OPTIMIZATION OF DATAFLOW ON MOBILE DEVICES IN
INFORMATION SYSTEM OF HOME CARE AGENCIES
Ondrej Krejcar, Dalibor Janckulik and Leona Motalova
Department of Measurement and Control, VSB Technical University of Ostrava
17. Listopadu 15, Ostrava, Czech Republic
Keywords: Mobile Device, Prebuffering, SQL Database, Web Service, Home Care Agencies.
Abstract: New kind of complex mobile devices can run full scale applications with same comfort as on desktop
devices only with several limitations. One of them is insufficient transfer speed on wireless connectivity.
Main area of interest is in a model of a radio-frequency based system enhancement for locating and tracking
users of a mobile information system. The experimental framework prototype uses a wireless network
infrastructure to let a mobile device determine its indoor or outdoor position. User location is used for data
prebuffering and pushing information from server to user’s PDA. The accessing of prebuffered data on
mobile device can highly improve response time needed to view large multimedia data. This fact can help
with design of new full scale applications for mobile devices. On mobile device the SQL Server CE
database is used as a cache. Finally the new way to manage the artifacts throw the framework is described
and tested. The prebuffering method is described in context of use on real case of information system of
home care agencies.
1 INTRODUCTION
The usage of various mobile wireless technologies
and mobile embedded devices has been increasing
dramatically every year and would be growing in the
following years. This will lead to the rise of new
application domains in network-connected PDAs
(Personal Digital Assistants) that provide more or
less the same functionality as their desktop
application equivalents. The idea of full scale
applications pursuable on mobile lightweight
devices is based on current hi-tech devices with
large scale display, large memory capabilities, and
wide spectrum of network standards plus embedded
GPS module. Example of such devices is HTC
Touch HD.
Users of these portable devices use them all time
in context of their life (e.g. moving, searching,
alerting, scheduling, writing, etc.). Context is
relevant to the mobile user, because in a mobile
environment the context is often very dynamic and
the user interacts differently with the applications on
his mobile device when the context is different.
My recent research of context-aware computing
has been restricted to location-aware computing for
mobile applications using a WiFi network (LBS
Location Based Services). The information about
basic concept and technologies of user localization
such as LBS, Searching for WiFi AP) can be found
in our published articles. On localization basis, I
created a special framework called PDPT (Predictive
Data Push Technology) which can improve a usage
of large data artifacts of mobile devices. We used
continual user position information to determine a
predictive user position. The data artifacts linked to
user predicted position are prebuffered to user
mobile device. When user arrives to position which
was correctly determined by PDPT Core, the data
artifacts are in local memory of PDA. The time to
display the artifacts from local memory is much
shorter than in case of remotely requested artifact.
The idea of prebuffering may not be only one
application method for user position knowledge. As
well as WiFi is not only one wireless network to use
for localization of user device. WiFi has advantage
in speed in indoor positioning therefore the
GSM/UMTS can be used in outdoor [Fig. 1]. The
GPS sensor is also embedded in several types of
current mobile devices, or it can be plugged by
SDIO or BT interface.
We would like to describe a position obtaining
from wireless networks background in the beginning
103
Krejcar O., Janckulik D. and Motalova L. (2009).
OPTIMIZATION OF DATAFLOW ON MOBILE DEVICES IN INFORMATION SYSTEM OF HOME CARE AGENCIES .
In Proceedings of the International Conference on e-Business, pages 103-108
DOI: 10.5220/0002226801030108
Copyright
c
 SciTePress

of next chapter to give a reader more information
about these themes.
Figure 1: Wireless networks and GPS sensor localization
possibilities on mobile devices.
2 PDPT FRAMEWORK CORE
The general principle of my simple localization
states that if a WiFi-enabled mobile device is close
to such a stationary device – Access Point (AP) it
may “ask” the provider’s location position by setting
up a WiFi connection. If position of the AP is
known, the position of mobile device is within a
range of this location provider. This range depends
on type of WiFi AP. The Cisco APs are used in my
test environment at Campus of Technical University
of Ostrava. I performed measurements on these APs
to get signal strength (SS) characteristics and a
combination of them called “super ideal
characteristic”. The computed equation for Super-
Ideal characteristic is taken as basic equation for
PDPT Core to compute the real distance from WiFi
SS.
