WiFi-based Indoor Localization Research Articles

A region-wise indoor localization system based on unsupervised learning and ant colony optimization technique

WiFi-based indoor localization has gained widespread attention in the recent past with the ubiquitous deployment of WLAN. However, for sustainable performance, it is crucial to identify and maintain the important WiFi Access Points (APs). Interestingly, the localization capabilities of APs differ even within their area of coverage depending on the indoor ambience and building properties. Thus, the significance of an AP to the localization performance can be better assessed if the entire region is divided into optimal number of clusters/subregions. This type of problem is hardly investigated in the literature. Consequently, in this paper, the aforementioned challenges are addressed from the perspective of expert systems through applying machine learning and meta-heuristic techniques. Accordingly, our contribution is three-fold. First, we have designed a Region-wise Indoor Localization System (RwILS) in which a sub-regional division algorithm is proposed using DBSCAN approach. Second, a sub-region-wise important AP selection algorithm is designed based on Ant Colony Optimization technique. Third, a location estimation approach is proposed that first identifies the sub-region and then predicts the location point in that sub-region using supervised learning classifiers. A publicly available dataset, JUIndoorLoc is used for experimental evaluation. The localization accuracy of RwILS is improved to 95.68% while the state-of-the-art classifiers give 71% to 83% accuracy. RwILS also outperforms some recent works which further validates its effectiveness. Thus, this proposed technique can lead to an effective expert system in the domain of indoor localization.

Learning Domain-Invariant Model for WiFi-Based Indoor Localization

Learning Domain-Invariant Model for WiFi-Based Indoor Localization

An explainable hybrid deep learning architecture for WiFi-based indoor localization in Internet of Things environment

The indoor positioning service is one of the essential services needed in the Internet of Things ecosystem. Recently, many researchers have focused on the fingerprinting method, which is a method based on signal mapping with the Received Signal Strength Indicator (RSSI) values obtained from the WiFi access points. However, the fingerprinting method is particularly challenging due to some difficulties, such as RSSI variance over time, device diversity, and similarities of fingerprints in indoor networks. For this reason, machine learning and deep learning methods are used for many purposes, such as estimating the location of the building, floor, or the rooms. Detecting the location of a room or more than one reference point in a room becomes a more difficult problem because neighboring reference points’ fingerprints are very similar to each other. This study proposes a WiFi-based XAI-empowered deep learning architecture to predict the reference points in a room or corridor. We present a hybrid deep learning-based method that uses Long-Short-Term Memory to capture long-term dependencies between the signal features, and Convolutional Neural Network to extract local spatial signal patterns. Our deep learning aims to enrich fingerprinting data of each sample to capture more meaningful feature maps coming from different angles. Moreover, the method applies effective filtering and dimension scaling on the data to regulate the RSS values and capture more discriminative patterns using particle filter and sparse autoencoder. To provide local and global explanations for indoor localization estimations, the proposed architecture comprises two Explainable Artificial Intelligence techniques as Interpretable Model-Agnostic Explanations, and SHapley Additive exPlanations. The experimental results demonstrate that the proposed architecture achieves higher accuracy values for all datasets than the baseline deep learning methods.

PLAP: CSI-Based Passive Localization with Amplitude and Phase Information Using CNN and BGRU

With the increasing popularization and development of WiFi devices, nowadays WiFi-based indoor localization has become a hot topic. Traditional Wi-Fi-based localization technologies which utilize received signal strength indication suffer from indoor multi-path effects and result in localization performance degradation. Therefore, choosing the appropriate characteristic of the WiFi signal is crucial for indoor localization. To improve the localization accuracy, we propose PLAP, a passive localization method using amplitude and phase of channel state information (CSI). Specifically, Hampel filter is used to process the amplitude signals and linear transformation is employed for calibrating phases. To extract representative features from calibrated amplitude and phase signals, we developed a deep learning framework which combines a convolutional neural network (CNN) and a bi-directional Gated recurrent unit (BGRU) to estimate the location of an objective. The experimental results show that the proposed PLAP outperforms other baselines with real-world evaluation.

