Journal of Software Engineering and Applications, 2011, 1, 23-36
doi:10.4236/jsea.2011.41004 Published Online January 2011 (http://www.scirp.org/journal/jsea)
Intelligent Management Functionality for
Emergency Medical Applications Based on
Cognitive Networking Principles
George Dimitrakopoulos, Marios Logothetis
Department of Informatics and Telematcis, Harokopion University of Athens, Athens, Greece
Received November 14th, 2010; revised December 21st, 2010; accepted December 28th, 2010.
ABSTRACT
Telecommunications and information technology rapidly migrate towards the Future Internet (FI) era, which is cha-
racterized by powerful and complex network infrastructures, advanced applications, services and content, efficient
power management as well as extensions in the business model. One of the main application areas that find prosper
ground in the FI era, is medicine. In particula r, latest advances in medical sciences are reflected on their capability to
approach previously past-cure diseases, as well as to prevent the appearance and evolution of unpleasant situations.
Those advances are o ften derived from interdisciplinary solution s to complex medical prob lems, supported by commu-
nications and electronics, which target fast, reliable and stable solutions to problems that are demanding in terms of
velocity and accuracy. The goal of this paper is to present intelligent, knowledge-based management functionality ca-
pable of supporting emergency med ical app lications. An indicative em ergency medica l scenario is p rovided, along with
extensive simulation results using the Network Simulator-2 (NS-2), for evaluating the performance of the proposed
functionality.
Keywords: Future Inte rnet (FI), Intelligent, Cognitive management, Electronic Medicine, Network Simulator-2 (NS-2)
1. Introduction
Over the last years, information and communication
technology (ICT) have been lying at the forefront of in-
ternational research and development efforts, aiming at
the provision of innovative services and applications, tai-
lored to user needs [1,2 ]. Latest trends refer to the migra-
tion of ICT towards the era of the “Future Internet” (FI)
[3], which envisages 1) powerful networ k infrastructu res,
2) the potential to provide multiple, advanced applica-
tions, services and conten t, by exploitin g the po werful in-
frastructure, 3) “green” infrastructures in terms of effici-
ent energy usage and 4) advances in the business model,
socio-economics and security. The FI era promises easier
overcoming of the structural limitations of telecommuni-
cation infrastructures and their management systems, thr-
ough the adoption of cognitive networking principles
[4-7], which further facilitate the design, development and
integration of novel services and applications.
An area of applications where telecommunications and
information tech nology find pro sper ground in th e FI era,
is medicine. This is justified from the fact that the world
of medical sciences has been undergoing an unparalleled
evolution during the last years, th is being reflected on the
continuous development and enhancement of various so-
lutions to medical problems. In this respect, as an out-
come of worldwide research attempts, novel methods
have been identified, which allow the diagnosis, treat-
ment, even prevention of numerous, previously incurable,
diseases. Moreover, the unstoppable development of e-
health products and applications can significantly im-
prove the right of access to quality medicine, regardless
of their personal condition and geographical location, all-
owing the selection of the appropriate health resource
from anywhere at any time [8-11]. This is especially ap-
plicable in emergency situations, where timely retrieval
of the necessary information might be of extreme impor-
tance.
Such advances have been facilitated through interdis-
ciplinary research and development strategies. This refers
mainly to the exploitation of recent findings in telecom-
munications and electronics, which cater for the devel-
opment of complete systems that can really improve the
patient’s quality of life and reduce medical errors and

Intelligent Management Functionality for Emergency Medical Applications Based on Cognitive Networking Principles 24
costs. Indicatively, concepts such as the ubiquitous pro-
vision of applications at increasingly high bit rates, have
paved the way for several innovative medical services
and applications [10-11]. Additionally, the advent of the
FI era [3] along with novelties in the network manage-
ment domain through the exploitation of past interactions
with the environment, in taking future decisions [12-13],
is expected to facilitate several medical approaches, such
as patient management technologies in telemedicine and
remote diagnostics.
In this respect, the goal of this paper is to discuss on
how wireless communication systems, enhanced with the
advantages of cognitive networking principles, can serve
as enablers for providing e fficient and dependable, emer-
gency medical applications, in the FI era. Several res-
earch attempts have been made in the past in the area of
medical applications supported by wireless communica-
tions, without considering cognitive networking aspects.
