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Complex Mobile User Adaptive System Framework for Mobile Wireless Devices

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Paper describes a concept of User Adaptive System (UAS) as well as Predictive Data Push Technology (PDPT) Framework and Biotelemetrical Monitoring System (BMS) as two joined parts of complex UAS framework. Main focus is in contribution of UAS to user or patient and his life quality. A Position Oriented Database on a server and mobile devices is described as important part of whole UAS, because the position and context of user are one of the most important areas of UAS. Also the problem of low data throughput on mobile devices is described, which can be solved by PDPT framework. Localization and user tracking is described only as a necessary condition for prebuffering realization because the PDPT Core makes a decision when and which artifact (large data files) need to be prebuffered. Every artifact is stored along with its position information (e.g. in building or larger area environment). The accessing of prebuffered data artifacts on mobile device improve the download speed and response time needed to view large multimedia data. The conditions for real stocking in corporate areas are discussed at the end of paper along with problems that must be solved before stocking. © 2010 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
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COMPLEX MOBILE USER ADAPTIVE SYSTEM FRAMEWORK FOR
MOBILE WIRELESS DEVICES
Ondrej Krejcar
Department of Measurement and Control, Center for Applied Cybernetics,
Faculty of Electrical Engineering and Computer Science
VSB Technical University of Ostrava
17. Listopadu 15, 70833,
Ostrava-Poruba, Czech Republic
ondrej.krejcar@remoteworld.net
ABSTRACT
Paper describes a concept of User Adaptive System (UAS) as well as Predictive
Data Push Technology (PDPT) Framework and Biotelemetrical Monitoring
System (BMS) as two joined parts of complex UAS framework. Main focus is in
contribution of UAS to user or patient and his life quality. A Position Oriented
Database on a server and mobile devices is described as important part of whole
UAS, because the position and context of user are one of the most important areas
of UAS. Also the problem of low data throughput on mobile devices is described,
which can be solved by PDPT framework. Localization and user tracking is
described only as a necessary condition for prebuffering realization because the
PDPT Core makes a decision when and which artifact need to be prebuffered. The
accessing of prebuffered data artifacts on mobile device improve the download
speed and response time needed to view large multimedia data.
Keywords: User Adaptive System, Localization, Biotelemetry, Position Oriented
Database, Prebuffering.
1 INTRODUCTION
The idea of User Adaptive Systems (UAS)
grown from interaction between user and system
(e.g. throws his mobile device). Such interaction
can behold in the reaction on user's non declared
requests. These requests are based on current user
environment and biological or emotional state (e.g
where I am?, what I feel?, am I ok?, etc.). Such user
questions can be answered by sensors on user body
or inside the user devices. By the help of user
mobile device, we can get a user location (e.g. user
current position, user future-predicted position, his
movement and tracking, etc.). Biomedical sensors
on user body can detect several important
biomedical data, which can be used for
determination of user emotional state in the
environment around. By the combination of user’s
requests (known or predicted) in conjunction with
other sources of user’s knowledge and behaviours,
the sophisticated information system can be
developed based on presented UAS Framework.
The impact of UAS can be seen in the
increased user comfort when accessing these
mobile UAS. In ideal case, everything what user
can imagine to have in his mobile UAS is there. A
one specific kind of problems is based in increased
data amount in new mobile systems. In current
cases, the user need to specify a data to be
downloaded to his mobile device and he need to
wait for data downloading and displaying. Due to a
several limitations in hardware of current mobile
devices, the use of such large amount data has
result in lower user comfort. The needs of any
techniques to reduce such large data amount or to
preload them before user’s needs, is still growing
up. We created a Predictive Data Push Technology
(PDPT) Framework to solve these problems by data
prebuffering. Our idea can be applied on a variety
of current and future wireless network systems.
More usability of PDPT grows from definition of
area to be prebuffered as well as from evaluation of
artifacts or other user's behaviour sources.
Additional will be presented in sections (3), (6).
The second area of problems which we would
like to solve is based on a users biomedical data
inputs and a wide area of their possible utility.
Current body sensors allow a monitoring of a huge
number of biomedical data information (e.g. use a
special t-shirt equipped with an ECG, temperature,
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pressure or pulse sensors). Current hi-tech mobile
devices are equipped with a large scale display,
provide a large memory capabilities and a wide
spectrum of network standards plus embedded GPS
module (e.g. HTC Touch HD, HD2). These devices
have built-in also a special accelerometer which can
be used to determine a user’s body situation (user is
staying or lying). Last but not least equipment is a
light sensor which can be used not only to
brightness regulation. Use of these declared inputs
will be discussed and presented in section (4).