From this super ideal characteristic it is also
evident the signal strength is present only to 30
meters of distance from base station. This small
range is caused by using of Cisco APs. These APs
has only 2 dB WiFi omnidirectional antenna.
Granularity of location can be improved by
triangulation of two or more visible WiFi APs. The
PDA client will support the application in
automatically retrieving location information from
nearby location providers, and in interacting with the
server. Naturally, this principle can be applied to
other wireless technologies like Bluetooth, GSM or
WiMAX.
To let a mobile device determine its own
position is needed to have a WiFi adapter still
powered on. This fact provides a small limitation of
use of mobile devices. The complex test with several
types of battery is described in my article [4] in
chapter (3). The test results with a possibly to use a
PDA with turned on WiFi adapter for a period of
about 5 hours.
2.1 The Need of Predictive Data Push
Technology
PDPT framework is based on a model of location-
aware enhancement, which I have used in created
system. This technique is useful in framework to
increase the real dataflow from wireless access point
(server side) to PDA (client side). Primary dataflow
is enlarged by data prebuffering. PDPT pushes the
data from SQL database to clients PDA to be helpful
when user comes at final location which was
expected by PDPT Core. The benefit of PDPT
consists in time delay reducing needed to display
desired artifacts requested by a user from PDA. This
delay may vary from a few seconds to number of
minutes. Theoretical background and tests were
needed to determine an average artifact size for
which the PDPT technique is useful. First of all the
maximum response time of an application (PDPT
Client) for user was needed to be specified.
Nielsen (Nielsen J., 1994) specified the
maximum response time for an application to 10
seconds (Haklay, M. and Zafiri, A., 2008
). During
this time the user was focused on the application and
was willing to wait for an answer. The book is over
20 years old (published in 1994). I suppose the
modern user of mobile devices is more impatient so
the stated value of 10 second will be shorter. This is
for me even better, because my framework is more
usable. I used this time period (10 second) to
calculate the maximum possible data size of a file
transferred from server to client (during this period).
If transfers speed wary from 80 to 160 kB/s the
result file size wary from 800 to 1600 kB.
The next step was an average artifact size
definition. I use a network architecture building plan
as sample database, which contained 100 files of
average size of 470 kB. The client application can
download during the 10 second period from 2 to 3
artifacts. The problem is the long time delay in
displaying of artifacts in some original file types. It
is needed to use only basic data formats, which can
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be displayed by PDA natively (bmp, jpg, wav, mpg,
etc.) without any additional striking time
consumption.
The final result of our real tests and
consequential calculations is definition of artifact
size to average value of 500 kB. The buffer size may
differ from 50 to 100 MB in case of 100 to 200
artifacts.
2.2 From Data Collection to
Localization
A first key step of the PDPT is a data collection
phase. I record information about the radio signals as
a function of a user’s location. The signal
information is used to construct and validate models
for signal propagation. Among other information,
the WaveLAN NIC makes the signal strength (SS)
available. SS is reported to units of dBm. Each time
the broadcast packet is received the WaveLAN
driver extracts the SS information from the
WaveLAN firmware. Then it makes the information
available to user-level applications via system calls.
If the mobile device knows the position of the
stationary device (transmitter), it also knows that its
own position is within a range of this location
provider. The typical range wary from 30 to 100 m
in WiFi case, respectively 50 m in BT case or 30 km
for GSM. Granularity of location can be improved
by triangulation of two or more visible APs (Access
Points). The PDA client currently supports the
application in automatically retrieving location
information from nearby WiFi location providers,
and in interacting with the PDPT server. Naturally,
this principle can be applied to other wireless
technologies like BT, GSM, UMTS or WiMAX. The
application (locator) is implemented in C# language
using the MS Visual Studio .NET with .NET
Compact Framework and a special OpenNETCF
library enhancement. Schema on figure describes a
localization process. The mobile client gets the SS
info of three BSs (Base Stations) with some
inaccuracy. Circles around the BSs are crossed in
red points on figure. The intersection red point
(centre of three) is the best computed location of
mobile user. The user track is also computed from
these measured SS intensity levels and stored in
database for later use by PDPT Core. This idea is
applicable in case of WiFi as well as BT and GSM
networks.
In previous research, I focused only to use of
WiFi networks while the other wireless possibilities
remained without a proper notice. Now I made an
enhancement of Locator component of PDPT
framework to allow operate with BT and GSM
networks.