Some preliminary ideas on modeling quality of service
(QoS) levels in e-health multimedia data are given in
[14]. Bandwidth allocation for e-health applications and
bandwidth aggregation is addressed in [15-16] to ensure
their QoS guarantees. However, these schemes do not
consider bandwidth aggregation over multiple wireless
networks. Moreover, a discussion on how alternative te-
chnologies can support the QoS requirements of e-health
applications is provided in [17-18], although no reference
to integration of those alternative technologies is made.
The paper builds on the aforementioned proposals by
presenting novel “Intelligent management functionality
for emergency medical applications based on cognitive
networking principles—i-MED”.
The contribution of this work is manifold:
First, the paper proposes intelligent management func-
tionality that can aggregate and utilize knowledge and
experience (thus exploit cognitive networking rules), so
as to be able to quickly and efficiently respond to emer-
gency contextual situations.
Second, the proposed functionality can provide satis-
factory levels of abstraction during the provision of effi-
cient and dependable medical services.
Third, the functionality contributes to the satisfaction
of both, medical and patient requirements in terms of ac-
curacy, reliability, unobtrusiveness and user acceptance,
during critical situations.
The paper is structured as follows. First, the motiva-
tion for this work is presented, through 1) an outlook on
the current wireless communications landscape, and 2) a
description of an indicative emergency medical scenario
that serves as a trigger for this work. Second, the paper
describes the “i-MED” functionality, first at a high level
view, and then in detail. Then, indicative simulation re-
sults using the Network Simulator 2 (NS-2) platform [19],
are provided, for showcasing the performance of the pro-
posed functionality. The paper concludes with summari-
zing remarks and some aspects for future reference.
2. Motivation
2.1. The FI Era, Cognitive Management and
Analogies with Medicine
As mentioned above, the unstoppable research in tele-
communications envisages their rapid migration towards
the FI era. The FI era is associated with several require-
ments, which include the increased interest for wireless
systems, the turning towards the provision of diversified
applications, social networking, as well as the “prosu-
mer” concept. Moreover, there is need for efficiency in
resource provision (utilization , “green”, costs) and for re-
solving potential infrastructure problems: congestion si-
tuations, expand coverage when/where needed, efficien-
tly offer localized applications and content [3].
From a technological perspective, in the FI era, wire-
less systems are regarded as systems having high-level
structures with various system components, as illustrated
in Figure 1. A Composite Wireless Network (CWN) re-
presents a set of radio networks, which is operated by a
Network Operator (NO) using a common network man-
agement system. Part of a CWN may be cognitive and
can be managed by the NO, under a centralized cognitiv e
management system. On the other hand, the Cognitive
Control Network (CCN) enables the establishment o f ad-
hoc and mesh networks, catering for higher resource uti-
lization, green decisions, lower costs, while it can also
exploit autonomic capabilities.
In particular, regarding management, the objective of
the FI management functionalities is to find the optimal
network configuration of the managed segments, in order
to serve the requests that they face. Essentially, they
should produce service offers in response to the faced de-
mand. In this respect, cognitive systems may be promising
for facilitating the design, development and integration of
novel services and applications in the FI era. In general, “a
cognitive system is capable of retaining knowledge from
past interaction s with the external en vironment and decide
upon its future behavior based 1) on this knowledge, 2)
other goals and also 3) policies, so as optimize its perfor-
mance” [12,20]. The operation of cognitive systems, called
“cognitive cycle” is depicted on Figure 2(a).
An area of applications where cognitive systems could
find prosper ground is electronic medicine (e-health). In
this respect, Figure 2(b) shows the usual diagnostic-treat-
ment cycle followed in the course of a medical incident
handling. The whole cycle consists in an interacttion be-
tween the patient domain and the doctor domain. The do-
ctor collects contextual information on the patient. This

Intelligent Management Functionality for Emergency Medical Applications Based on Cognitive Networking Principles 25
Figure 1. Realizing the FI world by means of cognitive systems.
(a)
(b)
Figure 2. (a) The cognitive cycle in the case of wireless networks; (b) The diagnostic-treatment cycle: A cognitive-based ap-
proach.

