Large-Scale Location-Aware Services in Access:Hierarchical Building/Floor Classification and

Location Estimation using Wi-Fi FingerprintingBased on Deep Neural Networks

Kyeong Soo Kim∗, Ruihao Wang∗, Zhenghang Zhong∗, Zikun Tan∗, Haowei Song†, Jaehoon Cha∗, and Sanghyuk Lee∗∗Department of Electrical and Electronic Engineering

Xi’an Jiaotong-Liverpool UniversitySuzhou, 215123, P. R. China

†Department of Computer Science and Software EngineeringXi’an Jiaotong-Liverpool University

Suzhou, 215123, P. R. China

Abstract—One of key technologies for future large-scalelocation-aware services in access is a scalable indoor localizationtechnique. In this paper, we report preliminary results fromour investigation on the use of deep neural networks (DNNs)for hierarchical building/floor classification and floor-levellocation estimation based on Wi-Fi fingerprinting, which wecarried out as part of a feasibility study project on Xi’anJiaotong-Liverpool University (XJTLU) Campus Informationand Visitor Service System. To take into account the hierarchicalnature of the building/floor classification problem, we proposea new DNN architecture based on a stacked autoencoder forthe reduction of feature space dimension and a feed-forwardclassifier for multi-label classification with argmax functionsto convert multi-label classification results into multi-classclassification ones. We also describe the demonstration of aprototype DNN-based indoor localization system for floor-levellocation estimation using real received signal strength (RSS)data collected at one of the buildings on the XJTLU campus.The preliminary results for both building/floor classificationand floor-level location estimation clearly show the strengths ofDNN-based approaches, which can provide near state-of-the-artperformance with less parameter tuning and higher scalability.

Index Terms—Indoor localization, Wi-Fi fingerprinting, deeplearning, neural networks, multi-label classification, multi-classclassification.

I. INTRODUCTION

In an indoor environment where there is no line-of-sightsignal from global positioning systems (GPSs), received signalstrengths (RSSs) from wireless network infrastructure can beused for localization through fingerprinting: For example, avector of a pair of a service set identifier (SSID) and anRSS for a Wi-Fi access point (AP) measured at a locationbecomes its location fingerprint. A position of a user/devicethen can be estimated by finding the closest match betweenits RSS measurement and the fingerprints of known locationsin a database [1].

When the indoor localization is to cover a large campusor a big shopping mall where there are lots of buildings

with many floors, the scalability of fingerprinting techniquesbecomes an important issue. The current state-of-the-art Wi-Fi fingerprinting techniques assume a hierarchical approach tothe indoor localization, where the building, floor, and position(e.g., a label or coordinates) of a location are estimated oneat a time. In [2], for instance, building estimation is done bythe following process: Given the AP with the strongest RSS ina measured fingerprint, we first build a subset of fingerprintswhere the same AP has the strongest RSS; then, we countthe number of fingerprints associated to each building and setthe estimated building to be the most frequent one from thecounting. Similar procedures are also proposed to estimate afloor inside the building. According to the results in [2], thebest building and floor hit rates achieved for the UJIIndoorLocdataset [3] are 100% and 94%, respectively.

One of the major challenges in Wi-Fi fingerprinting is howto deal with the random fluctuation of a signal, the noisefrom multi-path effects, and the device dependency in RSSmeasurements. Unlike traditional solutions relying on com-plex filtering and time-consuming manual parameter tuning,machine learning techniques — especially the popular deepneural networks (DNNs) — can provide attractive solutions toWi-Fi fingerprinting due to less parameter tuning and betterscalability in larger environments [4]–[6].

In this paper, we introduce a feasibility study on theXi’an Jiaotong-Liverpool University (XJTLU) Campus Infor-mation and Visitor Service System as a test bed for large-scale location-aware services in access. We report preliminaryresults from the investigation on the use of DNNs for hier-archical building/floor classification and floor-level locationestimation, which we carried out as part of this feasibilitystudy. We also describe the demonstration of a prototypeDNN-based indoor localization system for floor-level locationestimation using real RSS data, which we measured at one ofthe buildings on the XJTLU campus.

