=Paper=
{{Paper
|id=Vol-1614/paper_63
|storemode=property
|title=A Model-Based Framework for Adaptive Resource Management in Mobile Augmented Reality System
|pdfUrl=https://ceur-ws.org/Vol-1614/paper_63.pdf
|volume=Vol-1614
|authors=Mykola Tkachuk,Oleksii Vekshyn,Rustam Gamzayev
|dblpUrl=https://dblp.org/rec/conf/icteri/TkachukVG16
}}
==A Model-Based Framework for Adaptive Resource Management in Mobile Augmented Reality System==
https://ceur-ws.org/Vol-1614/paper_63.pdf
A Model-Based Framework for Adaptive Resource
Management in Mobile Augmented Reality System
Mykola Tkachuk 1, Oleksii Vekshyn 1, Rustam Gamzayev 1
1
National Technical University “Kharkiv Polytechnic Institute”,
Frunze str., 21, Kharkov, Ukraine
tka@kpi.kharkov.ua, alexeyvekshin@gmail.com,
rustam.gamzayev@gmail.com
Abstract. A 3-level modeling framework to adaptive resource management in
mobile augmented reality systems (MARS) is proposed, which is based on
comprehensive data structuring and analyzing of their specific hard- and soft-
ware features. At the conceptual modeling level an ontological specification of
MARS resources is constructed, at the logical modeling level a case-based rea-
soning algorithm is elaborated, and as a physical model the reference software
architecture is designed. This approach was successfully tested to solve the task
to adaptive management of image resolution on mobile device (MD) according
to changes of computational loading that finally enabled better video stream
quality in MARS.
Keywords. model, adaptation, resource management, augmented reality, case-
based reasoning.
Key Terms. ModelBasedSoftwareDevelopmentMethodology, Model, Soft-
wareSystem
1 Introduction
Nowadays, mobile information systems become more and more popular. One of the
most complex and dynamically grown type of these systems are mobile augmented
reality systems (MARS) [1]. Such systems require more hardware resources than
standard mobile applications (e.g. social network clients, instant messengers etc.), and
this fact leads to supporting problems of different devices such as mobile phones and
tablets. One of the possible solutions is an execution of complex business logic on the
server side, where computational capabilities are higher than on the mobile client
side; but on the other hand this, could lead to problems with application response time
and energy efficiency because of more intensive usage of wireless networking tech-
nologies. One of the overriding trends in software development is using of complex
systems construction principles, especially cybernetic adaptive control schemes for
software components control including appropriate decision making models and qual-
ity evaluation metrics [2]. These approaches are also useful in case of mobile infor-
ICTERI 2016, Kyiv, Ukraine, June 21-24, 2016
Copyright © 2016 by the paper authors
- 42 -
mation systems development, in particular for MARS. Such systems development
requires effective utilization of restricted mobile device (MD) resources and on the
other hand implementation of complex real-time algorithms.
In this paper we propose an adaptive model-based framework for resource man-
agement in MARS. Additionally a prototype implementation of adaptive MARS is
presented and series of experiments with proposed framework and MARS are provid-
ed. These experiments highlight positive effect of using adaptive approaches in
MARS development.
This paper is structured in the following way: Section 2 depicts briefly some mod-
ern trends in this research domain, with respect to some adaptation issues; Section 3
provides main concept of adaptive model-based framework for resource management
in MARS; Section 4 presents some ARS domain modeling issues like ontological
specifications; in Section 5 the algorithmic model for adaptive resources management
and metrics are given; in Section 6 presents proof of concept, introduces software
prototype and experimental results. Finally, a short outlook on the result achieved and
some future work is presented.
2 Related Work in the Domain of Adaptive Augmented Reality
System Development
An Augmented reality (AR) is a representational form for a real physical environ-
ment, which is extended by adding of computer-generated data [3]. AR registers
physical objects in three dimensions and combines them with the virtual ones. Apart
from virtual reality, AR combines models of objects from real world with additional
information, but virtual reality completely replaces the real world with the virtual one.