2 ARCHITECTURAL DESIGN FOR
UBIQUITOUS COMPUTING SYSTEMS
Ubiquitous Computing (UbiCom) is used to
describe ICT (Information and Communication
Technology) systems that enable information and
tasks to be made available everywhere, and to
support intuitive human usage, appearing invisible
to the user [1].
Three basic architectural design models for
UbiCom system can be divided to smart devices,
smart environment and smart interaction. The
concept of “smart” means that the object is active,
digital, networked, can operate autonomously, is
reconfigurable and has a local control of the
resources which it needs such as energy, data
storage, etc [1]. These three main types of system
design may also contain sub-systems, sub-parts or
components at a lower level of granularity that may
also be considered as a smart (e.g., a smart
environment device may contain smart sensors and
a smart controller, etc). An example of a three main
types of UbiCom models is presented in (Fig. 1).
Figure 1: Three models of ubiquitous computing:
smart devices, smart environments and smart
interaction [1].
Many sub-types of smarts for each of the three
main types of smarts can be recognized. These
main types of smart design also overlap between.
Smart device can also support some type of smart
interaction. Smart mobile device can be used for
control of static embedded environment devices.
Smart device can be used to support the virtual
view points of smart personal spaces (physical
environment) in a personal space which
surrounding the user anywhere.
Satyanarayanan [3] has presented different
architectures for developing UbiCom systems in
way of which angle it is focused on a design:
1. Mobile distributed systems are evolved from
distributed systems into ubiquitous computing
2. UbiCom systems are developed from smart
spaces characterized by invisibility, localized
scalability and uneven conditioning.
Poslad [1] has extended a Satyanarayanan
model to Smart DEI model (Device Environment
and Interactions). Poslads model also incorporates
smart interaction. Smart DEI model also reverses to
hybrid models. It is widely assuming by users that
the general purpose of end-user equipment will
endure but also it will evolve into a more modular
form.
2.1 Smart Devices
A smart device is a device that is digital, active,
networked, user reconfigurable and that can operate
to some extent autonomously. Smart devices can be
characterized like personal computers or mobile
phones with tend to be multi-purpose ICT devices.
These devices operate as single portals used to
access a multiple application services which are
running locally on the device or remotely on servers.
A range of forms are available for smart devices.
Smart devices can be defined as personal devices,
having a specified owner or user. In the smart
device model, the place of application user interface
is on side of the smart device. The main
characteristics of smart devices consist of concept
of: mobility, dynamic service discovery and
intermittent resource access.
Figure 2: Three models of ubiquitous computing:
smart devices, smart environments and smart
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interaction [1].
2.2 Smart Environments
A first definition of a smart environment
brought by Coen [4] as a computation which is
easily used to enhance ordinary activities. Cook and
Das [5] refer to a smart environment as ‘one that is
able to acquire and apply knowledge about the
environment and its inhabitants in order to improve
their experience in that environment’. A smart
environment consists of a set of networked devices
that have some connection to the physical world.
The devices which are used for a smart
environment usually execute a single predefined
task (e.g., motion or body heat sensors coupled to a
door release and lock control). Embedded
environment components can be designed to
automatically respond to interaction with user using
iHCI (implicit Human Computer Interaction). A
person can for example walk towards closed doors,
which are automatically opens as a respond. By this
reason the smart environments support a bounded,
local context of user interaction. Smart
environments will also follow a novel and
revolutionary upgrades to be incorporated into the
environment in the sense of a support less obtrusive
interaction (pressure sensors can be for example
incorporated into surfaces to detect a people sitting
location or walking over).
Figure 3: Three models of ubiquitous computing:
smart devices, smart environments and smart
interaction [1].
2.3 Smart Interaction
While a smart devices and smart environments
support the core properties of UbiCom, an
additional type of design is needed to connect
together their numerous particular activity
interactions. Smart interaction is needed to support
interaction model between UbiCom applications
and their UbiCom infrastructure, physical world
and human environments. In the smart interaction
design model, system components dynamically
interact to reach common goals. Components
interact to reach goals jointly because they are
deliberately not designed to execute and complete
sets of tasks to reach goals all by themselves. There
are several benefits to designs based upon sets of
interacting components. Interaction between
UbiCom system components does not exist only in
one predefined level but it is spread in a range of
levels from primitive to smart. Primitive interaction
uses fixed interaction protocols between two
statically linked dependent objects. While the smart
interaction uses richer interaction protocols
between multiple dynamic independent objects.
Figure 4: Three models of ubiquitous computing:
smart devices, smart environments and smart
interaction [1].
2.4 Adaptive Systems for Ubiquitous
Computing
Ubiquitous computing provides a vision of
computing systems which are located everywhere
around us, embedded in the things of our everyday
life. They provide an easy access to information
and communications bases dedicated to our current
location. People are able to interact with any
ubiquitous computing environment which they
attend. This is a reason why the ubiquitous
computing environments must respond dynamically
to specific user needs, resources dedicated to their
owner’s rights or to the current usage context.
