In-house Localization for Wi-Fi Coverage Diagnostics

Filipe Meneses

1,2 a

, Ricardo Ferreira

, Adriano Moreira

and Carlos Manuel Martins

Centro de Computação Gráfica, Guimarães, Portugal

Algoritmi Research Centre, University of Minho, Portugal

We-Do, Braga, Portugal

Keywords: Indoor Location, Wi-Fi Fingerprinting, Wi-Fi Coverage, Self-diagnostics.

Abstract: Telecommunication operators and Internet Service Providers often face the problem of having residential

customers complaining about deficient Wi-Fi coverage inside their houses and/or about the low quality of

service while accessing the Internet. Addressing these complaints properly involves a comprehensive in-house

diagnostic of the technical deployment, the use of specialized equipment and visits by qualified personnel.

An alternative is to involve the users in a preliminary diagnostic, by leveraging the potential of current

smartphones, aiming to identify possible causes for the complaints that can be solved remotely or through

simple procedures to be executed by the customers. A key feature of such a diagnostic procedure is the ability

to estimate the location of the smartphone indoors automatically. This paper proposes a simple indoor

localization solution, based on Wi-Fi fingerprinting, that can be integrated into one such diagnostics procedure.

The proposed solution was implemented and tested in real-world houses by emulating the behaviour of non-

qualified users. The obtained results show that Wi-Fi fingerprinting, when used in such an uncontrolled

environment, still poses some challenges as its precision is still significantly low.

1 INTRODUCTION

In most countries around the world, the typical

configuration for residential Internet access is based

on an all-in-one-box router and Wi-Fi Access Point

(AP), connected to the Internet through a point-to-

point link: ADSL, cable or optical fibre (FTTH).

Inside the house, costumers use their own devices to

connect to the local Wi-Fi network. Being based on a

single Access Point, this solution often struggles to

provide a convenient radio coverage of the entire

house, namely for large houses or in dense residential

areas where radio interference can significantly

degrade the performance of Wi-Fi networks. These

coverage problems can vary over time due to changes

in the house layout (e.g. moving furniture from one

place to another) or due to the deployment of other

Wi-Fi networks in the neighbourhood, making them

difficult to detect during the first installation of the

service by the technicians of the Internet Service

Provider (ISP). These problems are the cause of many

https://orcid.org/0000-0003-0575-981X

https://orcid.org/0000-0002-8967-118X

complaints by the customers about the provided

service, as their quality of experience is highly

dependent on the quality of the in-house Wi-Fi

network. Similar problems are also observed for

cellular coverage indoors, but these are not usually

understood by costumers as a break of the service

contract rules.

In-house diagnostics of Wi-Fi coverage in

residential environments, executed by the customers

using their Wi-Fi enabled devices (e.g. smartphones,

tablets, etc.), has the potential of identifying the

causes for simple problems that can be solved

remotely by ISP technicians. This diagnostic testing

procedure benefits from automatic indoor

localization capabilities as it enables the comparison

of test results performed in different time epochs and

the identification of locations with recurring issues. If

properly crafted, a smartphone App can even help

customers in self-diagnostic procedures and in tuning

their setup (e.g. by moving the Wi-Fi Access Point

slightly, changing its orientation or even changing

some configuration parameters such as the radio

216

Meneses, F., Ferreira, R., Moreira, A. and Martins, C.

In-house Localization for Wi-Fi Coverage Diagnostics.

DOI: 10.5220/0009426102160224

In Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2020), pages 216-224

ISBN: 978-989-758-425-1

channel number, or by changing the position of some

furniture objects) to get a better service without any

intervention from the ISP technicians. One key

enabler of one such self-diagnostic tool is the ability

to estimate the location of the smartphone/tablet

inside the house.

