Location Aware Information System for Non-intrusive Control of

Remote Workforce with the Support of Business IT Consumerization

Sergio Ríos-Aguilar

and Francisco Javier Lloréns-Montes

Engineering and Technology School, Universidad Internacional de La Rioja (UNIR), Logroño, La Rioja, Spain

Management Department, University of Granada, Granada, Spain

Keywords: Location Awareness, Workforce Control, LBS, BYOD, Mobile Check-in.

Abstract: This work proposes a Mobile Information System that can serve HR departments in companies to conduct

remote workforce location-based control, by means of a non-intrusive use of employees’ own smartphones,

taking benefit from the IT Consumerization phenomenon. This proposal provides quantitative and

qualitative references that should be met with respect to the location information accuracy needed in

common control scenarios for the remote workforce. A fully working prototype of the proposed Mobile

Information System was developed to evaluate the validity of the strict accuracy and precision requirements

proposed for location data, using a standard check-in process at remote workplaces under real world

conditions. The results obtained in this study confirm that at present it is viable for companies to implement

an Information System for the control of remote workforce that allows the companies to gain

competitiveness, adopting a BYOD paradigm which allows their employees to use their own smartphone

mobile devices in the workplace.

1 INTRODUCTION AND

DEFINITION OF THE

PROBLEM

Today, the increasing competitiveness of the actual

market forces companies to try to have a deeper

understanding of the relationship of cause and effect

of their actions on profitability, being necessary to

have specific information that guides the process of

improvement of their competitive performance

(Bradley, 1998; Lahoz and Camarotto, 2012).

Early detection, evaluation and rapid intervention

are crucial when managing tardiness and absences in

the workplace, and help prevent them from

becoming a serious problem for the competitiveness

of companies. Usually, to achieve such detection,

Information Technologies investment is required,

among other things for the acquisition and

implementation of Control, Access and Presence

Systems, which are often expensive, not only

because of the initial costs (equipment for physical

identification using card reader technology or

biometric identification), but also the maintenance of

the equipment and software that form the system’s

back-end, not to mention the possible costs for the

integration with pre-existent Information Systems

(Sen et al., 2009; Kauffman et al., 2011).

On the other hand, this kind of systems is proven

to be ineffective when it comes to extend the control

to mobile workforce, which in numerous private

service sector enterprises it may represent a high

percentage of the staff due to the very nature of its

business. In this case what is needed is a “proof of

presence” in places and times established in advance

(Kumar and Pandya, 2012)

1.1 Opportunities

In the current economic context, it is of vital

importance for a business to improve their

competitiveness by rationalizing the necessary

investment to achieve it. Two unique situations have

been detected that can allow small and medium

enterprises (SMEs) to have a presence control

Information System for both local and remote

workforce with a very reduced cost and with

minimum need of infrastructure:

(A) The current maturity of mobile location

technologies, using different transparent

442

Ríos-Aguilar, S. and LLoréns-Montes, F.

Location Aware Information System for Non-intrusive Control of Remote Workforce with the Support of Business IT Consumerization.

DOI: 10.5220/0006336704420448

In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) - Volume 2, pages 442-448

ISBN: 978-989-758-248-6

positioning mechanisms (A-GPS, GPS, WiFi

and Cell-ID).

(B) The growing trend BYOD ("Bring Your Own

Device") allowing employees the use of their

own mobile communication devices

(smartphones, tablets…etc) in the business as a

complementary tool that takes a double role as

a personal device for private use and as an

access to the company’s Information Systems.

On this basis, this paper tries to ascertain if it is

feasible the implementation of an Information

System for the behaviour-based control of workforce

that makes a non-intrusive use of technology for

getting the employees locations from their own

smartphones, regardless of the make and model they

have.

Of course, our focus is on non-intrusive location

information gathering, allowing the employee to

retain full control on when is his location

information sent to the employer’s enterprise

servers. So, ethical considerations such as privacy,

accuracy, security and reputation are not affected

due to this non-intrusive operation.(G. Kaupins and

R.Minch. 2005)

This paper is structured as follows: first, it

analyzes the viability of the use of employees’

smartphones following the BYOD paradigm as a

valid tool for companies in order to conduct

presence control (primarily for remote workforce).