These require a high level of adaptivity which must
be provided by ubiquitous computing systems and
related connecting networks [2].
Described project deals with several of issues
related to providing such adaptivity for ubiquitous
computing environments which will be described
more in the following sections.
3 REACTION ON A CHANGE OF
LOCATION – LOCATION-AWARE
ADAPTATION
We can imagine the usage of such described
UAS in the information systems area of botanical or
zoological gardens. In such areas there has been a
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big potential of usage of a continual localization by
use of GPS or wireless networks (in case the GPS
has not a sufficient signal – e.g. in urban centres or
neighbourhoods with high buildings, forest parks or
in deep valleys). There is also a possibility to
compute a current and predicted user track, so we
can predict a position of user in near future (e.g. 25
meters north in one minute). Usability of these
information sources is uncountable.
One of possible use of user predicted position
is for a determination of a data, which will be
needed by user of mobile UAS in near future. Such
data (data artifacts) can be preloaded to user’s
device memory for future requests. The need of
preloaded artifacts grown from a need of up to date
data context of dynamic online system. Of course
when static offline system is used, there is a
possibility to load a needed data before usage (e.g
store artifacts at SD Card with a size limit to several
GB). When user request info about his location in
context of zoo or garden (turn-on the device is only
needed by user), the client application will respond
with a map of near surroundings and a prebuffered
data artifacts. User can select a documentary about
animals or vegetation around him which can be
viewed or played. User can act with direct requests
to selected kinds of these. These preferred kinds
will be taken into account to evaluate future
objects/artifacts and preloaded only the most
important ones for a user. The type of artifact is
also evaluable as well as his size because the user
may not want to look at too long or micro
presentation.
As client devices of online UAS, the mobile
wireless devices like PDA or Smart phones are
commonly used equipped with internet connectivity.
The connection speed of the two most common
standards GPRS and WiFi varies from hundreds of
kilobits to several megabits per second. In case of
online UAS or some other types of facility
management, zoological or botanical gardens,
libraries or museums information systems, the WiFi
infrastructure network is often used to interconnect
mobile device clients with a server. Unfortunately,
the low performance hardware components are used
in PDAs or SmartPhones due to a very limited
space. Due this a theoretical maximum connection
speed is not reachable on such devices. The limited
connection speed represents a problem for clients of
online system using large artifacts (data files). In
some specific cases it is not possible to preload
these artifacts before the use of mobile device in a
remote access state due any reason.
3.1 Low System Throughput on Current
Mobile Devices
The real downlink speed for WiFi network
(802.11b,g) is about 1280 kbit/s for modern PDA
devices [6], [7]. Primary dataflow can be increased
by data prebuffering. Selecting of data objects to be
buffered to mobile device cache is made on the
base of position of user’s device. For every position
in area, where the prebuffering is being made, the
location-aware objects for such user’s position
exists. PDPT Core pushes a data from SQL
database (WLA database (Fig. 2)) to clients PDA
on a base of PDPT Core decision algorithm.
Figure 5: Scheme of WLA architecture PDPT
server database.
The benefit of using a PDPT consists in
reduction of time delay, which is needed to display
requested artifacts from PDA client. This delay
must not be longer than the time for which a user is
able to wait for some response from application.
Hence, the maximum response time of an
application (PDPT Client) for user must be
specified firstly. Nielsen in his book [8] specified
this time delay to 10 seconds [9]. During this time
the user was focused on the application and was
willing to wait for an answer. The Nielsen book is a
basic literature for this phenomenon. Galletta,
Henry, McCoy and Polak (2002) findings suggest
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that, ‘decreases in performance and behavioral
intentions begin to flatten when the delays extend to
4 seconds or longer, and attitudes flatten when the
delays extend to 8 seconds or longer’. Based on this
knowledge, we defined this delay for our testing
purposes to 5 seconds. For this time is possible to
transfer (from server to client) a data amount of 800
kB (for 1280 kbit/s downlink).
The next step was an average artifact size
definition. The network architecture building plan
is used as a sample database, which contained 100
files of average size of 470 kB. The client
application can download during the 5 second
period from 1 to 2 artifacts. The final result of
several 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.
3.2 Position Oriented Database
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 varies 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) or using the more accurate
position algorithms (Monte Carlo localization). In
PDPT framework only the triangulation technique
is used due to the sufficient granularity of user
position information. Monte Carlo localization was
tested in one segment of tested environment
without marginal success (Time needed to
implement algorithm was inadequate to position
quality results). Information about the user position
are stored in Position table (Fig. 2). Locator table
contain info about wireless AP with signal strength
which are needed to determine user position.