Currently AR supports several data sources: two-dimensional markers; data re-
ceived from GPS-modules (Global Positioning System) [4] and from build-in gyro-
scopes. Additionally ARS could use some modern technologies like images recogni-
tion without any markers and GPS data [5].
With respect to supported data source, it is possible to construct some MARS clas-
sification, which is presented in Fig. 1. There are four “top-level” types of MARS;
each of these types has some features and requirements to MD performance. Marker-
based MARS operates with special markers, which stores some data and links to addi-
tional information. Non-marker based MARS are more complex types of these sys-
tems, it based on image recognition algorithms and requires more resources to find
and recognize free-form objects in an input image. Geolocation MARS uses build-in
GPS sensors in order to get information about real environment and augment it with
some virtual data. Finally, infrared-sensors based MARS operates with some infrared
sensors, which is able to detect object and moves in real environment, subsequently
this class of MARS is very useful in entertainment and simulators. In scope of this
research we are focused on Markes-based MARS, because this class of MARS has the
lowest complexity, easy to implement and does not require any additional equipment
apart from MD.
- 43 -
Fig. 1. Classification of MARS types
In terms of software development there are some modern software frameworks to
develop MARS: Metaio Mobile SDK (Software Development Kit) [6], D’Fusion
Mobile [7] and Qualcomm [8].
Nowadays, software adaptation is one of the common trends in modern software
engineering (see, e.g., in [9]), and especially in mobile application development.
There are several approaches to adaptation in mobile systems, some of them are rep-
resented in projects like Q-CAD (QoS and Context Aware Discovery), MADAM
(Mobility and Adap-tation Enabling Middleware), IST-MUSIC (Self-Adapting Appli-
cations for Mobile Users in Ubiquitous Computing Environments) [10], CloudRidAR
(A Cloud-based Architecture for Mobile Augmented Reality) [11] and researches like
Elastic Application Model [12] and resource management in mobile cloud computing
[13].
Q-CAD is a resource discovery framework which enables mobile applications to
dis-cover and to select resources best satisfied the user’s needs. MADAM and ITS-
MUSIC frameworks provide model-driven development approach enabling to assem-
ble applications through a recursive composition process. In this case, variability is
achieved by plugging into the same component type different component's implemen-
tation with similar functional behavior [10]. In [14], a new approach to the composi-
tion of mismatching components in context-aware systems is introduced.
CloudRidAR is a cloud-based framework to MARS development which provides
development facilities to construct MARS using all advantages of cloud computing
and code offloading, but on the other hand this framework forces developer to use
quite complex design solutions (e.g. could computing, workflows, etc) [11].
Elastic Application Model [12] based on code offloading, but flexible application
architecture and models are built only on the server side called “weblets” and MD
hosts only simple client application, which is connected to these weblets. Resource
management in mobile cloud computing [13] addressed to cloud structure, code of-
floading and energy efficiency but not to runtime application adaptation.
To sum up related work investigation we can conclude that none of existing ap-
proached doesn’t provide complete model based framework to adaptive resource
management in MARS, but take into account particular aspects of this problem.
- 44 -
In the Fig. 2 and in the Fig. 3, the interface of marker-based MARS is presented.
This MARS detects source object in the input video stream recognize it, obtain 3D
graphical model of this gear and finally augment source image with this model.
Fig. 2. Not augmented image
Fig. 2 demonstrates interface of the marker-based MARS with not augmented image.
In current state MARS is ready to analyze source image and search for markers.
Fig. 3. Example image, augmented by marker-based MARS
In the Fig. 3 result image is shown, MARS finds marker in the source image (in this
case – gear), obtains related data and augments source image with this data. In this
case, result of augmentation is the gear’s 3D model which is presented over sources
gear image.