Second,, a Mobile Information System is proposed

for Presence Control using exclusively terminal-

based reactive location technologies, meeting cost

minimization and universal access criteria. Third,

this paper proposes qualitative and quantitative

references, adequate to the location information

accuracy demanded in different business remote

workforce control scenarios. And Fourth, this paper

discuss the results of testing the accuracy and

precision of location data using real devices (iOS

and Android) under heterogeneous connectivity

conditions and workplace premises

1.2 Granularity in the Control of

Workforce

In order to quantify the level of demand of accuracy

suitable for the location of the workforce, reviewing

existing international regulations on the subject, it

was found that the United States Federal

Communications Commission (FCC) E-911

mandate, concerning the precision of the location of

calls from mobile devices to Emergency Service

E911, is the most specific and strict set of

regulations (Table 1). And it also provides a

methodological framework for verification processes

regarding compliance. At present, E-911 is the only

regulation set that clearly quantifies the required

location accuracy. So, for the proposed Information

System, the most restrictive location accuracy values

of the aforementioned regulations will be considered

as a valid working quantitative reference.

2 INFORMATION SYSTEM AND

PROTOTYPE

Taking into consideration all conditioning elements,

in this work we propose an Information System

specifically oriented towards business use for the

behaviour-based control of remote workforce, using

smartphones under the BYOD paradigm, and

putting the focus on universal access (device

independence).

Additionally, a fully operational prototype of

this Information System has been developed; It

consists in a mobile web application along with a

control panel serving as a balanced scorecard and

SaaS (Software as a Service, in the cloud).

This prototype fulfills a double purpose of (i)

serve as a proof of concept for an Information

System for the control based on behaviour of the

remote workforce using a mobile web application,

and (ii) to allow an empirical analysis on the current

feasibility of its use under real conditions, using the

most strict obtained geographical location accuracy

requirements from mobile devices to emergency

services established by the FCC to be complied in

2019. (FCC, 2011)

Table 1: Mobile devices location accuracy requirements

from which emergency service E-911 is requested.

Adapted from FCC (2010).

Location

Type

Required

Accuracy

Regulation

date of entry

into force

Mobile

terminal

based

50 meters for 67%

of the calls and 150

meters for 80% of

the calls

Already in force

(since

18/1/2013)

50 meters for 67%

of the calls and 150

meters for 90% of

the calls

Scheduled to

enter into force

on 1 January

2019

2.1 Description and Components of the

Information System

The proposed Information System for the behaviour-

based control of remote workforce consists of two

discrete functional components:

Location Aware Information System for Non-intrusive Control of Remote Workforce with the Support of Business IT Consumerization

443

First, a Mobile Web application that will be

accessed by the workforce using their smartphone

mobile devices in order to clock in when arriving at

the customer’s facilities or other installations

deemed appropriate. This application also allows to

send remarks concerning the clocking in that will be

registered in the system in that moment.

Technologies such as HTML5 and JQuery Mobile

have been chosen because they are the most cost-

effective for companies, and also involve less costs

in development as it require just one code base (only

one production line) and it can be deployed in

almost all mobile devices that have a browser and

access to the Internet (Heitkötter et al. 2013; Zhu et

al. 2013)

Figure 1: Information System Architecture for behaviour-

based control of the workforce.

Secondly, a Control Panel Web Application for

the control of the remote workforce, accessible from

any Web browser, it allows real-time queries

regarding the workforce clocking in processes,

including georeferenced information in maps, as

well as make individual historical analysis of such

processes for each employee. This individual

historical analysis is in line with behaviour analysis

in the context of ubiquitous monitoring (Moran and

Nakata, 2009)

All the components used in the Server and

Database layers in this Information System are open

source software (LAMP platform: Linux OS,

Apache Web Server, PHP, and MySQL database), in

order to minimize costs involved in the deployment

of the proposed Information System.