Many indoor positioning and tracking

technologies have been proposed in the last decade,

targeting different scenarios and aiming to support

different applications (a good overview of the many

solutions can be found in (Mautz, 2012) or

(Davidson, 2016). Among those technologies, Wi-Fi

fingerprinting has become very popular, both within

the research community and also as the base for many

commercial products, mainly due to its simplicity and

easy of deployment. Given the current ubiquity of

Wi-Fi networks in indoor spaces and since the large

majority of mobile devices are Wi-Fi enabled, an

indoor positioning solution can be created based

entirely on software components, without the need to

deploy any infrastructure. Other similar solutions,

based on Bluetooth Low Energy (BLE), are also

becoming popular at airports, hospitals and shopping

malls, despite demanding the installation of a dense

network of BLE beacons (Faragher, 2014).

Sound and ultrasound propagation in indoor

environments have also been investigated for

supporting indoor positioning solutions (Harter,

2002; Priyantha, 2001), but they also require the

installation of a dedicated infrastructure. The systems

described in (Rishabh, 2012) and (Nakashima, 2011)

are examples of solutions that use the loudspeakers

available in offices or shopping malls to implement

indoor positioning systems. They disseminate barely

audible controlled sounds or watermarked signals,

which are then captured by the mobile users’ devices

and used to estimate the receiver position. Other

solutions, namely those based on infrared or

UltraWideBand (UWB) have the potential of

achieving high accuracy at the cost of requiring the

use and deployment of specific, and often expensive,

hardware (Alarifi, 2016).

This paper reports on the development of a

technical solution to assist residential customers of

Internet access services in performing self-diagnostic

tests in their houses. The developed solution includes

a software library with several methods to run

performance tests, including upload and download

rates, latency statistics, link speed, RSSI - Received

Signal Strength Indicator, and Wi-Fi radio

interference level (based on the number of access

points using the current and adjacent Wi-Fi channels).

The library also includes methods to estimate the

location (at room level) where each test has been

performed, thus enabling the geo-referencing of each

set of test results.

This paper is focused on the development and

testing of an indoor localization solution based on

Wi-Fi fingerprinting. Section II provides an overview

of the fundamental principles of Wi-Fi fingerprinting,

with its advantages and limitations. The proposed

solution for the in-house localization component is

introduced in section III. Its evaluation in real-world

settings is described in section IV, along with a

discussion of the obtained results. The paper ends

with conclusions and some ideas for future

developments, in section V.

2 WI-FI FINGERPRINTING

Wi-Fi fingerprinting is a scene analysis method of

positioning (Bahl, 2000). It is based on the

fundamental principle that the characteristics of the

radio environment are unique at each location, and

involves two main stages. In its first stage, samples of

the radio environment (strength of the received signal

from each observable AP, frequency channel, or other

characteristics of the radio signals) are collected at

known locations and subsequently stored in a

database to form what is known as a Radio Map.

Collecting samples can be achieved by querying the

network interface of Wi-Fi enabled devices through

the devices’ Application Programming Interface

(API), thus using simple software components. In

most of the reported solutions, multiple samples are

collected at each location, with one or more distinct

orientations of the collecting device (e.g. with the

device heading North, South, etc.). Collecting

multiple samples aims at capturing the variability of

the radio signals at each location. In some systems,

the set of collected fingerprints is pre-processed,

namely by averaging the measured signal level from

each AP or by filtering data from specific APs, to

produce the final radio map. This is considered, by

some authors, one additional stage added to the two

conventional ones.

In the operational stage, also known as the on-line

stage, a device at an unknown location collects one

sample of the radio environment (operational

fingerprint) and an estimation method is used,

together with the radio map, to estimate the position

of the device. Two main approaches are used to

estimate the position - deterministic and probabilistic,

with none of them being clearly superior to the other.

The probabilistic approach is based on a probabilistic

model that describes the probability of observing a

given signal level from a particular AP at a given

In-house Localization for Wi-Fi Coverage Diagnostics

217

position, and is usually built by approximating the

distribution of the measured signal level to a Gaussian

distribution. The unknown position of the device is

then estimated through a Bayesian method (see, e.g.