Marker-based MARS [15] uses different marker recognition approaches, such AS
QR-codes [16] or barcodes [17]. One of the typical examples of such MARS is QR
Droid application [18].
- 45 -
3 Adaptive Model-based Framework for Resource
Management
Taking into account the results of the provided analysis and based on the understand-
ing of modern trends in the domain of adaptive MARS-development (see Section 2),
we can conclude that it is necessary to elaborate a complex model-based framework
for adaptive resource management in MARS. This assumption is completely corre-
sponded with such well-proved and recognized approaches in modern software devel-
opment as model-driven development (MDD) and model-driven architecture (MDA)
[19]. Last time these issues are already discussed intensively in a lot of publications
about resource management in distributed real-time systems [20], in SOA- and cloud-
centered applications [13, 21, 23], but there is a lack on such work in the domain of
MARS development. That is why we propose to construct such model-based frame-
work in the following way:
to elaborate a domain model specify to describe all hard- and soft-ware resources
to be analyzed and considered in any adaptive procedure in MARS, and such a vi-
sion services as a conceptual level in the proposed framework;
to propose some algorithmic approaches to manage these resources in adaptive
mode, and this vision about the resources in MARS can be considered as a logical
modeling level in our framework;
to develop a reference software architecture to implement a logical model of
MARS with appropriate components and interfaces, and such architecting has to
be recognized as physical modeling level in this framework.
Fig. 4. 3-level modeling framework for MARS adaptation
- 46 -
It is to note that according to this model-based vision about resource management in
MARS for the one and the same domain model a lot of different algorithmic ap-
proaches can be elaborated, and for any such an approach several reference software
architectures might be implemented.
The interacting between these abstraction levels which build the proposed model-
based framework is shown in Fig. 4.
In this way, it is possible to elaborate a lot of knowledge-oriented and reusable al-
gorithm-centered solutions and software components, which support the adaptive
approach to resource management in MARS. More detailed these issues are consid-
ered below.
4 Ontological Specifications for MARS Domain Modeling
Taking into account the 3-level modeling framework for adaptive resource manage-
ment in MARS proposed in previous Section 3, it is needed to elaborate the domain
model for this purpose. This model should represent all relevant hard- and software
capabilities of MD which can be considered as adaptable parameters in an appropriate
algorithmic modeling approach.
Ontology models are widely used to represent relationships between concepts in
some application domain, and they can be applied for different purposes in software
engineering, e.g.:
1. for information sharing between human and machines in Semantic Web applica-
tions [23];
2. for natural language processing, and knowledge engineering,
3. in software product line engineering [24], etc.
For example, [25] represent the domain model including 4 ontologies like User, Ser-
vice, Environment and Device. It describes general relationships that occur during
ARS development. In [26] ontology is used to build platform for educational institu-
tions that could be used for rapid ARS development. In [27] the ontological specifica-
tions are used to connect together the knowledge about the users, environment, and
user aims in the given application domain (museum).
Most of models mentioned above describe different MARS components taking into
account some static resource allocation, and they do not represent system features
needed for adaptive resource management. To close this gap in our approach the new
MARS model was created using the OWL notation, and the OWLGrEd tool is used
for this purpose [28], which provides the UML-like graphical editor. In Fig. 5, the
proposed ontological domain model for MARS is presented.
There are following main entities included in our domain model
1. Augmented Reality Application: is an application that provides a technology to ana-
lyze elements in the real physical environment, and to extend them with additional
virtual objects or with additional information.
- 47 -
2. ACU (Adaptive Control Unit): this is control management element that is responsi-
ble to adapt system to the mobile device. It could use different techniques to opti-
mize application, e.g. to change display resolution.
3. Mobile Device: this is a small device (iPhone, PDA, notebook, tablet etc.), with re-
stricted amount of system resources, which does not have permanent access to the
power sources, and which uses wireless communication technologies. For resource
adaptations, the following device parts (model entities) could be used: Screen – by
changing screen resolution performance of the device could be changed; RAM –
depending on the allocated memory different application modes might be used;
Battery - usually a charged level is used as a main parameter to switch device into
power save mode; modern CPU could change frequency and switch its operation
mode.