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XJTLU Camus Information and Visitor Service SystemFingerprinting Server(SSID1, RSS1) (SSID2, RSS2) (SSIDN, RSSN) RSSMeasurements EstimatedLocationLocation-AwareServicesClient(User) XJTLUIntranetICEebridgeportal…Front-end and Middleware… Service Request(RSS Measurements, …)(a)Engineering Building 3F Lecture Theatre(b)

Fig. 1. XJTLU campus information and visitor service: (a) Architecture and(b) location-aware service examples.

The outline of the rest of the paper is as follows: In Sec. II,we introduce the feasibility study project on the XJTLUCampus Information and Visitor Service System. In Sec. III,we discuss the hierarchical classification of building and floorbased on a multi-label classifier with argmax functions.Sec. IV describes the demonstration of a prototype DNN-basedindoor localization system for floor-level location estimation.Sec. V summarizes our work described in this paper.

II. XJTLU CAMPUS INFORMATION AND VISITOR SERVICESYSTEM: A TEST BED FOR LARGE-SCALELOCATION-AWARE SERVICES IN ACCESS

Location awareness is one of enabling technologies for fu-ture smart and green cities; understanding where people spendtheir times and how they interact with environments is criticalto realizing this vision. At XJTLU, a group of researchers fromElectrical and Electronic Engineering, Computer Science &Software Engineering, and Urban Planning, together with anexternal researcher from Electrical and Electronic Engineeringof City University of London, U.K., has been carrying out afeasibility assessment and road mapping for XJTLU CampusInformation and Visitor Service System with the aim of iden-tifying key component technologies and preparing plans forits implementation as a test bed for large-scale location-awareservices in access and its use cases for behavioral study ofstudents and visitors on the campus.

Fig. 1 shows an overall architecture of the XJTLU CampusInformation and Visitor Service System and location awareservice examples provided by the system. At the core of thesystem is indoor localization based on wireless fingerprinting,

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Fig. 2. A DNN architectures for building/floor classification: (a) A stackedautoencoder (SAE) for the reduction of feature space dimension; (b) an overallarchitecture consisting of the encoder part of the SAE and a feed-forwardclassifier for multi-class classification with flattened building-floor labels [6].

which utilizes RSSs from Wi-Fi APs to estimate a userlocation: As shown in Fig. 1 (a), RSSs from nearby APs aresubmitted by a user or an App running background in theuser’s mobile device to the system as part of a service request.The submitted RSS measurements, then, are compared withthe RSS samples of known locations (i.e., location fingerprints)stored in the database at the fingerprinting server to find theclosest match; the location ID of the closest match (e.g.,“EB306”1) is returned as the user’s estimated location. Basedon the estimated location, the system provides location-awareservices by integrating existing data and services available onXJTLU Intranet and tailoring them for the location as shownin Fig. 1 (b).

III. HIERARCHICAL CLASSIFICATION OF BUILDING ANDFLOOR USING A MULTI-LABEL CLASSIFIER

In [6], the authors proposed a DNN system for building/floorclassification that uses a stacked autoencoder (SAE) before afeed-forward classifier to reduce the dimension of the featurespace for robust and precise classification as shown in Fig. 2.Note that, after training with RSSs as both input and outputdata, only the gray-colored nodes are used as an encoder forfeature space dimension reduction as shown in Fig. 2 (b). Theperformance of the proposed DNN system was verified withthe UJIIndoorLoc dataset [3] and is shown to be comparable

1It means the room 306 on the third floor of the EB building.

to that of the state-of-the-art systems (i.e., about 92% ofcombined building and floor recognitions).

The work reported in [6] is the first in applying a DNN to thebuilding/floor classification of large-scale indoor localizationand, as such, reveals a couple of areas of further improvementas follows:

First, the DNN architecture is not optimized enough. Whilethey achieve 92% accuracy in building/floor classificationwith an SAE (i.e., only the encoder part) containing threehidden layers of 256, 128, and 64 neurons and a feed-forwardclassifier with two hidden layers of 128 neurons per each,we could achieve a nearly equivalent performance with anSAE having just two hidden layers of 64 and 4 neurons anda classifier without any hidden layer (i.e., the output layer ofthe SAE is directly connected to the system output layer).

Second, the proposed DNN system in [6] does not take intoaccount the hierarchical nature of the classification problemat hand due to its calculating the loss and the accuracyover flattened building-floor labels (e.g., (Bi, Fj)→“Bi-Fj”)2.In other words, the misclassification of building and that offloor have equal loss during the training phase and results inthe same accuracy during the evaluation phase.