(Youssef, 2005) and (Ledlie, 2012)). With the

deterministic approach, a distance function is used to

compute the similarity between the operational

fingerprint and all the fingerprints in the radio map.

The most similar fingerprint or the k most similar

fingerprints in the radio map are then selected and

their corresponding positions are used to estimate the

position of the target device (usually the centroid or

weighted centroid). This is known as the k-Nearest

Neighbour (kNN) method. Many alternative distance

functions have been proposed to compute the

similarity between fingerprints, with the Euclidean

and Manhattan distances being the most popular

(Torres-Sospedra, 2015). When estimating the

location at room level, majority rules can be used to

pinpoint the most probable compartment indoors

(Marques, 2012). Other classification methods have

also been proposed, including decision trees, random

forest, Support Vector Machine and Neural

Networks.

Due to the variability of the radio environment,

the typical performance of Wi-Fi fingerprinting-

based indoor positioning solutions is characterized by

an accuracy (mean error) around 5 meters, with the

frequent observation of very large errors (larger than

15 meters). One good reference regarding the

performance of these positioning methods is the set

of results obtained in indoor positioning

competitions, such as the IPIN (Torres-Sospedra,

2016) and Microsoft competitions (Lymberopoulos,

2017), although paying attention to the fact that most

of the competing systems are not pure Wi-Fi

fingerprinting-based but, instead, hybrid system

fusing data from multiple sensors.

Indoor positioning based on Wi-Fi fingerprinting

is, however, very challenging. Firstly, creating radio

maps for large buildings is a very tedious and time-

consuming task, even when resorting to advanced

localization and mapping (SLAM) approaches

(Ferris, 2007; Wu, 2012; Jiang, 2012). Moreover,

radio maps degrade with time due to changes in the

radio environment, requiring frequent recalibrations.

Some of the causes of these changes in the radio

environment are modifications in the layout of the

space (e.g. furniture moving, doors opening/closing),

alterations in the layout of the Wi-Fi network (APs

being relocated, added or removed, nearby networks

being deployed/modified) and the presence of mobile

hotspots (temporary APs created by mobile devices).

Wi-Fi-fingerprinting also suffers from the use, in the

operational phase, of devices different than those

used for creating the radio map, including different

versions of the Android OS API version, from the

orientation of the devices that affect the measured

received radio signals due to body shadowing, and

also from how the devices are handled (carried in the

pocket, in the hand, etc.) while collecting a

fingerprint.

3 ROOM-LEVEL

LOCALIZATION

The aim of the positioning system described in this

paper is to detect when network performance tests

have been run in the same room inside the house

where similar tests were run earlier. Therefore, it is a

problem of recognizing (recalling) a previously

visited room. In its simplest form, the user of a

smartphone App visits each and every room inside

his/her house and collects enough fingerprints to

properly characterize them, also labelling these

fingerprints with the room name. This approach for

building the radio map can be easily and rapidly

performed in a regular house with the help of a

smartphone App based on a wizard. In a more

automated form, the fingerprints are collected

automatically, in the background, by the smartphone

App while also running the other performance tests.

In any case, the collected fingerprints are then used to

characterize and later recognize each room.

The proposed approach for this system is based on

pure Wi-Fi fingerprinting, running completely in the

smartphone, without depending on any network

service. The reason for this design choice is to ensure

total privacy of the users. On the other hand, this

choice imposes some limitations on the choice of the

estimation method (e.g. deep learning might not be

practical to implement).

Following a tradition approach, based on a

deterministic estimation method, let R be the set of all

labelled fingerprints (fp), collected at each room,

during an initial calibration, that form the radio map.

Each fingerprint is described as:

𝑓𝑝







𝑙,





𝑀



, 𝑅𝑆𝑆𝐼





,…,



𝑀



, 𝑅𝑆𝑆𝐼









(1)

where l is a unique label identifying each room inside

the house, M is the MAC address of an observed AP,

RSSI is the Received Signal Strength Indicator

representing the measured signal level (represented in

dBm), and N is the number of APs observed at a

particular location in a particular time instant.