4. AR Object Recognizer: is a module to analyze and to extend extracted physical ob-
jects with additional or virtual information.
Fig. 5 Ontological domain model for resource adaptation in MARS
Below this domain model is used to elaborate an algorithmic approach to adaptive
resource management in MARS.
5 Algorithmic Model of Adaptive Resource Management in
MARS
According to the proposed adaptive model-based framework (see Section 3) at its
logical level an algorithmic model to resources management has be constructed with
respect to specific hard- and software characteristics of MD which is used to get client
applications running within a given MARS.
- 48 -
5.1 Formal Definition
In order to formalize the proposed framework to find adaptive solutions for resource
management we can use an algorithmic modeling approach [29], and the appropriate
algorithmic model AM can be defined as the following tuple
AM WorkflowMethods, InfoBase, Metrics , (1)
where Workflow Methods are some algorithms which implement the given methods
Methods , InfoBase is an information base to be used for these methods, and Metrics is
a collection of metrics to assess a quality of adaptation process in mobile ARS. The
choice of a set of adaptation methods in (1) depend on specific features of ARS’s
resources, which should be managed, and one of such possible way will be considered
in the next subsection. A set of metrics in (1) also has to be defined taking into ac-
count the appropriate hard- and software properties of MD which is used in a target
ARS (see e.g. in [10, 15]).
5.2 Case-based reasoning (CBR) within an algorithmic model
Taking into account a complex and weak-formalized character of ARS – functioning,
namely: parallel and multi-threaded calculation processes, turbulence loading on MD,
permanent changes on number of users etc., it is reasonable to use so-called soft cal-
culation methods [30]: neuronal net technologies, fuzzy-logic methods, generic algo-
rithms, case-based reasoning (CBR) and some others. In particular, exactly CBR-
methods can be considered as an effective way to develop decision-making proce-
dures for management of complex software system (see e.g. in [31-33]. According to
this statement the collection of Methods in formula (1) can be specified in the follow-
ing form
Methods NNM , kNNM , kwNNM , (2)
where NNM is a Nearest Neighbor Method, kNNM is a k-Nearest Neighbors Method,
and kwNNM is k-weighted Nearest Neighbors Method [31].
The main idea of all CBR-methods is that any new problem occurred in some ap-
plication domain can be resolved using already existing solution for the similar situa-
tion (called precedent or case). The several CBR differ each other in a search algo-
rithm to find an appropriate precedent in the given case - database. For this purpose, it
is also important to elaborate an adequate description for the precedent’s representa-
tion, which reflects all relevant issues of ARS functionality.
5.3 Information base for CBR-method
Corresponding to formula (1), InfoBase is an information base which is used to apply
the CBR-methods defined in (2). It includes a set of precedents, and any such prece-
dent can be defined in the following way
- 49 -
c p, s , (3)
where p is a vector of parameters to characterize a given problem situation, s is a
vector of parameters to represent an appropriate solution for this problem.
Taking into account the hardware - and software issues of MD which is included in
ARS, vector p can be given as:
p CPU , RAM , BAT, RES, FPS , (4)
where CPU is a current level of processor loading (in %), RAM is a current level of
RAM usage, BAT is a current level of battery charging; RES is a number of possible
screen resolution modes in MD, FPS is a measure of a screen refresh rate.
Vector s in formula (2) can be represented as the tuple
s Width, Hight , (5)
where Width and Height are respectively a width and a height of a video frame on
MD.
5.4 Metrics for ARS resource estimation
In [15] is mentioned that a performance of a ARS-client application is depend on
its screen resolution, and accordingly to this reason a number of frame per second
(FPS) can be used as one of the metrics from its set Metrics defined in formula (1).