To take into account the hierarchical nature of the build-ing/floor classification, we are now considering two possibleapproaches: One approach is the use of a hierarchical lossfunction (e.g., a loss function with different weights forbuilding and floor) with the existing multi-class classifier DNNarchitecture and flattened labels. The other is the use of multi-label classification with argmax functions to convert resultsback to those of multi-class classification.3

As for the first approach, we found that the hierarchicalloss function for flattened labels does not provide a well-defined gradient function, which forces us to use evolutionaryalgorithms (e.g., genetic algorithm (GA) [8] and particleswarm optimization (PSO) [9]) for training of DNN weights.Due to its many tradeoffs between complexity and flexibilityresulting from the use of evolutionary algorithms in DNNweight training, this approach could be an interesting topicfor long-term research.

In case of the use of the multi-label classification, applyingdifferent weights to building and floor labels during thetraining phase is rather straightforward, and it is also easyto convert the results of multi-label classification into thoseof multi-class classification using argmax functions. Fig. 3shows a newly proposed hierarchical building/floor classifierbased on multi-label classification framework with argmaxfunctions.

The classification of building and floor with the proposedarchitecture shown in Fig. 3 is achieved as follows: The threebuilding and five floor identifiers in the UJIIndoorLoc datasetare one-hot encoded into an eight-dimensional vector (e.g.,

2Bi and Fj denote building and floor labels, respectively.3In multi-class classification (also called single-label classification), an

instance is associated with only a single label from a set of disjoint labels;in multi-label classification, on the other hand, an instance may be associatedwith multiple labels [7].

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Fig. 3. A DNN architectures for hierarchical building/floor classification basedon an SAE for the reduction of feature space dimension and a feed-forwardclassifier for multi-label classification.

TABLE IPRELIMINARY RESULTS OF HIERARCHICAL BUILDING/FLOOR

CLASSIFICATION

Class Weight AccuracyBuilding Floor Overall Building Floor

1 1 8.991899e-01 9.918992e-01 9.036904e-012 1 9.063906e-01 9.918992e-01 9.090909e-015 1 9.207921e-01 9.972997e-01 9.234923e-01

10 1 9.333933e-01 9.972997e-01 9.342934e-0120 1 9.216922e-01 9.936994e-01 9.225923e-01

“001|01000” for the third building and the second floor withinit) and classified with different class weights for buildings(i.e., for the first three digits) and for floors (i.e., for the lastfive digits); the one-hot-encoded vector from the multi-labelclassifier is split into a three-dimensional building and a five-dimensional floor vectors, and the index of a maximum valueof each vector is returned as a classified class by the argmaxfunction.

The advantages of the proposed building/floor classifieris two-fold: First, the number of output nodes is smallercompared to that of [6]: In case of the UJIIndoorLoc dataset,the number of output nodes is eight (i.e., the number ofbuildings (3) plus the maximum of the numbers of floors forbuildings (5)), while that of [6] is thirteen; the difference couldbe much larger for large-scale access where there are lots ofbuildings (i.e., � 3) with many floors.

Second, as described, the allocation of class weights canbe done separately for buildings and floors, which providesmore degrees of freedom in classification.4 In this regard,we are investigating the dependence of building and flooraccuracies as well as overall accuracy on class weights throughextensive experiments. Table I shows preliminary results, andTable II summarizes common DNN parameter values for thoseexperiments.

According to the results shown in Table I, the class weightsof 10 and 1 for buildings and floors provide the best results,but the dependence of accuracies on the class weights seems

4Note that the class weights are originally for cost adjustment for imbal-anced data, not for hierarchical classification.

TABLE IIDNN PARAMETER VALUES FOR HIERARCHICAL BUILDING/FLOOR

CLASSIFICATION

DNN Parameter ValueRatio of Training Data to Overall Data 0.70Number of Epochs 20Batch Size 10SAE Hidden Layers 64-8-64SAE Activation Rectified Linear (ReLU)SAE Optimizer ADAM [10]SAE Loss Mean Squared Error (MSE)Classifier Hidden Layers NoneClassifier Optimizer ADAMClassifier Loss Binary CrossentropyClassifier Dropout Rate 0.20

Server( Algorithm & Database )

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Fig. 4. A prototype of DNN-based indoor localization system for floor-levellocation estimation.

not to be big. This suggests that we need to carry out a moresystematic investigation on this before making any meaningfulconclusions.