3 LOCATION DATA ANALYSIS

METHODOLOGY

An empirical analysis is to be made to determine the

location accuracy obtained with mobile devices

using the developed mobile web application. Even

though the accuracy of conventional GPS receivers

is well documented for various devices (such as

PNDs), the study on the accuracy of devices with

Assisted GPS (A-GPS), usually available in

smartphones like those intended to be used in the

proposed Information System, has not yet evolved as

much. Due to hardware limitations, it is to be

expected a worse performance concerning location

precision and accuracy using smartphones than

using a conventional GPS device (Zandbergen and

Barbeau 2011).

To study the feasibility of the use of location

data obtained from smartphones by means of a web

app in the proposed Information System, sets of GPS

location data under real conditions have been

collected. Location data obtained is compared with a

real known location (“truth point”), and with this an

absolute accuracy measurement will be obtained.

3.1 Instrumentation and Data

Collection

The GPS location data quality tests were done using

two of the most widespread smartphone mobile

terminals actually in the European market: Samsung

Galaxy S6 and Apple iPhone 6S. These devices are

also highly representative since they belong to two

of the most prevalent smartphone groups

(corresponding to the Galaxy and iPhone

trademarks) with the biggest market growth, and

evolving with constant improvements in their

hardware specifications.

Samsung Galaxy S6 uses a Broadcom BCM4773

GNSS Location Hub as GPS receiver chip with

active suppression of sources of interference. The

device used in the tests has Android v6

(Marshmallow) Operating System. Apple iPhone 6S

uses a Qualcomm WTR3925GPS receiver chip and

the operating system used was iOS 9. In both cases

the tests were made with the GPS circuits activated

and using network assisted mode (A-GPS),

additionally WiFi based location mechanisms were

active to help increase the accuracy.

The tests were carried out strictly during

standard business hours, at random times between

8h and 17h, representative of workforce daily

activities times in working days. The geographical

coordinates of this point (446737m, 4114616m

UTM zone 30S, European Datum 50) were obtained

using a topographic grade sub-meter accuracy

professional GPS receiver Topcon Hiper+ with a

10mm + 1.0ppm horizontal accuracy, properly

calibrated and using Differential Global Positioning

System (DGPS)

The mobile devices registered location data in

WGS-84 format, in intervals of 3 minutes,

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

444

RMSE





generating data sets of 25 samples each. Data was

gathered, in different days, 10 sets of samples,

totaling 250 measurements for each study group.

3.2 Data Analysis

To assess the accuracy of both devices used in the

gathering of the data, the variability in the data will

be contrasted against a previously determined real

value, corresponding to the observation point (truth

point).

To determine the possible normal distributions of

the data sets (variable: accuracy- planimetric or

horizontal error) several tests were conducted:

Kolmogorov-Smirnov, de Ryan-Joiner (similar to

Shapiro-Wilk) and Anderson-Darling (See Table 2).

Table 2: Results of the normality tests of samples of

accuracy from both data origins.

Data origin device

Samsung Galaxy S6

(Android)

Normality test

Statistical

p-value

Kolmogorov-

Smirnov

0,059

<0,010

Ryan-Joiner

0,859

<0,010

Anderson-Darling

3,984

<0,005

Data origin device

Apple iPhone 6S (iOS)

Normality test

Statistical

p-value

Kolmogorov-

Smirnov

0,178

<0,010

Ryan-Joiner

0,902

<0,010

Anderson-Darling

7,155

<0,005

Once again it is proved that the result is similar

for the three conducted tests, which provide p-values

of less than 0,05 which confirms with a 95% of

reliability that for both data origins, Galaxy S6 and

iPhone, the hypothesis of normality for horizontal or

planimetric accuracy is rejected.

Having verified that horizontal error distributions

(for accuracy) are not normal, the non-parametric

Mann-Whitney U test was used in order to contrast

if both data origins have the same distribution, the

obtained result is a statistical value W=59745.6, this

test is also statistically significant in 0,0000 (p<0,05)

therefore the hypothesis of equal distributions of

horizontal error (planimetric accuracy) for both

populations is also rejected with a 95% reliability.

In the data analysis it has been shown that the

average values of the precision variable and the

accuracy variable differ significantly for both mobile

devices.

Also it has been shown that deviations are

significantly different for both data origins.

