – in particular in open Wi-Fi networks provided by cities, airports, conferences and other venues. In this paper we build a 50 client opportunistic network in a single Wi-Fi access point and use it to uncover scaling problems and to suggest mechanisms to improve the performance of single segment dissemination. Further, we present an algorithm for breaking down a single dense segment dissemination problem into multiple smaller but identical problems by exploiting resource (e.g., Wi-Fi channel) diversity, and validate our approach via simulations and testbed experiments. The ability to scale to high density network segments creates new, realistic use cases for opportunistic networking applications.

FM radio for indoor localization with spontaneous recalibration

The position of mobile users has become highly important information in pervasive computing environments. Indoor localization systems based on Wi–Fi signal strength fingerprinting techniques are widely used in office buildings with an existing Wi–Fi infrastructure. Our previous work has proposed a solution based on exploitation of a FM signal to deal with environments not covered with Wi–Fi signal or environments with only a single Wi–Fi access point. However, a general problem of indoor wireless positioning systems pertains to signal degradation due to the environmental factors affecting signal propagation. Therefore, in order to maintain a desirable level of localization accuracy, it becomes necessary to perform periodic calibrations of the system, which is either time consuming or requires dedicated equipment and expert knowledge. In this paper, we present a comparison of FM versus Wi–Fi positioning systems and a combination of both systems, exploiting their strengths for indoors positioning. We also address the problem of recalibration by introducing a novel concept of spontaneous recalibration and demonstrate it using the FM localization system. Finally, the results related to device orientation and localization accuracy are discussed.

Indoor location tracking using RSSI readings from a single Wi-Fi access point

This paper describes research towards a system for locating wireless nodes in a home environment requiring merely a single access point. The only sensor reading used for the location estimation is the received signal strength indication (RSSI) as given by an RF interface, e.g., Wi-Fi. Wireless signal strength maps for the positioning filter are obtained by a two-step parametric and measurement driven ray-tracing approach to account for absorption and reflection characteristics of various obstacles. Location estimates are then computed using Bayesian filtering on sample sets derived by Monte Carlo sampling. We outline the research leading to the system and provide location performance metrics using trace-driven simulations and real-life experiments. Our results and real-life walk-troughs indicate that RSSI readings from a single access point in an indoor environment are sufficient to derive good location estimates of users with sub-room precision.