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

218

In the operational stage, a deterministic method is

used to estimate the room associated with a given

fingerprint (fp

) collected at an unknown location.

First, the similarity S between fp

and all the

fingerprints fp

in R is computed using a modified

version of the Manhattan distance, defined as:

𝑆



𝑓𝑝



,𝑓𝑝







∑

𝑅𝑆𝑆𝐼





 𝑅𝑆𝑆𝐼











𝛼𝑛𝐶𝑀

(2)

where RSSI

is the measured RSSI of AP

in the

operation fingerprint (fp

), RSSI

is the measured

RSSI of AP

in the radio map fingerprint (fp

), N is the

total number of APs observed in the fp

and fp

, nCM

is the number of APs than are observed in both fp

and

, and

⍺

is a parameter that gives more or less weight

to the number of common APs (nCM). Since not all

APs are observed in all fingerprints, whenever an AP

is missing (not observed), the corresponding RSSI

value is replaced by a constant representing a weak

signal (we found -90 dBm to provide good results).

Let B be the set of radio map fingerprints ordered

by decreasing similarity (meaning increasing value of

S) with fp

. The location (room) associated to fp

estimated by applying a majority rule to the top k

fingerprints taken from B: the most frequent room is

the most probable location for fp

(k-Nearest

Neighbours – k-NN). This method assumes that R

includes more than one labelled fingerprint per room.

Otherwise, k must take the value 1, and the estimated

location is that of the most similar fingerprint (1-NN),

i.e. that of the top fingerprint in B.

The location estimation method described above

was implemented as a Java library including classes

and methods to collect fingerprints, manually label

fingerprints (to get ground truth), to build the radio

map, and to estimate the location associated to a given

fingerprint. Other methods, used to assess the

performance of Wi-Fi networks have also been

implemented, including a feature to upload the results

to a server.

4 EVALUATION IN THE REAL

WORLD

The developed system was evaluated in three

different houses, of varying configuration, in order to

assess the performance in recognizing a previously

mapped room. Three distinct persons performed the

evaluation using three different smartphones. As

much as possible, the evaluation was performed

trying to mimic the behaviour of non-technical users.

This process was performed without altering the

normal behaviour of the space inhabitants or

changing the physical layouts, such as the position of

furniture and other large objects. These experiments

were conducted in the first days of February 2019.

4.1 Experimental Setup

The infrastructure created to evaluate the developed

solution (the Java library, with emphasis on the

localization solution) includes an Android App and a

data server, and three houses where the system has

been tested.

The App implements a simple graphical user

interface that facilitates the access to the main library

functions (see Figure 1): Add Place – to collect a set

of fingerprints, label the corresponding location and

add them to the radio map; Get Location – to collect

one single fingerprint, estimate the corresponding

location, collect ground truth (Figure 1.b), and send

the result to a server. All the other functions are used

to manage the radio map: reset it (Delete Places),

rename a place, delete a place, and list all places.

(a) (b)

Figure 1: Android App used for the validation of the

localization system: (a) main menu; (b) validating a

recognized place.

The server is used only to collect the experimental

results: every time a localization estimation is

performed, the corresponding operational fingerprint,

the used radio map and the ground truth are sent to

the server, where these data is stored in a database for

further processing. This allows the same data to be

processed offline using variants of the estimation

algorithm.

In-house Localization for Wi-Fi Coverage Diagnostics

219

The system has been tested in three different

houses:

 House A (hA) – a three-floor house with 12

compartments, 1 in the basement, 5 “spaces” in

the ground floor and 6 compartments in the

upper floor; all spaces in the ground floor are

in an open space without any walls or doors

separating them, except for the toilet and

laundry; most interior walls are made of brick;

the neighbourhood includes several similar

houses around, with small gardens in between;

no testing has been performed in the basement;

 House B (hB) – a 120 square meters single-

floor house with 8 compartments; there is only

one neighbour house that share a wall from

kitchen and a room; all spaces are separated by

walls made of brick and accessible by the hall;

 House C (hC) – a flat, with almost 130 square

meters, on the 3

floor of a multi-floor

building, with 9 compartments; all spaces are

separated by walls made of brick, except the

laundry room which is separated from the

kitchen by a glass; the neighbourhood includes

three other flats on the same floor (with a

double wall made of brick in between) and

several other flats on the same floor but without

direct contact; the same layout exists on the

remaining four floors of the building.