Rank Based Iterative Clustering (RBIC) for indoor localization

In the current era, technological advancements have resulted in a spike in various sensor data-intensive applications including indoor localization services. WiFi fingerprint-based localization for large indoor areas is primarily contingent on laborious site surveys. To ease the flow of research, grossly labeled data collected using a crowd-sourced method could be clustered. However, for a real-time localization approach, the challenge is to generate the clusters containing data samples that are really physically close. In this paper, a two-phase semi-supervised localization approach has been proposed, which is general enough to be applied to indoor localization datasets. In the offline phase, a Rank-Based Iterative Clustering (RBIC) algorithm has been proposed that generates a clustered dataset with a negligible chance of containing physically apart location points within a common cluster. RBIC can be viewed as a clustering ensemble model. Different clustering algorithms are selected as baseline algorithms and assigned unique ranks depending on well-known clustering scores to be fed as input to RBIC. In the online phase, the users’ location is estimated using machine learning (ML) classifiers based on the dynamic received signal strength indicator (RSSI) vector received through its handheld device transceiver. The system is evaluated with three benchmark datasets for WiFi based indoor localization. For the first dataset, JUIndoorLoc, 94% to 99% localization accuracy is achieved for individual supervised classifiers. For the second and third datasets, the ranges of obtained localization accuracies are 96% to 99% and 95% to 98% respectively.

Laser Range Scanners for Enabling Zero-overhead WiFi-based Indoor Localization System

Robust and accurate indoor localization has been the goal of several research efforts over the past decade. Toward achieving this goal, WiFi fingerprinting-based indoor localization systems have been proposed. However, fingerprinting involves significant effort—especially when done at high density—and needs to be repeated with any change in the deployment area. While a number of recent systems have been introduced to reduce the calibration effort, these still trade overhead with accuracy. This article presents LiPhi++ , an accurate system for enabling fingerprinting-based indoor localization systems without the associated data collection overhead. This is achieved by leveraging the sensing capability of transportable laser range scanners to automatically label WiFi scans, which can subsequently be used to build (and maintain) a fingerprint database. As part of its design, LiPhi++ leverages this database to train a deep long short-term memory network utilizing the signal strength history from the detected access points. LiPhi++ also has provisions for handling practical deployment issues, including the noisy wireless environment, heterogeneous devices, among others. Evaluation of LiPhi++ using Android phones in two realistic testbeds shows that it can match the performance of manual fingerprinting techniques under the same deployment conditions without the overhead associated with the traditional fingerprinting process. In addition, LiPhi++ improves upon the median localization accuracy obtained from crowdsourcing-based and fingerprinting-based systems by 284% and 418%, respectively, when tested with data collected a few months later.

WePos

On-demand delivery is an emerging business in recent years where accurate indoor locations of Gig couriers play an important role in the order dispatch and delivery process. To cater to this need, WiFi-based indoor positioning methods have become an alternative method for on-demand delivery thanks to extensive WiFi deployment in the indoor environment. Existing WiFi-based indoor localization and positioning methods are not suitable for large-scale on-demand delivery scenarios due to high costs (e.g., high labor cost to collect fingerprints) and limited coverage due to limited labeled data. In this work, we explore (i) massive crowdsourced WiFi data collecting from wearable or mobile devices of couriers with little extra effort and (ii) natural manual reports data in the delivery process as two opportunities to perform merchant-level indoor positioning in a weak-supervised manner. Specifically, we proposed WePos, an end-to-end weak-supervised-based merchant-level positioning framework, which consists of the following three parts: (i) a Bidirectional Encoder Representations from Transformers (BERT) based pre-training module to learn latent embeddings of WiFi access points, (ii) a contrastive label self-generate module to produce pseudos for WiFi scanning lists by matching similarity embedding clustering results and couriers' reporting behaviors. (iii) a deep neural network-based classifier to fine-tune the whole training process and conduct online merchant-level position inference. To evaluate the performance of our system, we conduct extensive experiments in both a large-scale public crowdsourcing dataset with over 50 GB of WiFi signal records and a real-world WiFi crowdsourced dataset collected from Eleme, (i.e., one of the largest on-demand delivery platforms in China) in four multi-floor malls in Shanghai. Experimental results show that WePos outperforms state-of-the-art baselines in the merchant-level positioning performance, offer up to 91.4% in positioning accuracy.