In BT network case, the position of BT APs
must be known to allow the position determination.
To collect BT APs position info in outdoor
environment, the GPS can be used. For indoor area,
the GIS (Geographic Information System) software
with buildings map must be used to measure exact
position of BT AP against to local environment. To
manage with BT hardware of mobile device another
library InTheHand 32Feet.NET is used. The source
code has a simple implementation:
Example of a Locator Source Code – Scanning
the nearby for BT APs:
using InTheHand.Net.Bluetooth;
BluetoothClient bc = new
BluetoothClient();
BluetoothDeviceInfo[] bdi =
bc.DiscoverDevices();
foreach (BluetoothDeviceInfo BTdi in
bdi)
{
drDataRow = dtVisibleAP.NewRow();
drDataRow["AP_name"] =
BTdi.DeviceName.ToString();
drDataRow["MAC_AP"] =
BTdi.DeviceAddress.ToString();
drDataRow["Signal_Strength"] =
BTDi.Rssi;
drDataRow["Date_Time"] =
DateTime.Now;
drDataRow["AP_type"] =
AP_type.Bluetooth;
dtVisibleAP.Rows.Add(drDataRow);
}
GSM network is not local network but a cellular
network. The problem is in position information of
GSM BTSs (Base Transceiver Stations). The
operator doesn’t provide any such information. One
of possible solutions is based on unofficial BTSs
lists which can be found on internet. The lists are
typically available in HTML, TXT or CSV formats.
The medium rate for BTs with GPS position
information is about 90 % of all BTs in European
countries. In case of PDPT Framework, the list must
be converted to PDPT server database – GSM_BTS
table.
In all three described cases of nearby BSs
scanning, the data are saved to Locator Table in
PDPT server DB. Data are processed from Locator
Table throw the PDPT Core to Position Table. The
OPTIMIZATION OF DATAFLOW ON MOBILE DEVICES IN INFORMATION SYSTEM OF HOME CARE
AGENCIES
105

processing techniques depend on selected wireless
network. WiFi and BT network provide all visible
APs nearby the user. From list of these APs is
computed actual position (by triangulation).
Figure 2: Radio Interface Layer Architecture.
Mobile devices with windows mobile operation
system do not provide any GSM info to .NET
Compact Framework. Even any special framework
as in previous two cases is not known for me until
now. Only possibility is in use of RIL (Radio
Interface Layer) library. This library is divided into
two separate components, a RIL Driver and a RIL
Proxy. The RIL Driver processes radio commands
and events. The RIL Proxy performs arbitration
between multiple clients for access to the single RIL
driver. When a module calls the RIL to get the signal
strength, the function call immediately returns a
response identifier. The RIL uses the function
response callback to convey signal strength
information to the module.
Example of a Locator Source Code – retrieving
the GSM BTSs info with LIR:
[DllImport("ril.dll")]
public static extern IntPtr
RIL_GetCellTowerInfo(IntPtr
[DllImport("ril.dll")]
public static extern IntPtr
RIL_Deinitialize(IntPtr
[DllImport("ril.dll")]
public static extern IntPtr
RIL_GetSignalQuality(IntPtr
res = RIL_GetCellTowerInfo(hRil);
res = RIL_GetSignalQuality(hRil);
RILCELLTOWERINFO rci = new
RILCELLTOWERINFO();
result += String.Format("MCC: {0}, MNC:
{1}, LAC: {2}, CID: {3}, ",
rci.dwMobileCountryCode,
rci.dwMobileNetworkCode,
rci.dwLocationAreaCode, rci.dwCellID);
RILSIGNALQUALITY rsq = new
RILSIGNALQUALITY();
result += String.Format("Signal
Quality: {0}, MinSig {1}, MaxSig {2},
LowSig {3}, HighSig {4}",
rsq.nSignalStrength,
rsq.nMinSignalStrength,
rsq.nMaxSignalStrength,
rsq.nLowSignalStrength,
rsq.nHighSignalStrength);
The GSM network provide only one BS info in
each search cycle. This BS has the highest signal
strength. The more BTSs info is collected by a
several iteration cycles. During 10 cycles (per 10
seconds) the 4 BTS info is obtained on average.