Four different smartphones were used to collect

the data: Nexus 5, Oneplus 5T, Xiaomi Mi8 Pro, and

Lenovo Pb2. In all tests, the same device was used to

build the radio map and to perform the localization

tests. Experiments using one smartphone for building

the radio map and a different one to perform the

localization tests were not considered since those

scenarios are not expected to happen in the real use of

this system.

4.2 Evaluation Metrics

The main metric for evaluating the performance of

symbolic location systems is Accuracy – it measures

the percentage of times the system correctly

recognizes the visited place. Since the performance of

the location recognition is dependent on the total

number of distinct places to recognize (the smaller the

number of distinct places, the easier is to recognize

the correct place), a secondary metric is defined as the

gain over a random guessing. This metric, named

Relative Accuracy, is defined as Ar = Np x Accuracy,

where Np is the total number of rooms inside the

house (number of classes in the classifier). Ar is

simply the gain over a random classifier, which

accuracy is 1/Np. In our evaluation, the number of

samples (fingerprints) collected in each room to build

the radio map is the same, so that there is no initial

unbalance (bias) and the prior probabilities of each

class are all equal.

4.3 Evaluation Procedure

An evaluation procedure was defined prior to any

data collection at the houses referred above.

It is also well known that the movement of

devices affects the propagation of radio signals

significantly. However, despite the large number of

location/positioning systems based on radio signals

that have been proposed, their evaluation is usually

performed with stationary devices. The exception is

the class of positioning tracking systems based on

pedestrian dead reckoning since the movement of the

human body is fundamental to estimate displacement

(e.g. through step counting and stride length

estimation). Therefore, this work also aims at

investigating the impact of movement in the

performance of Wi-Fi based fingerprinting location

systems.

Three different modes were tested:

 still-table: stationary smartphone over a table

or similar object;

 still-hand: stationary smartphone being hold in

the hand of the user;

 moving-hand: smartphone handled by the user

while walking inside the house at normal speed

(less than 1m/s).

Two sets of experiments were conducted: one

with the radio map built with the smartphone placed

on top of a table (still-table), and another with the

smartphone in the hand (still-hand). This is intended

to evaluate the impact of having the smartphone in the

hand while collecting the fingerprints. For the first

radio map (still-table), two localization tests were

performed: still-table and moving-hand. For the

second radio map (still-hand), the localization tests

were performed for the modes still-hand and moving-

hand.

Data collection involved two stages. First, a user

registered all the places inside the house to create the

radio map. In the second stage, the user visited all the

spaces/rooms, many times, and asked the system to

recognize his location while being stationary (still-

table), while holding the smartphone in the hand

(still-hand), and while walking without stopping

(moving-hand).

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220

To create the radio map, the user visited each

place/room, selected a location near the centre of the

room, placed the smartphone on top of a table or other

surface at similar height (still-table), with the screen

pointing to the ceiling, pushed the ADD PLACE

button (see Figure 1), and typed in the room name.

Ten fingerprints were collected per room. No specific

order was defined to visit the several rooms. Each

room was visited only once. In the second set of

experiments, this procedure was repeated while

holding the smartphone (still-hand). Table 1 shows a

summary of the collected data, including the number

of fingerprints collected in each house for each testing

mode (#fps) and the total number of observed Access

Points (#APs).

Table 1: Summary of the collected data (radio maps).