Geomagnetic Positioning-Aided Wi-Fi FTM Localization Algorithm for NLOS Environments

WiFi-based indoor localization using the fine time measurement (FTM) protocol has become a popular technique. However, in harsh Non-Line-of-Sight (NLoS) environments, WiFi FTM positioning (WFP) suffers from poor performance. In this letter, a novel WiFi FTM localization method with the assistance of geomagnetic positioning (GP) is proposed. To ensure the accuracy of GP, an enhanced mind evolutionary algorithm (EMEA) is designed. A fine-grained WiFi position estimation method using the overlapping searching area (OSA) and the coincident points selection strategy is proposed. Experimental results show that the EMEA-based GP improves the localization performance of WFP in NLOS environments, the mean localization error (ME) and root-mean-square error (RMSE) of the GP-aided WFP are 1.82 m and 2.08 m, respectively. Compared with the classic WFP using the weighted least square (WLS) method, the ME and RMSE are reduced by 51.7% and 52.4%, respectively.

A survey on ubiquitous WiFi-based indoor localization system for smartphone users from implementation perspectives

Indoor localization has the capability to change the way of providing location-based services in a closed environment and has more potential than that of GPS if the present shortcomings can be overcome. Thus, developing a ubiquitous Indoor Localization System (ILS) is the need of the day. WiFi-based indoor localization using smartphones is a promising approach to achieve ubiquity since smartphones are widely available today and most of the buildings are WiFi enabled. However, the significant variation of WiFi signal strengths with ambient conditions as well as device configuration badly affects the localization accuracy. Hence, a ubiquitous ILS, which would be widely available and would not require any specialized hardware support is still out of reach. Thus, this paper surveys state-of-the-art WiFi-based indoor localization techniques with a critical analysis of their applicability as a ubiquitous system. Consequently, the main objective of this paper is to highlight the key research challenges of implementing a WiFi-based ILS when system ubiquity is the prime concern with a discussion on future direction. Some of the experiments have been implemented on a real-life dataset in order to indicate the implementation challenges of such a system.

A novel feature based ensemble learning model for indoor localization of smartphone users

For WiFi-based indoor localization, optimal selection of features leads to the increased perceptibility of the localization procedure. It is essential to capture the important sets of Access Points (APs) that best defines the floor map for the positioning process. To maintain sustainable localization, the selection of APs enables scaling the solution and reducing the maintenance cost. In the present work, our contribution is twofold- the power of Particle Swarm Optimization is utilized for the selection of important APs. Then, a feature-based ensemble model is designed for the selected subsets of APs to retain the generality of localization performance. The base learners capture the different ambiance in the training and testing process. Extensive experimentation was carried out using the collected dataset from multiple smartphone devices. The proposed feature selection and training pipeline has also been tested with two popular benchmark datasets- UJIIndoorLoc and JUIndoorLoc. Results indicate that the proposed feature-based ensemble model could achieve 86%–96% accuracy with around 50%–65% reduction in APs for the datasets. The mean absolute error (MAE) indicates the distance between the predicted and actual location points. It is found to be 2.68 m, that is, neighboring location points, which is quite acceptable for user localization in indoor spaces.

An Overview on Position Location: Past, Present, Future

Prior to the 21st century, positioning technologies had limited applications including air traffic control, air and sea navigation, satellite communications and related military uses. Today, positioning technologies have deeply merged with daily life and enabled many novel sensors, systems and services. For example, navigation systems are the enablers of road traffic prediction, assisted and autonomous driving, and several aspects of healthcare. They have also facilitated worldwide services provided by companies such as Uber and Lyft. In fact, in many aspects of modern life, localization systems are deemed essential to day-to-day living and are contributing to our general well-being, the economy, and security. Accordingly, position location technologies have become key components of many worldwide industries. These positioning technologies include the Global Positioning System (GPS), WiFi-based indoor localization, cell-phone based localization (including the fusion of GPS, cell-tower based localization and dead-reckoning), and inertial/dead-reckoning techniques. Tracking technologies are also considered key components for localization, as are the more recently integrated concepts of machine learning and artificial intelligence. This paper provides a review of the history of localization, the main technological enablers of localization and assesses the future directions of localization methods.

Practical server-side WiFi-based indoor localization: Addressing cardinality & outlier challenges for improved occupancy estimation