WiFi_AP, BT_AP and GSM_AP tables contain all
necessary info about used wireless base stations.
WLA_data table contain data artifact along with
their position, priority and others metadata.
3.3 PDPT Client - Mobile Database Server
The large data artifacts from PDPT Server
(WLA_data table (Fig. 2)) are needed to be
presented for user on mobile device. In case of
classical online system the data artifacts are
downloaded on demand. In case of PDPT solution,
the artifacts are preloaded to mobile device cache
before user requests. As mobile cache the SQL
Server 2005 Mobile Edition was selected. Our
mobile cache contain only one data table Buffer.
Only the needed columns from PDPT server
WLA_data table were taken for mobile version
Buffer table. MS SQL Server 2005 Mobile Edition
was selected for easiest managing of them in case
that the Visual Studio and classic SQL Server are
used. Small data amount for installation (2,5 MB) is
also an advantage.
4 REACTION ON A CHANGE OF
BIOMEDICAL DATA – ACTIVE
CONTEXT-AWARE ADAPTATION
A key problem of context-aware systems
design is to balance the degree of user control and
awareness of their environment. We can recognize
two extreme borders as active and passive context-
aware. In active context-aware system, the UAS is
aware of the environment context on behalf of the
user, automatically adjusting the system to the
context without the user being aware of it [1]. This
is a useful in our application where a strict time
constraints exists, because the user-patient cannot
due to immobility, or would not otherwise be able
to adapt to the context quickly enough. We are
using principles of UAS in area of biomedical data
processing, where we try to predict some kind of
problems by patient data analysis. We developed a
context-aware Biotelemetrical Monitoring System
(BMS) as a part of the UAS and PDPT Framework
project facilitates the following:
Real-time collection of the patient vital signs
(e.g. ECG, EEG) by means of a Body Area
Network (BAN) or direct wireless connection to
PDA device monitoring station.
Real-time transmission of the vital signs using
the wireless connectivity to the healthcare
professionals through a complete architecture
including a server database, web services, doctors
web access to patients collected and preprocessed
data.
Seamless handover over different wireless
communication technologies such as BlueTooth,
WiFi, GPRS or UMTS.
Context-aware infrastructure to sense the
context (e.g. location, availability, activity, role) of
the patients and Emergency Response Services
(ERSs) to provide assistance to the patient in case
of an emergency. An ERS could be fixed (e.g.
hospital) or mobile (e.g. caregiver). A mobile ERS
is published in the BMS [18].
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Figure 6: Flowchart of Reactive (Left – Fig.6.a)
and Proactive (Right – Fig. 6.b) ERSs Selection and
Invocation Approach.
Classical access to patients request are made by
reactive flowchart (Fig. 6.a), where a patient is
equipped with a classical offline measuring devices
with some type of alarms. Every violated alarm
need to be a carried out by doctor decision. Such
access is very time-consuming.
Second proposed access is based on a proactive
principle (Fig. 6.b), where the patient is equipped
with an online measuring devices with an online
connection to some kind of superior system (in our
case the BMS is presented). In this case, a patient’s
measured data are processed on mobile monitoring
station or at server. An alert will invoke when the
anomaly data are founded in patient’s records.
Consequently the doctor is responsible to make a
decision to invoke other ERSs or to remove Alarm
(in case of false detection of anomaly). Such kind
of behavior is based on UAS. In many of events a
predicted and solved problems can save a life. The
predicted patient’s problems are in most cases
minor in compare to a major problems detected in
time where occurred.
4.1 Biomedical Data Acquisition, Processing
and Proactive Reaction
Our developed BMS can currently handle two
types of biomedical data:
1. 12 channels wireless ECG – BlueECG and
2. Bipolar wireless ECG – corbelt.
These data are measured, preprocessed on
mobile monitoring station (PDA, embedded device,
notebook), visualized on monitoring station’s
display (in available), sent by wireless connection
to web service and stored on server for
consequential access by doctors or medical personal.
Used data acquisition devices provide a successful
result in case of testing a developed solution. In
near future we plan to use a t-shirt with equipped
biosensors network (e.g. ECG, pulse, oxy, pressure).
The biomedical ECG data are continually processed
(in Real Time) through a complete infrastructure of
developed UAS. First false artifact recognition is
made on mobile measurement stations near the
patient to allow an immediate action from ERSs.
The more sophisticated data analysis is made at
server level. This data processing is made on the
base of neuron network and fuzzy logic behaviour.
Unfortunately, we reach only a small level of
successful false detection (patient problem
detection) up to date. In this area we are expected a
future impact of our solution. The low detection
rate is caused by several facts. Of course the better
algorithms are needed at the first, but this problem
cannot be solved satisfactory in near future.