Distributions are different not only in precision but

also in accuracy for both populations (Android and

iOS data). Both mobile devices provide data of

enough quality, and the mean of their values is close

to the real value, but a greater data dispersion in

found for iOS devices

4 RESULTS

The horizontal error (in meters) of all the gathered

samples confirm, as evidenced earlier, the variability

of location data collected by the iPhone device

around the well known spot (truth point) is greater

than the data set collected with the Android device.

For this reason, the collected data will be

analyzed quantitatively, performing the calculation

of the horizontal accuracy taking the root mean

square of the errors (RMSE) of each data origin, first

for each component and afterwards calculating the

planimetric or horizonal.value

(1)

RMSERMSERMSE 

(2)

In the case of the Galaxy device, the planimetric

RMSE error value obtained is 13,12m for the

horizontal component (XY). This value means that

for a 67% of the observations made with the

Android device, the accuracy will be less at 13,12m.

In this case, the value for the Samsung Galaxy S6

device is 2DRMS = 26.24m

It might be concluded that at a 95% confidence

level the accuracy obtained with the mobile web

application using the GPS of the Samsung Galaxy

S6 device is within a 26.24 meter radius from the

real location value in an urban environment with

demanding conditions for GPS signal propagation

and shifting HDOP values, non planned in the tests

conducted.

Also, in the case of the iPhone device, the

planimetric RMSE value obtained is 27,11m for the

horizontal component (XY). This value means that

for a 67% of the observations made with the iOS

device, the accuracy will be less at 27,11m. In this

case, the value for the iPhone 6S device using the

mobile web application is 2DRMS = 54.22m

It might be concluded, that with the samples

taken in this test, that at a 95% confidence level the

accuracy obtained with the mobile web application

using the GPS of the iPhone 6S device is within a

54.22 meter radius from the real location value in an

urban environment with demanding conditions for

GPS signal propagation, and shifting HDOP values,

non planned in the tests conducted.

RMSE





Location Aware Information System for Non-intrusive Control of Remote Workforce with the Support of Business IT Consumerization

445

5 VERIFICATION OF

COMPLIANCE WITH FCC

RULES

The FCC proposes a method for determining if a set

of location errors demonstrates compliance with

accuracy requirements of location data, using order

statistics. The FCC’s confidence intervals for

accuracy standards can be selected with a 90%

confidence based on the number of samples. (FCC

2000).Generally, when

 The number of measurements is n,

 The r-th and s-th largest measurements are x

and

, respectively

 x and y are the percentile points associated with

probabilities p

y p

, respectively, then

the probability that x is less than x

, while

simultaneously y is less than y

, is given by the

formula





  



  

































































(3)

being in this particular case p

=0.67 y p

=0.95

From this expression, upper bounds on the

percentile points can be determined, searching pairs

of values (r, s) for which the level of confidence

desired of 90% is achieved (Table 3).

As stated in Table 1, the most strict accuracy

requirements established by FCC to be met in 2019

are 50 meters for 67% of the samples and 150

meters for 90% of terminal based samples. If not, the

data set is rejected for not meeting the proper

standards. In this case, a data set of location errors

obtained using the mobile web application

(developed as part of the proposed Information

System) under real conditions, will be tested using

the methodology proposed by the FCC for the

assessment of compliance of accuracy requirements.

(FCC 2000)

To that end, data tables are sorted in ascending

order of accuracy for each observed location error,

and for each 100 samples obtained, according to

Table 3 it must satisfy the following expression

[(accuracy

< 50m) and (accuracy

100

< 150m)] or

[ (accuracy

< 50m) and (accuracy

< 150m)]

First, for the Android Device, the verification

process will be performed with those samples

obtained using the mobile web application in an

Android device. The results show that:

Accuracy

74-android

=17.3 m < 50m and

Accuracy

100-android

=38.3 m < 150m

Accuracy

75-android

=17.7 m < 50m and

Accuracy

99-android

=35.4 m < 150m

Therefore, it can be stated that following FCC’s

proposed methodology, with samples obtained with

the Android device, using the mobile web

application of the proposed Information System

under real world conditions, with a 90% confidence

level, the compliance of the strictest requisites set by

this organization is verified.