Server-side WiFi-based indoor localization offers a compelling approach for passive occupancy estimation (i.e., without requiring active participation by client devices, such as smartphones carried by visitors), but is known to suffer from median error of 6–8 meters. By analyzing the characteristics of an operationally-deployed, WiFi-based passive indoor location system, based on the classical RADAR algorithm, we identify and tackle 2 practical challenges for accurate individual device localization. The first challenge is the low-cardinality issue, whereby only the associated AP generates sufficiently frequent RSSI reports, causing a client to experience large localization error due to the absence of sufficient measurements from all nearby APs. The second is the outlier resolution issue, whereby clients physically located outside the fingerprinted region but attached to the WiFi network are localized erroneously to a fingerprinted landmark. To tackle the low-cardinality challenge, we identify the stationary-period of devices, and augment the device’s live AP-reported RSSI readings, during such stationary periods, with the useful but ‘stale’ values reported by neighboring APs. To eliminate extraneous outlier devices, we apply a threshold-based filtering strategy, where the RSSI thresholds for all interior points are derived using a combination of a weighted path-loss propagation model and the Voronoi tessellation of the fingerprinting map. In addition, to overcome intermittent false positives/negatives in localization or subsequent occupancy estimation, we apply two additional techniques: (a) temporal smoothing of location estimates over a time period, and (b) identification and removal of static devices. We experimentally evaluate these combined set of techniques on 3 different indoor work, collaboration & residential spaces, and show how these techniques improve the robustness of location tracking, which subsequently translates into an approx. 80+% reduction in the overall occupancy estimation error.

Indoor Positioning System Based on Standardizing Waveform Tendency

Estimating the indoor position of users in commercial buildings remains a significant challenge to date. Although the WiFi‐based indoor localization has been widely explored in many works by employing received signal strength (RSS) patterns as the features, they usually lead to inaccurate results as the RSS could be easily affected by the indoor environmental dynamics. Besides, existing methods are computationally intensive, which have a high time consumption that makes them unsuitable for real‐life applications. In order to deal with those issues, we propose to use standardizing waveform tendency (SWT) of RSS for indoor positioning. We show that the proposed SWT is robust to the noise generated by the dynamic environment. We further develop a novel smartphone indoor positioning system by integrating SWT and kernel extreme learning machine (KELM) algorithm. Extensive real‐world positioning experiments are conducted to demonstrate the superiority of our proposed model in terms of both positioning accuracy and robustness to environmental changes when comparing with state‐of‐the‐art baselines.

Improved Gaussian mixture modeling for accurate Wi-Fi based indoor localization systems

This paper deals with the problem of device’s location using Wi-Fi signal strength in an indoor environment. In order to improve the location accuracy a new fingerprinting-probabilistic algorithm is proposed. In this algorithm, the probability distribution of the Received Signal Strength (RSS) by a Mobile User (MU) from several Access Points (AP) is approximated using the Gaussian Mixture Model (GMM) approach. This probability distribution is then exploited to improve the location estimation of the mobile user. To tackle the initialization problem of the Expectation Maximization (EM) algorithm, which is used to estimate the Gaussian mixture parameters, a deterministic initialization method is proposed. This method named Manual initialization (MI) initializes manually the mixture parameters directly from the data. To cope with the MI shortcomings, an Adaptive Initialization (AI) technique is proposed. The performance of the resulting method, named the Improved GMM (IGMM) is evaluated experimentally and compared to that of other robust methods. The obtained results illustrate the efficiency of the IGMM method. Compared to the standard initialization technique, the MI and AI initialization techniques improve the location accuracy by 14.4% and 18.4%, respectively. Compared to other location methods, the improvement brought about by the IGMM method, varies from 5.1% to 17.7%, when the MI technique is used, and from 10% to 21.5%, when the AI technique is used.

WiBorder

Recent research has shown great potential of exploiting Channel State Information (CSI) retrieved from commodity Wi-Fi devices for contactless human sensing in smart homes. Despite much work on Wi-Fi based indoor localization and motion/intrusion detection, no prior solution is capable of detecting a person entering a room with a precise sensing boundary, making room-based services infeasible in the real world. In this paper, we present WiBorder, an innovative technique for accurate determination of Wi-Fi sensing boundary. The key idea is to harness antenna diversity to effectively eliminate random phase shifts while amplifying through-wall amplitude attenuation. By designing a novel sensing metric and correlating it with human's through-wall discrimination, WiBorder is able to precisely determine Wi-Fi sensing boundaries by leveraging walls in our daily environments. To demonstrate the effectiveness of WiBorder, we have developed an intrusion detection system and an area detection system. Extensive results in real-life scenarios show that our intrusion detection system achieves a high detection rate of 99.4% and a low false alarm rate of 0.68%, and the area detection system's accuracy can be as high as 97.03%. To the best of our knowledge, WiBorder is the first work that enables precise sensing boundary determination via through-wall discrimination, which can immediately benefit other Wi-Fi based applications.