Another problem is caused by a slow connection by
WiFi network, because some biomedical data
contain a huge amount of data. This problem is
possible to solve by our PDPT framework as a part
of our UAS solution. By this solving, we improve
the quality of detection by a 40 % (median value of
12 channels ECG). All the same, the real time
transfer rate is now still fail to reach.
4.2 wakeNsmile Application – Proactive User
Adaptive System
Proactive principle can be used not only in
large distributed solution for medical centres, but it
can be found usable in a many other solutions. One
of them we found in an application to allow for
people have a happy wake up. A mobile device
application was developed to solve a problem of
bad wake up at morning for all the people.
Sleep is a complex process regulated with our
brain and as such is driven by 24 hour biological
rhythm. Our biological clocks are controlled by
chemical substances that are mostly known to us.
Approximately two hours after we fall asleep
our eyes starts to move back and forth irregularly.
Based on this fact scientists divided sleep stages
into two main stages REM sleep with (Rapid Eye
Movement) and NREM sleep stage (Non Rapid Eye
Movement). NREM sleep is divided into another
four sub-stages, when with increasing number the
sleep is more and more deeper.
During healthy individual sleep, REM and
NREM stages changes a few times. Most of the
dreams are happening in REM stage. Body muscles
are completely loosened and thanks’ to this fact one
is awaken refreshed.
During deep (NREM 3 and 4) sleep stages
blood pressure is decreasing which lowers chance
of cardiovascular danger. Also growth hormone is
produced in its maximum in adolescent age. Sleep
stages are possible to divide into several:
(a) Wake (Awake),
(b) REM – we dream in this stage,
(c) NREM1 – falling asleep,
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(d) NREM2 – light sleep,
(e) NREM3 – deep sleep,
(f) NREM4 – deepest sleep.
wakeNsmile application (Fig. 7) was developed
in C# programming language and uses .NET
compact framework version 3.5, which is a special
derivative of .NET framework for mobile devices
[7]. Application was developed in Visual Studio
2008 Team Edition on Windows Mobile 6.5
emulator and tested on a Hewlett-Packard mobile
device (originally HTC Roadster) with Windows
Mobile 6.5 operating system. Minimal requirements
for application running are Mobile device with
Windows Mobile 6 and higher and .NET compact
3.5 or higher.
wakeNsmile application uses user control
called Alarm, that has been created as a part of this
project. Application is using Math.NET neodym
library for FIR (Finite Impulse Response) filter
design and WaveIn and WaveOut libraries for
mobile device sound interface communication.
Figure 7: wakeNsmile application example in
Visual Studio 2008 Windows Mobile 6 emulator.
wakeNsmile application is developed to react
on users declared request in form of happy wake up
at predefined time (Fig. 7). The time defined for
alarm is however the latest possible time to wake
up of user. We are trying to detect a body state in
which the user is most able to wake up with a smile.
Time period for detection analysis of state phases is
declared to 30 minutes. A Fast Fourier
Transformation (FFT) and some other sophisticated
methods are used for it. Created application is an
ideal example of user adaptive solution for mobile
devices. Currently a single application is developed,
but a distributed architecture version with a neural
network analysis and people database is planned for
future steps to be a completely embedded solution
at Mobile UAS Framework. We executed several
tests with very promising results. More than 70 %
of successful happy wake up of test persons at
morning without any restriction to test persons. For
healthy test persons the results was more than 89 %
[13].
Developed application act as a proactive
solution in sense of happy wake up of users in most
suitable body state. User adaptivity can be however
sustained by user’s inputs collection store (user
manual sets along with successfully detected mild
sleep stages) to achieve a higher level of user
adaptivity based on them. Similar adaptation is used
by next project of intelligent alarm called “Gently
Alarm” [15].
In wide context a limited type of green
pervasive computing system can grown from
developed application as a base for such system
inputs in form of users knowledge or users mental
state during a day after a successful or unsuccessful
happy wake up [16], [17].
5 REACTION ON A CHANGE OF LOGGED
USER – PERSONAL-AWARE
ADAPTATION
Next possible way to react on user needs is in
classical user input processing. Based on user login
a personal-aware adaptation of UAS can be defined.
Well known is a model of screen resolution
adaptation based on a used mobile device. Classical
way is in user setting module located in used
application. This however requests a user action at
each time a different user is logged in.
5.1 Adaptive User Interface for Mobile User
Adaptive System
To prevent such waste user time, user interface
adaptivity can be developed and used based only on
user login information. UAS server can collect a
user data such as a request of special user interface
layout (font size, buttons size and locations, wide of
scrollbars, etc.). After user login application is
initiated in used best fitting scheme. Example of
such user defined user interface is shown at (Fig. 8)
resp. (Fig. 9).
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Figure 8: User interface layout initiated based on
UAS server data. QVGA layout on a VGA display.