Equally, a verification process will be performed

with the samples obtained using the mobile web

application in an iOS device. The results show that:

Accuracy

74-iOS

=32.6 m < 50m and

Accuracy

100-iOS

=57.3 m < 150m

Accuracy

75-iOS

=32.8 m < 50m and

accuracy

99-iOS

=56.2 m < 150m

Table 3. Horizontal error value sample identification for

its comparison with thresholds of 67% and 95% as

required by the FCC for a 90% confidence level.

Sample size

Test sample pairs

, y

)

, y

)

, y

)

, y

)

, y

) o (x

, y

)

, y

) o (x

, y

)

, y

) o (x

, y

)

, y

) o (x

, y

)

100

, y

100

) o (x

, y

)

Source: Adapted from (FCC 2000)

Therefore, it can be stated that following FCC’s

proposed methodology, with samples obtained with

the iOS device, using the mobile web application of

the proposed Information System under real world

conditions, with a 90% confidence level, the

compliance of the strictest requisites set by this

organization is verified.

6 RESULTS DISCUSSION

The most important conclusion of these results is

that an Information System for the behaviour-based

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

446

control of workforce can always identify the

employee’s location with an accuracy level that

comfortably fulfills actual and future people location

requirements of the most strict Emergency Systems,

which nowadays provide the only valid quantitative

reference to validate the quality of people

geographical location data in this context.

This also entails the protection of the investment

made by the company in the implementation of the

proposed mobile based GPS Information System.

Due to the evolving nature of technological

innovation, mobile GPS receivers sensitivity will

increase still further, thus, obtaining more precise

and accurate results than those here obtained

(Schiller and Voisard, 2004) Also, both iOS and

Android devices used in the verification process

performed quite well in terms of power consumption

throughout the working hours without battery

recharges, and this is specially important taking into

consideration the typical battery drain usually

associated to the GPS receiver circuit. Specifically,

the mean battery drain using the proposed system

with both smartphones, has been estimated to be

4.1% of full load, using Powerbooter, an automated

power model construction technique that uses built-

in battery voltage sensors and knowledge of battery

discharge behavior (Zang et al, 2010).

7 CONCLUSIONS

This paper proposes an Information System for the

behaviour-based control of workforce. It defines the

architecture and functionality, always in line with

business needs relative to control and taking into

consideration the implementation costs. The latter is

achieved by using open software technologies and

adapting those technologies to give adequate support

to the BYOD paradigm.

A prototype was developed and tested under real

world conditions, not evaluating strictly controlled

parameters of a device’s accuracy -like in empirical

tests-, but testing the Information System as a whole

in real conditions that reflect normal business

activity. Under these conditions, it has also been

verified the compliance with the strict accuracy

demands proposed here as a reference.

In the proposed Information System three

concepts, which until now have been evaluated

separately by previous studies, have been brought

together: (i) IT consumerization, (ii) actual

capabilities of personal mobile devices which

employees can use in the workplace, and (iii) the

opportunity represented by the new Mobile Web

technology which provides information on the move

-no matter the device being used and without any

app installation hassles-, and significantly reduces

costs in comparison to other technological options.

Lastly, the study establishes a new reference

framework regarding qualitative and quantitative

requirement levels which must be set in relation to

the accuracy of mobile location systems used in

business Information Systems, particularly those

related to the control of the remote workforce.

The results obtained in this study confirm that at

present it is viable for companies to implement an

Information System for the control of remote

workforce that allows the companies to gain

competitiveness, at the same time reducing costs and

increasing the ROI, adopting a BYOD paradigm

which allows their employees to use their own

smartphone mobile devices in the workplace.

As for future work, the ongoing research focuses

on evolving this Information System so that (i) it can

be integrated seamlessly with the different emerging

indoor positioning technologies, allowing the

companies to get more accurate position data of

remote workforce under very adverse indoor

conditions, and (ii) it can take advantage of

contactless technologies, like NFC, in order to

speed up the check-in process when the workforce is

at local premises, and to provide location proof

using the NFC tag as an extra authentication factor.

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