This factor is depend on some parameters: on power of MD processor, on size of its
RAM, on screen resolution of MD, and on screen resolution of video-camera.
Therefore, a collection of metrics Metrics in (1) has the following definition
s T , R , (6)
where T is a metric to estimate a number of frame per second, P is a metric to meas-
ure a MD total pRoductivity (R).
A value of metric Т can be calculated using the standard function Count(), namely:
T Count FPS (7)
A value of metric R (named below as a productivity index) defines a MD perfor-
mance ratio, which is dimensionless parameter and it can be defined as following
CPU cur RAM cur BATcur
R wc wr wb (8)
CPU total RAM total BATtotal
where CPUcur is a current MD processor loading ratio (in %); RAMcur is a current
RAM usage ratio (in Kb); BATcur is a current battery charging ratio (in Ah); CPUto-
tal, RAMtotal , BATtotal are respectively the nominal values of the given parameters;
wc, wr, wb are some weighting coefficients for these parameters, and the following
- 50 -
condition must be fulfilled wc wr wb 1 . In this work we have defined the follow-
ing value ranges for the index R: it is critical if R 0.95 ; it is high if 0.6 R 0.95 ; it
is normal if 0.25 R 0.6 ; and it is low if 0 R 0.25 .
Therefore, the metrics defined in formula (6) – (8) allow us to estimate the compu-
tational resources of an appropriate MD which is used in a target ARS with respect to
our final goal: to provide an adaptive recourses management in this ARS.
6 Prove of Concept: Software Prototype and Experimental
Results
6.1 Software prototype design and implementation
In order to prove efficiency of the proposed approach the MARS prototype with inte-
grated ACU has been developed. The main purpose of developed MARS is to recog-
nize marker on a cinema poster, search information about this cinema in an appropri-
ate database and augment source video stream with this additional information at real-
time mode. Such MARS with integrated ACU which analyzes environmental parame-
ters and adopts frame size with respect to MD current state and resources utilization
rate. In the Fig. 6 is presented adaptive MARS functioning algorithm in form of UML
activity diagram.
Fig. 6. Adaptive MARS functioning algorithm
- 51 -
Initial activity in this algorithm is – calculation of a productivity index. If this index is
less than 0.95 (R ≤ 0.95) it is possible to augment data on the MD side, so the next
steps are to define adaptive video stream size, using CBR method, and augment
source video stream with a virtual data on a MD side, and finally – display augmented
video stream to user. If index R > 0.95 it is not possible to augment data on the MD
side and in this case it is necessary to use external resources to augment image from
input video stream (e.g. server in client-server MARS) and show result to user. This
activity could be interrupted by user’s event (pressing exit button).
Presented algorithm takes into account some code offloading possibility (in case of
quite high computation load on a MD), subsequently it is useful to select three-tier
software architecture to implement adaptive MARS prototype. Such architecture al-
lows us to implement recognition component on the server side in order to offload
some logic from MD in case of critical computational load. Another plus point of this
architecture is a possibility to deploy centralized precedent database on the server side
and distribute this precedents among different MDs.
Fig. 7. Adaptive MARS functioning algorithm
- 52 -
In the Fig. 7 presented three-tier component software architecture of MARS in form
of UML deployment diagram. This architecture provides few crucial advantages such
as: high scalability, data processing security, lower resource requirements for clients
MD.
Client MARS application implemented with Android platform [34], using embed-
ded Berkeley DB [35] and OpenCV library [36]. To develop server-side application
PHP programming language [37], Apache web-server [38], MySQL [39] and Mon-
goDB [40] have been selected.
ACU on a MD node (Android Device) contains the following components: Prece-
dent-Storage – local precedent DB, SystemMonitor – the component which provides
data regarding current state of MD and ACU – the component, which implements
CBR methods on the mobile client side. On the server node into adaptation process
are involved the following components: Precedent DB Processor is the accessor
component for centralized Precedents DB, and Data Analyzer is the component that
implements movie's additional data search by input images hash-code.