The important info from BTSs is Signal Strength
and Time Advance (TA). SS is refreshed every
several seconds (in every scan) whereas TA is
provided only during some type of communication
with selected BTS (e.g. request to talk, move to
another area - Location Area Code (LAC)). The list
of these BTSs with info is further processed as in
previous case for WiFi and BT networks. Only
change is in usage of TA if it is accessible.
Another possibility to get the user position in
outdoor space is in GPS. GPS provide a position by
LONgitude and LATitude (X and Y). Only simple
conversion is needed to transform a GPS coordinates
to S-JTSK, which is used in PDPT Framework.
3 PARTIAL PREBUFFERING
PDPT framework design is based on the most
commonly used server-client architecture.
Technology data are continually saved to SQL
Server database (Arikan E., Jenq J., 2007), (Jewett
M., Lasker S., Swigart S., 2006).
The active presented area was divided to more
partial artefacts [Fig. 3]. This new modified system
is now implemented to our other projects, where the
position of user is needed. One of these projects is a
Guardian II. This project is for hospitals areas for
patients and physicians monitoring. In such
implemented the new possibilities of biomedical e-
health systems are discovered for increasing of
interactivity. Based on position of patient, the server
can select the nearest physician or nurse to act on
discovered problem. By this way the response on
problem can be reduced and it can help to save more
human life.
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Active area for move of map basis can be not
only on outer margin of the whole area, but it can be
on borders of several pictureboxs. By this technique
is possible to move with map basis in softer grid and
allow to more precisely presenting the actual
position of tracked object.
Table 2: Response time average – Insert one 50KB
artefact into SQL database trouch different technologies
[ms].
Linq ADO.NET
HP 614c 686,8 607,5
HP hx4700 867,8 767,7
HTC Advantage 883,3 781,4
HTC BlueAngel 531,7 470,3
Samsung Omnia 724,3 640,8
By consequential evaluation of object moving
speed, and with suitable modified map basis, we can
achieve the effect of zoom of map basis. After
application of such principle the system can be
applicable for open space with sufficient WiFi signal
for triangulation. This part of framework is suitable
for patient tracking in case of home care agencies.
We can track the time of one attendance of nurse at
the patient.
Preliminary test are graphed in next figure and
has only informative character.
0,0
100,0
200,0
300,0
400,0
500,0
600,0
700,0
800,0
900,0
1000,0
One of next steps is testing of accessible
technologies for accessing of SQL server buffer and
the selection of better one. In this time the testing of
technologies like LINQ, ADO.NET and the direct
access using SQL queries is being realized.
Responsetime[ms]
Figure 3: New application buffering – visible part merged
to smaller artefacts.
Table 1: Response time average – Insert one 50KB
artefact into SQL database trouch different technologies
[ms].
Linq ADO.NET
HP 614c 229,1 202,6
HP hx4700 289,2 255,8
HTC Advantage 294,6 260,6
HTC BlueAngel 177,4 156,9
Samsung Omnia 241,6 213,7
Linq50KB
ADO.NET50KB
Linq150KB
Figure 4: Average reaction times for inserting artefacts
trough different technologies on a few devices.
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107

4 CONCLUSIONS
I am focused on the real usage of the developed
PDPT Framework on a wide range of wireless
mobile devices and its main issue at increased data
transfer. For testing purpose, five mobile devices
were selected with different hardware and software
capabilities. The high success rate found in the test
data surpassed our expectations. This rate varies
from 84 to 96 %.
The PDPT prebuffering techniques can improve
the using of medium or large artifacts on wireless
mobile devices connected to information systems.
The localization part of PDPT framework is
currently used in another project of biotelemetrical
system for home care named Guardian II to make a
patient’s life safer. Another utilization of PDPT
consists in use of others wireless networks like BT,
GSM/UMTS, WiMAX, or in GPS. This idea can be
used inside the information systems like botanical or
zoological gardens where the GPS navigation can be
used in outdoor. The BT and GSM data collecting
and processing is described in this article along with
sample code. Some improvements of Locator
module or Artifact Manager are described as well as
improved architecture of PDPT server database. The
larger area of PDPT utilization can improve
importance of PDPT Framework in wireless mobile
systems.
ACKNOWLEDGEMENTS
This work was supported by the Ministry of
Education of the Czech Republic under Project
1M0567.
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