House Mode #rooms #fps #APs

hA still-hand 11 110 7

hA still-table 11 110 8

hB still-hand 8 80 11

hB still-table 8 80 7

hC still-hand 9 90 23

hC still-table 9 90 22

hC still-hand 9 90 29

hC still-table 9 90 21

Total:

740

The second stage was devoted to evaluate the

performance of the system in recognizing previously

mapped rooms. To collect the corresponding data, the

user repeated the same procedure as for creating the

radio map but, instead of pushing the ADD PLACE

button, the user used the GET LOCATION function

of the App. After receiving a reply from the App, the

user confirmed the estimated location, if correct, or

selected the correct place from the list of registered

places, otherwise. An “unknown” location could also

be selected in case the user was at a place not

previously registered into the system. In this stage,

each room was visited three times. This procedure

was repeated for the modes still-table, still-hand and

moving hand.

Since all these data was sent to a server during the

evaluation, the performance of the system was

computed offline.

5 EVALUATION RESULTS AND

DISCUSSION

The results presented in this section were obtained by

using the following values for the system parameters:

 default RSSI value for missing APs: -90 dBm



⍺

(see equation (2)): 4

 k (number of top fingerprints to use when

applying the majority rule): 5

A summary of the evaluation results is shown in

Table 2. These results are, apparently, disappointing,

since the accuracy is quite low. It means that, in too

many cases, the localization system is not able to

estimate the correct room, even though the

performance of the system is way better than a

random classifier.

Table 2: Overall Accuracy (A) and Relative Accuracy (Ar).

Radio map

(still-table)

Radio map

(still-hand)

A Ar A Ar

0.563 6.19 0.508 5.59

0.612 4.89 0.430 3.44

0.598 5.38 0.719 6.47

Average

0.591 5,49 0.552 5.17

These results also show that the difference in

performance across the three houses is not very large

when the radio map is built with the smartphone

standing on top of a table (still-table), but larger

variations are observed when the smartphone is in the

hand while building the radio map (still-hand). This

was an expected result, as it is known that the human

body attenuates the radio (Wi-Fi) signals

significantly. On the other hand, the average results,

considering the three houses, are similar for both

radio maps.

A deeper analysis of the results revealed that,

many times, the incorrect location estimates are on

rooms adjacent to the correct one, or that the second

or third guesses include the correct room. This is

illustrated in Table 3, where it is shown that in around

80% of the estimates, the correct room is within the

three best guesses. One possible explanation for these

results is that, in the houses used for testing, some

rooms are not clearly separated by walls and/or doors

– they are just different areas in a large open space.

Therefore, it is not easy to distinguish the different

areas since there are no obstacles to differentiate the

propagation of the radio signals clearly. The results in

Table 3 also show that, when considering the

aggregated data, there is no significant difference

between the two radio maps.

In-house Localization for Wi-Fi Coverage Diagnostics

221

Table 3: First, second, and third best guesses.

Guess

Radio map

(still-table)

Radio map

(still-hand)

A Ar A Ar

0.592 5.46 0.594 5.49

1+2

0.697 6.42 0.700 6.46

1+2+3

0.802 7.38 0.797 7.35

Table 4 shows results about the impact of

movement, aggregated for the three houses. It

compares the results (first guess only) for the two

radio maps and the performed testing modes.

Table 4: Impact of movement.

Mode

Radio map

(still-table)

Radio map

(still-hand)

A Ar A Ar

still-table

0.623 5.77 - -

still-hand

- - 0.650 6.00

moving-

hand

0.562 5.15 0.538 4.98

Here it is more evident the impact of performing

tests (localization) while moving: for both radio maps

there is a clear degradation on the accuracy when the

fingerprints are collected while the user was walking.

In one of the houses, the tests were performed

using two different smartphones, aiming to evaluate

if the use of different devices has a significative

impact on the accuracy. For each test, both the radio

map fingerprints and the online fingerprints were

collected using the same device. The corresponding

results are shown in Table 5, and are inconclusive.