With respect to the proposed architectural solution, three databases have been im-
plemented: 1) local embedded precedents DB (PrecedentStorage), this DB is used by
ACU; 2) the remote centralized DB (Precedent DB) to store all precedents, all local
DBs are synchronized with this one; 3) movies DB (Movies DB), this DB stores in-
formation about movies, which are handled in MARS prototype. Conceptual data
model of the Precedent DB is presented in the Fig. 8 as the UML class diagram. This
data model takes into account the following entities: Device, Precedent, Param,
ListPrecedent, Platform, TypeParameter. Therefore, it allows handling data, required
by CBR method in our domain.
Fig. 8. Conceptual data model of the Precedent DB
To develop this database we have selected non-relational (NoSQL) database man-
agement system MongoDB [40]. This DBMS provides high-speed data processing
and stores the appropriate information in object-oriented form.
- 53 -
6.2 Estimation results and their analysis
In order to estimate an efficiency of the implemented MARS prototype the experi-
ments have been performed using the following scheme: 1) selection of mobile devic-
es for experiments; 2) precedents DB generation; 3) run MARS prototype with differ-
ent operating modes for ACU (with enabled and disabled ACU).
To test MARS prototype two types of MD were selected, the detailed characteris-
tics of these devices are presented in the Table 1.
Table 1. Technical characteristics of testing MD
Device name Processor RAM Maximal resolution
Nexus 7 Qualcomm APQ8064 2 GB RAM 1920х1080
(1.5 GHz)
Fly IQ4416 MT6572 (1.3 GHz) 512 MB 800х600
RAM
In the Fig. 9 presented example of tuple, which describe particular precedents in-
cluded in the precedents DB.
Fig. 9. Example of precedents tuple in precedents DB
Two series of experiments have been provided with these mobile devices. The first
experiment has been provided with disabled ACU (i.e. without adaptation), and the
second one with enabled ACU. During these experiments the image resolution of MD
screen in case of different values of productivity index (see equation 8) had been
measured. In this experiment we take into account two intervals from normal and high
ranges of the productivity index: 0.4 R 0.6 and 0.6 R 0.8 .The results of these
experiments are presented in Table 2 and Table 3 respectively.
Table 2. Experimental results in case of disabled ACU
Mobile device Resolution T (FPS) for T (FPS) for
0.4 R 0.6 0.6 R 0.8
Nexus 7 640х480 15 13
Nexus 7 800х600 14 10
Fly IQ4416 800х600 10 9
Fly IQ4416 480х320 15 14
- 54 -
The data from the Table 2 show that in case of the fixed image resolution on MD,
and if the value of productivity index R is increased: from the values range [0.4; 0.6]
to the range [0.6; 0.8], then the T(FPS) metric value is decreased, namely, these val-
ues are placed in interval [9, 14]. In other words, the maximum value’s difference
T(FPS) is about 35.7% apart from difference 33,3% for case of [10, 15]
( 0.4 R 0.6 ). The reason of such trend in this experiment is the disabled mode of
ACU.
Table 3. Experimental results in case of enabled ACU
Mobile Resolution T (FPS) Resolution T (FPS)
device 0.4 R 0.6 0.6 R 0.8
Nexus 7 1024х768 11 800x600 13
Nexus 7 800x600 14 640х480 16
Fly 640х480 13 640x480 14
IQ4416
Fly 800х600 9 640x480 13
IQ4416
Table 3 represents experiment results with enabled ACU. ACU component moni-
tors computational load on a mobile device and in case of its increasing correct image
resolution; such correction leads to stabilization of T (FPS) metric: this metric chang-
es only in 18.75%. Additionally, in this experiment value of the maximal difference
for T (FPS) in case of 0.4 R 0.6 is 37.5%. That is why we can make conclusion
that the proposed adaptive resource management approach enables better video
stream quality for mobile device in MARS.