Figure 9: User interface layout initiated based on
UAS server data. VGA layout on a VGA display.
Depending on a user ability to view smaller
fonts an indispensable number of other rows are
viewable by user a higher resolution (Fig. 9).
5.2 New Components for Mobile User
Adaptive Systems
However not every user is able to access small
fonts so user interface with a large elements of user
interface are welcome. Examples of such elements
are described in (Fig. 10). A first example presents
switchers (Fig. 10.a.). They provide a sizable
intuitive way to support an adaptation on user
ability. Every described element is developed as
components of UAS framework. Use in any other
projects is therefore very easy and comfortable.
Figure 10: User interface components: 0/1 switch
(Upper-Left – Fig. 10.a), On/Off switch (Upper-
Right – Fig. 10.b) and navigation arrows where a
left direction is selected (Lower – Fig. 10.c)
Another component of UAS framework is
navigation arrows (Fig. 10.c.), which is a sizeable
component with one enumeration type of direction
which can be used to easily navigate in some
outdoor use cases.
Next component of UAS framework is circle
visualizer (Fig. 11.a.), which is a sizeable
component with two properties: color areas
definition and min-max values. This component can
be used to inform user about valued state of some
controlled properties in the context of their
boundary values. By use of this context a user can
get more complex information instead of classical
value information (e.g. in text/numerical form).
The last example of component is based on
previous circle visualizer component, which is
parent of a new component is sense of object
programming model. The component can represent
e.g. milliammeter (Fig. 11.b.) or voltmeter (Fig.
11.c.) as a two examples of measurement
visualization component. From parent it inherits all
properties and it adds a text properties for type of
meter which it is represent in real case. Of course
the shape is not a circle type, but it is rectangle.
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Figure 11: User interface components of
measurement visualization: value of 17 at circle
visualizer (Upper-Left – Fig. 11.a), voltmeter
(Upper-Right – Fig. 11.b) and milliammeter (Lower
– Fig. 11.c).
More information about user adaptation
improvement using software components for
mobile control systems in .NET Compact
Framework can be found in [14].
6 THE USER ADAPTIVE SYSTEM
FRAMEWORK
A combination of a predicted user position with
prebuffering of data, which are associated with
physical location bears many advantages in
increasing throughput of mobile devices. The key
advantage of PDPT solution in compare to existing
solutions is that the location processing, track
prediction and cache content management are
situated at server side. The solution allows for
managing many important parameters (e.g. AP info
changes, position determination mechanism tuning,
artifacts selection evaluation tuning, etc.) online at
a server. By adding a Biomedical Data Processing
solution, the Complex User Adaptive System
(UAS) Framework is growing from (Fig. 12).
While the whole PDPT Framework concept allow
to manage a artifacts in context-awareness and
time-awareness, the UAS Framework shift these
possibilities to manage artifacts in biomedical
context-awareness allowing a response for example
to user´s non declared needs.
Biomedical Data Processing sensor at Mobile
Device side of architecture (Fig. 12) collect
information from user’s body through a Bluetooth
connection to any kind of wearable biotelemetrical
devices. These data are transferred to UAS Server
along with locator module data, which is processing
these knowledge to act with adequate reaction in
sense of user comfort improvement as a response
time reducing for requested information by data
prebuffering or any other reaction (e.g. screen
resolution improvement, display brightness etc.).
Figure 12: User Adaptive System Framework
architecture.
Artifact data object can be defined as a
multimedia file type in complex-awareness, which
represent an object in Position Oriented Database –
table WLA_data with time, position and
biomedical-awareness. To manage locations of
artifacts, firstly the building map is needed. The
position of corporate APs is also needed to
determine a user position based on a distance from
each visible APs. All obtained positions info need
to be stored in UAS server database through a
PDPT Core web service. Artifacts with position
coordinates are stored in WLA_data table by use of
“WLA Database Artifact Manager”. This software
application was created to manage the artifacts in
Position Oriented Database.
The PDPT prebuffering principle consists of
several following steps:
1. Client must activate the PDPT buffering
checkbox on PDPT tab at PDPT Client, which
creates a list of artifacts (PDA buffer view sample
which contain only ID of artifacts), which are
contained in his mobile SQL Server CE database.
2. PDPT Framework Core web service module
creates own list of artifacts (imaginary view sample
of PDA buffer) dedicated to actual user device
position. It also compares it with real “PDA buffer
view sample”. The area is defined as a 3D rectangle
object where the user’s position is located in center.
3. The PDPT Core continues in next step with
comparing of both images. If there are some missed
artifacts in PDA buffer, they are prebuffered to
PDA buffer. When all artifacts for current user
position are prebuffered in PDA buffer, there is no
difference between images.