While for the first radio map there is a clear advantage

on the performance of the Xiaomi smartphone, for the

second radio map the results are, coincidentally,

exactly equal.

For the house with multiple floors, the correct

floor rate was calculated and found to be 93,1%.

Table 5: Impact of using different smartphones.

Mode

Radio map

(still-table)

Radio map

(still-hand)

A Ar A Ar

Xiaomi

0.718 6.46 0.719 6.47

Lenovo

0.478 4.30 0.719 6.47

Given the obtained results, which suggest that

there is room for considerable improvements, other

localization methods, were evaluated. Among them,

well-known classifiers such as Naïve Bayes, Decision

Trees, Random Forest (ensembles), Neural Networks

and Support Vector Machine were evaluated using

the radio maps for training and the testing fingerprints

for testing, using the vector of all RSSI values

observed from all APs as the single feature. The

obtained results (accuracy) were consistently worse

than those obtained with the method described in this

paper.

6 CONCLUSIONS AND FUTURE

WORK

In this paper we reported on the evaluation of an

indoor localization system, at room level, based on

Wi-Fi fingerprinting. The particular characteristic of

the developed system is that it is to be used in

residential houses by non-qualified people using a

simple smartphone App. Therefore, no initial setup

and/or calibration should be made by professionals.

In order to preserve the privacy of the users, the

system should also implement all the required

functions in the smartphone App, and no external

server should be used. This requirement prevents the

use of advanced methods that imply a high

computational burden, incompatible with the

processing capabilities of average smartphones/tables

or required high energy consumption.

The proposed system is based on Wi-Fi

fingerprinting and a simple deterministic estimation

method (similarity and majority rule). Its

performance has been evaluated in three real houses.

The results reported in this paper show that

recognizing previously mapped rooms inside the

house is a difficult task, and the obtained accuracy

was in the range of 55 to 80%. These results are worse

than initially specified. One reason for this level of

performance might be the small number of Access

Points observed in a house, compared with what is

now typical in more network-dense places such as

universities, hospitals or airports. On the other hand,

these results are in line with the typical positioning

results reported in the literature for Wi-Fi

fingerprinting, where the accuracy is around 5 meters.

With this level of accuracy, it is quite easy to estimate

the wrong room inside a house with typical room

sizes (~10-15 m

A direct comparison with the results of other

authors is a difficult task, as reported in (Torres-

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Sospedra, 2017), since the evaluation conditions are

often very different and the reported methods are

difficult, if not impossible, to replicate. Moreover, the

room-level accuracy depends deeply on the layout of

the space and materials used. Results reported in

(Yasmine, 2016) show an accuracy of 0.88. However,

these results were obtained in a test performed in a

shopping mall, with shops spreading a much larger

area than is typical in a house. This larger spread

facilitates the distinction among rooms (shops in this

case) that are far apart, which is not the case in a 100

house.

As future work, and in order to improve the

accuracy, a few hybrid solutions will be evaluated,

including the combination of Wi-Fi fingerprinting

with fingerprinting based on cellular networks radio

signals (Otsason, 2005) or with sound-based

fingerprinting. In these hybrid methods, the use by

non-professionals should be evaluated and its impact

measured.

One other area deserving further investigation, for

this particular application, is the use of multiple

fingerprints collected at each room during the

localization (online) phase. Multiple fingerprints can

be combined to reduce the inherent variability of the

RSSI values. This technique can be easily

incorporated in the developed App at the expense of

longer data collection periods at each location.

ACKNOWLEDGMENT

Authors acknowledge the project Next-Gen

RAID.Cloud for the Digital Transformation,

Individual Demonstration Projects, NUP: POCI-01-

0247-FEDER-033539, a project co-funded by the

Incentive System for Research and Technological

Development, from the Thematic Operational

Program Competitiveness of the national framework

program - Portugal2020. This work has also been

supported by FCT – Fundação para a Ciência e

Tecnologia within the R&D Units Project Scope:

UID/CEC/00319/2020.

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