7 Conclusions and Future Work
In this paper we have presented the model-based framework to adaptive resource
management in mobile augmented reality systems (MARS), which is based on the 3-
level data structuring and analyzing of their specific hard- and software features. At
the conceptual modeling level the appropriate ontological specification of MARS
resources was constructed, at the logical modeling level a case-based reasoning ap-
proach is utilized, and as a physical model to implement an adaptive resource man-
agement in MARS the 3-level reference software architecture is elaborated. This ap-
proach was successfully applied in order to solve the task to adaptive management of
screen image resolution on mobile device according to changes of its computational
loading that finally enabled better video stream quality in MARS.
In future we are going to extend a collection of decision search methodologies in
order to improve an adaptation process and compare its efficiency with case-based
reasoning approach implementation. Besides that is it supposed to develop a more
sophisticated adaptive MARS domain model with wider amount of input and output
parameters, which should enable a more configure options in the proposed model-
based adaptive resource management framework.
- 55 -
8 References
1. Lopez, H., Navarro, A., Relano, J.: An Analysis of Augmented Reality Systems. In: Pro-
ceedings of the 2010 Fifth International Multi-conference on Computing in the Global In-
formation Technology (ICCGI '10), pp. 245--250 (2010)
2. Joao W. C., Scott D. M., Kai-Yuan C., Aditya P. M.: Software Cybernetics. Encyclopedia
of Computer Science and Engineering. WILEY (2008).
3. Furth, B.: Handbook of Augmented Reality. Springer-Verlag, New York (2011)
4. Official U.S. Government information about the Global Positioning System (GPS) and re-
lated topics, http://www.gps.gov
5. MacWilliams, A.: Decentralized Adaptive Architecture for Ubiquitous Augmented Reality
Systems. PhD thesis, Institut fur Informatik der Technischen Universitat Munchen (2005)
6. Official Web-site of Metaio Mobile SDK, http://www.metaio.com/software/mobile-sdk/
7. Official Web-site of D’Fusion Mobile project,
http://www.t-immersion.com/products/dfusion-suite/dfusion-mobile
8. Official Web-site of Qualcomm AR SDK,
http://www.qualcomm.com/solutions/augmented-reality
9. Kell, S.: A Survey of Practical Software Adaptation Techniques. In: J. of Universal Com-
puter Science, Vol. 14, pp. 2110--2157 (2008)
10. Kakousis, K., Paspallis, N., Papadopoulos, G. A.: A survey of software adaptation in mo-
bile and ubiquitous computing. In: J. of Enterprise Information Systems, Vol. 4, pp. 355--
389 (2010)
11. Huang, Z., Li, W., Hui, P., Peylo, C.: CloudRidAR: A Cloud-based Architecture for Mo-
bile Augmented Reality. In: Proceedings of MARS ‘14, pp. 29-34 (2014)
12. Zhang, X., Jeong, S., Kunjithapatham A., Gibbs, S.: Towards an elastic application model
for augmenting computing capabilities of mobile platforms. In: Proceedings of 3rd Interna-
tional ICST Conference on Mobile Wireless Middleware, Operating Systems, and Appli-
cations (Mobile-Ware), pp. 161--174 (2010)
13. Ionescu, A.: Resource Management in Mobile Cloud Computing. Informatica Economica,
Vol. 19, pp. 55--66 (2015)
14. Camara, J., Salaun, G., Canal, C.: On run-time behavioral adaptation in context-aware sys-
tems. In Proceedings of M-ADAPT’07 at ECOOP ‘07. pp. 26—34 (2007)
15. Vekshyn O., Tkachuk M. Algorithmic Software Adaptation Approach in Mobile Aug-
mented Reality Systems. In: Proceedings of 7-th International Conference on Software
Engineering Advances, Lisbon, Portugal, pp. 40--43 (2012)
16. Official Web-site of QRCode, http://www.qrcode.com
17. Official Web-site of Barcodes & ID Key Standards, http://www.gs1.org/gsmp/kc/barcodes
18. Official Web-site of QR Droid,
https://play.google.com/store/apps/details?id=la.droid.qr&hl=ru
19. Sommervile, I. Software Engineering. Addison Wesley (2011)
20. Tian He et.al. Feedback Control-based Dynamic Resource Management in Distributed Re-
al-time Systems. In: J. of Systems and Software, vol. 80, pp. 997–1004 (2007)