Wi-Fi based Indoor Localization for Location-based Smart Notification

Indoor localization has been researched widely in the recent two decades due to its wide range of applications such as navigation, elder care, advertising. This work presents a utilization of indoor positioning for a location-based smart notification purposed, deployed in a meeting room booking application. Our localization method is based on Wi-Fi fingerprint, tailored to our application needs to alleviate the drawback of its tedious offline phase. The initial implementation is done with limited number of recorded locations. The testing shows that the meeting room booking application works well, with the localization detecting user’s location correctly aside from when poor signal condition occurs.

Machine Learning Based Indoor Localization using Wi-Fi and Smartphone

Wi-Fi based indoor positioning with the help of access points and smart devices have become an integral part in finding a device or a person’s location. Wi-Fi based indoor localization technology has been among the most attractive field for researchers for a number of years. In this paper, we have presented Wi-Fi based in-door localization using three different machine-learning techniques. The three machine learning algorithms implemented and compared are Decision Tree, Random Forest and Gradient Boosting classifier. After making a fingerprint of the floor based on Wi-Fi signals, mentioned algorithms were used to identify device location at thirty different positions on the floor. Random Forest and Gradient Boosting classifier were able to identify the location of the device with accuracy higher than 90%. While Decision Tree was able to identify the location with accuracy a bit higher than 80%.

Progressive RSS Data Augmenter With Conditional Adversarial Networks

Accuracies of most fingerprinting approaches for WiFi-based indoor localization applications are affected by the qualities of fingerprint databases, which are time-consuming and labor-intensive. Recently, many methods have been proposed to reduce the localization accuracy reliance on the qualities of the established fingerprint databases. However, studies on establishing fingerprint databases are relatively rare under the condition of sparse reference points. In this paper, we propose a novel data augmenter based on the adversarial networks to build fingerprint databases with sparse reference points. Additionally, two conditions of these networks are designed to generate data effectively and stably, which are 0–1 sketch and Gaussian sketch. Based on the networks, we design two augmenters with different cyclic training strategies to evaluate the augmenting effects comparatively. Meanwhile, five quantitative evaluation metrics of the augmenters are proposed from two perspectives of the artificial experiences and the data features, and some of them are also used as the gradient penalties for generators. Finally, experiments corresponding to these metrics and localization accuracies demonstrate that the data augmenter with the 0–1 sketch adversarial network is more efficient, effective and stable totally.

Accurate WiFi-based indoor localization by using fuzzy classifier and mlps ensemble in complex environment

With the rapid increase of mobile devices, there are a lot of location-based applications. Therefore, the localization of indoor environments is an increasingly important problem. WiFi-based fingerprint method which is cost-effective without investing additional infrastructure has drawn significant attention over the past decade. However, due to the interference of moving objects and so-called co-channel interference, incurring the high variability of WiFi signals over time for the same location and make it hard to obtain the satisfied accuracy and mean error. To remedy those problems, in this paper, we propose an ensemble model consisting of fuzzy classifier and multi-Multi-layer perceptron (MLPs) for indoor parking localization. The clustering algorithm is applied to get the similar areas and form the local model, and then the ensemble learning is trained in offline stage. In online stage, the ensemble learning is utilized to get real position of unlabeled input. The experiment has been conducted at indoor parking of Riyueguang mall in Chongqing, China. This proposed approach yields higher accuracy and lower mean error, and makes it possible to apply WiFi-based localization in real indoor parking.

Locate the Mobile Device by Enhancing the WiFi-Based Indoor Localization Model

Due to the advent and pervasive deployment of wireless local area networks, WiFi-based indoor localization systems have received increasing attention in the last few years. However, their localization accuracy has always been a challenging issue. In addition, because of diverse interference such as multipath effects, the block of signals, an unstable or weak signal in itself, etc., not all the access points (APs) are informative for the localization. Faced with these problems, we propose a WiFi-based localization model by modifying the large localization errors and enhancing the Gaussian process regression (MEGPR). 1) To select the AP subsets that contribute more to the localization and further reduce the computational load, the AP discrimination criterion (APDC) is introduced to quantify the discernibility of the APs detected in the workspace and filter out the APs with low discrimination. 2) Second, to enhance the localization model, the localization residual is fed and learnt by the model. 3) Furthermore, the large localization errors are mitigated by the location modification method (LMM). Experiments were conducted in a real environment with an area of more than 1200 m2 and the results show that compared with other existing localization models, the average localization error of the proposed MEGPR model is minimum, which further verifies the effectiveness of the proposed MEGPR localization model.