IV. DEMONSTRATION OF A DNN-BASED INDOORLOCALIZATION SYSTEM FOR FLOOR-LEVEL LOCATION

ESTIMATION

To show the feasibility of DNN-based indoor localization,we have implemented a prototype shown in Fig. 4 anddemonstrated floor-level location estimation with real RSSdata measured on the fourth floor of the EE building on thenorth campus of XJTLU.

The fingerprinting server is implemented using Flask [11],a Python-based microframework for web development, withSQLite [12] database engine; we chose Flask because we alsoused Python-based deep learning frameworks, i.e., keras [13]and TensorFlow [14], for the implementation of DNN-basedlocalization algorithms, which are nearly identical to the oneused for building/floor classification in [6]. When a client, i.e.,an Android App running on a user’s mobile phone, submitsscanned RSSs, the server estimates its location based on theimplemented localization algorithm and returns the estimatedlocation back to the client.

Fig. IV shows a floor layout of the fourth floor of the EEbuilding where Wi-Fi fingerprints were collected. The areashighlighted in yellow are part of seven locations selected tocollect data and test the prototype; both isolated rooms andopen public spaces are considered. During the measurement,we used Android mobile phones from different brands totake into account device dependency of Wi-Fi fingerprints.

Fig. 5. A partial layout of the fourth floor of the EE building on the northcampus of XJTLU.

0 5 10 15 20 25 30Epoch

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Fig. 6. Training and validation accuracy of floor-level location estimation bythe DNN-based indoor localization system with real RSS data.

From around 200 APs detected, we collected more than 4000fingerprints at different locations with labels.

Fig. 6 shows the training and validation accuracy of floor-level location estimation by the DNN-based indoor localizationsystem with the collected RSS dataset. As shown in the figure,the DNN architecture used for building/floor classification in[6] can achieve more than 97% accuracies after thirteenthepoch in floor-level localization with both training and val-idation datasets. After training and validation, we applied thesystem to a training dataset of 253 fingerprints and obtainedthe accuracy of 0.97198, which is the average of five trials.Note that, even though the testing accuracy is pretty high, itmay be lower if we test with a much larger dataset.

V. SUMMARY

In this paper we have introduced the feasibility studyproject on the XJTLU Campus Information and Visitor Servicesystem, which will serve as a test bed for large-scale location-aware services in access, and reported preliminary results ofour investigation on the use of DNNs for building/floor classi-

TABLE IIIDNN PARAMETER VALUES FOR FLOOR-LEVEL LOCATION ESTIMATION

DNN Parameter ValueRatio of Training Data to Overall Data 0.75Batch Size 10SAE Hidden Layers 128-64-32-64-128SAE Activation Hyperbolic Tangent (TanH)SAE Optimizer ADAMSAE Loss MSEClassifier Hidden Layers 64-32-7Classifier Activation ReLUClassifier Optimizer AdaGrad [15]Classifier Loss Cross EntropyClassifier Dropout Rate 0.50Classifier Epochs 30

fication and floor-level location estimation. The preliminaryresults for both building/floor classification and floor-levellocation estimation clearly show the strengths of DNN-basedapproaches, including immunity against signal fluctuation,noise effects, and device dependency, no need of findingthe best match against every fingerprint in a database afterDNN training, and the elimination of time-consuming manualparameter tuning.

Still, further study is needed for hierarchical building/floorclassification and more scalable & higher resolution floor-levellocalization.

Note that the implemented DNN models, results of perfor-mance evaluation, and collected RSS datasets are availableonline at the project home page: http://kyeongsoo.github.io/research/projects/indoor localization/index.html.

ACKNOWLEDGMENT

This work was supported in part by Xi’an Jiaotong-Liverpool University (XJTLU) Research DevelopmentFund (under Grant RDF-14-01-25), Summer UndergraduateResearch Fellowships programme (under Grant SURF-201739), Research Institute for Smart and Green Cities Seed

Grant Programme 2016-2017 (under Grant RISGC-2017-4),and Centre for Smart Grid and Information Convergence.

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