21. Ghanbari, H. Model-Based Dynamic Resource Management for Service Oriented Clouds.
PhD thesis, York University Toronto, Ontario (2014)
22. Sun, Y. White, J., Eade, S.: A Model-Based System to Automate Cloud Resource Alloca-
tion and Optimization. In: J. of Model-Driven Engineering Languages and Systems, Vol.
8767, p 34 (2014)
23. Fensel, D., Kerrigan, M., and Zaremba M.: Implementing Semantic Web Services: The
SESA Framework. Springer-Verlag Berlin Heidelberg(2008)
- 56 -
24. Tenório T., Dermeval D., Bittencourt I.: On the use of ontology for dynamic reconfiguring
software product line products. In: ICSEA 2014, Proceedings of the 9th International Con-
ference on Software Engineering Advances, pp. 545--550 (2014)
25. Hervas, R., Bravo, J., Fontecha, J., Villarreal, V.: Achieving Adaptive Augmented Reality
through Ontological Context-awareness Applied to AAL Scenarios. In: J. of Universal
Computer Science, vol. 19 (9), pp. 1334--1349 (2013)
26. Chuan-Jun S., Tzu-Ning Y., Yu-Ming Y.: Ontology-based Mobile Augmented Reality for
Personalized U-Campus. In: Proceedings APIEMS 2012, pp.2037--2046 (2012)
27. Hatala, M., Wakkary, R.: Ontology-Based User Modeling in an Augmented Audio Reality
System for Museums. In: J. of User Modeling and User-Adapted Interaction, Vol. 5(3–4),
pp. 339--380 (2005)
28. Bārzdiņš, J., Bārzdiņš, G., Čerāns, K., Liepiņš, R., Sproģis, A.: OWLGrEd: a UML Style
Graphical Notation and Editor for OWL 2. In: Proceedings of 7th International Workshop
“OWL: Experience and Directions”, (2010)
29. Ramesh, K., Karunanidhi, P. Literature Survey on Algorithmic and Non-Algorithmic
Models for Software Development Effort Estimation. In: Int. J. of Engineering and Com-
puter Science, Vol. 2 (Issue 3), pp. 623--632 (2013)
30. Aliev A., Soft Computing and its Applications. World Scientific (2001)
31. Maiden, N., Sutcliffe, A. Case-based Reasoning in Software Engineering. In: IEE Collo-
quium on Case-Based Reasoning, Digest No.036, pp. 1-3, (1993)
32. Limthanmaphon, B., Zhang, Y. Web Service Composition with Case-Based Reasoning.
In: Proceedings of 14th Australian Database Conference (ADC2003), Vol. 17, (2004)
33. Tkachuk, M., Polkovnikov, S., Bronin, S. Adaptive Control Framework for Software
Components: Case-based Reasoning Approach. In: Proceedings of 6th International Work-
shop on Software Cybernetics (IWSC – 2009), Seattle, USA, pp. 47--56 (2009).
34. Official Web-site of Android platform, https://www.android.com/
35. Official Web-site of Oracle Berkeley DB,
http://www.oracle.com/technetwork/database/database-technologies/berkeleydb
36. Official Web-site of OpenCV project, http://www.opencv.org
37. Official Web-site of PHP language, http://www.php.net
38. Official Web-site of Apache project, http://www.apache.org
39. Official Web-site of MySQL project, http://www.mysql.com
40. Official Web-site of MongoDB , http://www.mongodb.org