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4. After all artifacts are prebuffered to PDA buffer
of mobile client, the PDPT Core is going to make
steps 1 to 3 once more for a new predicted user
position with new enlarged area (3D rectangle).
7 DISCUSSION OF RESULTS
The PDPT Framework project is developed
from 2005 until now in several consequential
phases. Current state of the project is near the real
company stocking. Final tests were executed in
university campus of Technical university of
Ostrava. For company stocking is possible to think
about several areas. These possibilities will be
discussed in [section VII.2].
7.1 Final Test Results of PDPT Framework
Part
For testing purpose, five mobile devices were
selected with different hardware and software
capabilities. Six types of tests batches were
executed in test environment. Two different test
scenarios were executed as static and dynamic tests
scenarios. Static test was based on a predefined
collection of data artifact which belongs to defined
user position in test environment. Five test position
were selected where approximately 12 data artifacts
was needed to successful prebuffering. Three
iterations were repeated in each position. If any of
these expected artifacts stay un-buffered, the
quality of prebuffering is going low. The tests were
performed with result from 69,23 % to 100 %. The
mean value of test results was 93,63 %. From all 15
tests, the 9 were executed with a 100 % of
successful score.
Every dynamic test was between two points
with 132 meter distance. Every even test was in
reversed direction. Five iterations (five devices
used) were made during one batch. Results provide
a good level of usability when user is moving
slowly (less than 0,5 m/s). This fact is caused by
low number of visible WiFi APs in test
environment, where 60 % of time only 1 AP was
visible, 20 % for 2 visible and 5 % for 3 or more
visible WiFi APs. 15 % of time represents a time
without any WiFi connections. Reached values of
prebuffering quality in such case are very good.
7.2 Possibilities of Using a PDPT Framework
in Real Environment
Dynamic tests of PDPT Framework show the
problem of a low number of visible WiFi APs for
localization determination in the test environment
of university campus. For the real case of usage and
for the high level of prebuffering quality, the
minimal number of simultaneously visible WiFi
APs at each place of stocking area must be from 3
APs. For successful stocking of PDPT, the area of
prebuffering is needed to be defined and also the
data artifacts must be defined. One way is in use of
developed software “WLA Database Artifact
Manager” for offline case, but the useful solution is
in determination of large data objects in online case.
Such determination is not easy. Possible solution
can be seen in application of Position Oriented
Database scheme to convert an existing server
database of online system to Position Oriented
Database structure. After such conversion, the data
are possible to select based on position in stocking
area. Consequently if data object – artifact can be
selected, the PDPT server can prebuffer such data
to mobile device.
As a summary, the PDPT is now usable at
immobile patients at 100% successful rate of
prebuffered artifacts. Such sort of patients is
specific for only low speed of their transfer inside
the environment. Due this fact the PDPT is
functioning. If the environment for prebuffering
will be equipped with a higher number of WiFi APs,
the usability of PDPT in dynamic cases will be
much more achievable.
8 CONCLUSIONS
A concept of UAS as well as PDPT Framework
and BMS Framework was described with main
focus on Position Oriented Database on server and
mobile devices. Coexistence of proposed solutions
is in unnumbered areas and the results of complex
solution are better than expected. Also the final
static and dynamic tests were performed and
discussed. The developed UAS can be stocked on a
wide range of wireless mobile devices for its main
issue at increased downlink speed. The localization
part of PDPT framework is currently used in
another project of biotelemetrical system for home
care agencies to make a patient’s life safer. Several
areas for PDPT stocking was founded in projects of
Biotelemetry Homecare. In these selected areas the
use of PDPT framework is not only partial, but
complete include the use of wide spectrum of
wireless communication networks and GPS for
tracking people and urgent need of a high data
throughput on mobile wireless connected
monitoring devices. Several of UAS principles can
be used there also. These possibilities will be
investigated in future.
ACKNOWLEDGMENT
This research has been carried out under the
financial support of the research grants “Centre for
Applied Cybernetics“, Ministry of Education of the
Czech Republic under Project 1M0567 and “Safety
and security of networked embedded system
applications”, GACR, GA 102/08/1429, Grant
Agency of Czech Republic.
UbiCC Journal, Volume 6: Issue 3
893
9 REFERENCES
[1] Poslad, S.: Ubiquitous Computing: Smart
Devices, Environments and Interactions, John
Wiley & Sons, Ltd, London,UK, ISBN 978-0-470-
03560-3 (2009)
[2] Lewis, D., O'Sullivan, D., “Adaptive Systems
for Ubiquitous Computing”, In Proceedings of the
1st international symposium on Information and
communication technologies, ACM International
Conference Proceeding Series; vol. 49, pp. 156,
(2003)
[3] Satyanarayanan, M. “Pervasive computing:
vision and challenges”. In IEEE Personal
Communications, vol 8, pp. 10–17. (2001)
[4] Coen, M. H., “Design principles for inteligent
environments”. In Proceedings of 15 th National /
10 th Conference on Artificial Intelligence /
Innovative Applications of Artificial Intelligence,
pp. 547–554. (1998)
[5] Cook, D.J. and Das, S.K., “How smart are our
environments? An updated look at the state of the
art.” In Pervasive and Mobile Computing, 3(2): pp.
53–73. (2007)
[6] Krejcar, O., Prebuffering as a way to exceed
the data transfer speed limits in mobile control
systems, In ICINCO 2008, 5th International
Conference on Informatics in Control, Automation
and Robotics, May 11-15, 2008 Funchal, Portugal,
pp. 111-114, (2008)
[7] Krejcar, O.: Problem Solving of Low Data
Throughput on Mobile Devices by Artefacts
Prebuffering, In EURASIP Journal on Wireless
Communications and Networking, Article ID
802523, 8 pages. Hindawi publishing corp., New
York, USA, (2009)
[8] Nielsen, J.: Usability Engineering, Morgan
Kaufmann, San Francisco, (1994)
[9] Haklay, M., Zafiri, A.: Usability engineering
for GIS: learning from a screenshot. The
Cartographic Journal 45(2), 87–97 (2008)
[10]Brasche, G. P., Fesl, R., Manousek, W., Salmre,
I. W., “Location-based caching for mobile devices”,
United States Patent, Microsoft Corporation
(Redmond, WA, US), 20070219708 (2007)
[11] Squibbs, R. F., “Cache management in a
mobile device”, United States Patent, Hewlett-
Packard Development Company, L.P.,
20040030832, (2004)
[12] Krejcar, O., “Using of Ubiquitous Computing
Principles to Develop a Mobile User Adaptive
System Framework”, In Proceedings of 9th
RoEduNet IEEE International Conference,
RoeduNet 2010, 24. – 26. June 2010, Sibiu,
Romania, pp. 352-357, ISBN 978-1-4244-7335-9,
ISSN 2068-1046, (2010)
[13] Krejcar, O., Jirka, J., Janckulik, D., “Proactive
User Adaptive System for Windows Mobile
Devices – Processing of Sound Input Signal for
Sleep State Detection”, in Proceedings of 2nd
International Conference on Mechanical and
Electronics Engineering (ICMEE 2010), vol. 1, pp.
374-378, Kyoto, Japan, August 2010. DOI
10.1109/ICMEE.2010.5558525
[14] Krejcar, O., Cajka, J., User Adaptation
Improvement Using a Software Components for
Mobile Control Systems in .NET Compact
Framework, In Proceedings of International
Conference On Networking and Information
Technology, ICNIT 2010, 11. – 13. June 2010,
Manila, Philippines, NJ. IEEE Conference
Publishing Services, 2010 pp. 545-549, ISBN 978-
1-4244-7579-7, DOI 10.1109/ICNIT.2010.5508453
[15] Gentle Alarm iPhone Application developed by
Craft mobile company (http://gentle-alarm.com/)
2010
[16] N. Chilamkurti, S. Zeadally, S. Jamalipour, and
S. K. Das, “Enabling Wireless Technologies for
Green Pervasive Computing,” in EURASIP Journal
on Wireless Communications and Networking, vol.
2009, Article ID 230912, 2 pages, 2009.
doi:10.1155/2009/230912
[17] N. Chilamkurti, S. Zeadally, and F. Mentiplay,
“Green Networking for Major Components of
Information Communication Technology Systems,”
in EURASIP Journal on Wireless Communications
and Networking, vol. 2009, Article ID 656785, 7
pages, 2009. doi:10.1155/2009/656785
[18] Pawar, Pravin and Beijnum van, Bert-Jan and
Mei, Hailiang and Hermens, Hermie (2009)
Towards Proactive Context-Aware Service
Selection in the Geographically Distributed Remote
Patient Monitoring System. In: 4th International
Symposium on Wireless Pervasive Computing,
ISWPC 2009, 11-13 Feb. 2009, Melbourne,
Australia.
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... The concept of "smart" means that the object is active, digital, networked, can operate autonomously, is reconfigurable and has a local control of the resources which it needs such as energy, data storage, etc These three main types of system design may also contain sub-systems, sub-parts or components at a lower level of granularity that may also be considered as a smart (e.g., a smart environment device may contain smart sensors and a smart controller, etc). An example of a three main types of UbiCom models is presented in (Fig. 1) [4]. ...
... Smart mobile device can be used for control of static embedded environment devices. Smart device can be used to support the virtual view points of smart personal spaces (physical environment) in a personal space which surrounding the user anywhere [4]. ...
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