Intelligent Management Functionality for Emergency Medical Applications Based on Cognitive Networking Principles 26
data, together with the patient’s historical data, constitu te
the information to be analyzed by the doctor. The analy-
sis results in the doctor’s decision upon the most appro-
priate treatment manner to be applied to the patient in
question. During the decision making process, the doctor
considers specific goals and policies, as well as past kn-
owledge and experience, which is derived from previous
interactions with patients with similar incidents. So, the
whole process can be reflected on a closed loop.
The diagnostic—treatment cycle finds several analo-
gies with cognitive wireless networks and systems, as
identified above, this being the basic motivation for this
work. In particular, the doctor could be significantly faci-
litated by a syste m (management function ality) that keeps
track of past actions, stores information on a “knowledge
database” and provides this information as an input to the
doctor, prior to decision making. At another level, cogni-
tive management functionality may cater for fast and eff-
ective adaptation s of the communication infrastructure to
changing requirements, and thus guarantee unobtrusive
communication during critical situations. This is exactly
the focus of the paper.
In this respect, the next section presents an indicative
scenario that shows how novel management function ality,
enhanced with cognitive network ing capabilities, may be
needed for the provision of a medical application with
faster transmissions, as well as higher reliability and
availability.
2.2. Indicative Emergency Medical Scenario
This section aims at exemplifying the way in which cog-
nitive management functionality can support a medical
application, through an indicative emergency medical
scenario, depicted on Figure 3.
The scenario begins with an ambulance having col-
lected a patient from a place of an incident. No informa-
tion is considered prior to this point (e.g. the call for an
ambulance received by a telephone operator of the am-
bulance service). En-route towards the District General
Hospital (DGH), pre-hospital emergency care is provided
to the patient (e.g. basic/advanced life support), whereas
also medical information is collected (e.g. bio signals,
vital signs or medical DICOM images [21]) using the
appropriate electronic instruments of the ambulance, and
transmitted to the DGH through the utilization of ontolo -
gies, as defined by HL7 standard [22]. This information
needs to be perfectly accurate, i.e. with the lowest possi-
ble level of packet loss, as well as of high quality, so as
to be considered reliable for assessing the patient’s con-
dition.
As soon as the info rmation r eaches the DGH, it is us ed
to potentially retrieve the patient’s pa st medical data arc-
hived in a Medical Data Repository (MDR). The pa-
tient’s medical condition, as assessed and reported by the
ambulance paramedics and the ambulance service staff,
in conjunction with his/h er medical data, is used for spe-
cifying the degree of urgency, as well as the potentially
additional necessary medical procedures/measurement to
be performed on the patient by the ambulance parame-
dics. Additionally, the MDR comprises also a Reference
Context Repository (RCR), which states how similar in-
cidents were tackled in the past. In this respect, the most
appropriate doctor for the case is selected, according to
the type of case, the case’s degree of urgency and the
DGH’s availability, workload balance and suitability for
the case. For example, in the case of a heart-related inci-
dent, a cardiologist needs to be immediately notified,
whereas also the equipment of the relevant lab needs to
be in order. This information is also communicated to the
ambulance staff. Finally, on ambulance arrival at the
emergency department of the DGH, the delivery of in-
hospital emergency medicine begins, while the case’s
data recorded by the ambulance service staff is made
available to the emergency department’s doctors. Emer-
gency case’s past medical data can also be made availa-
ble to the emergency department’s doctors on demand by
accessing the health district’s medical data repository at
the DGH.
During such an emergency scenario, it is of utmost
importance that information is transmitted at the most
dependable manner. This is considerably difficult since
the ambulance is constantly changing locations inside a
versatile telecommunicatio ns environment. Consequen tly,
intelligent management functionality is needed, for sup-
porting the scenario. The enhancement of the functional-
ity with knowledge and experience constitutes a funda-
mental step forward for increasing its efficiency and de-
pendability.
2.3. I-MED High-Level View
In the light of the above, th e cogn itive manage ment func-
tionality for emergency medical applications (“i-MED”)
aims at providing a seamless connectivity during such
critical situations, while th e ambulance is moving fast to-
wards the DGH, as well as at building the basis for effi-
cient emergency handling. To do so, it uses a set of inputs,
utilizes optimization algorithms (that have been propo sed
and extensively analyzed in [23-26]) and provides an
output consisting in the selection of the most ap- propri-
ate (reliable) network operating parameters (wireless ac-
cess systems and/or spectrum, power levels, bit rates for
operation), so as to guarantee a sea mless communication,
which is of high significance. Furthermore, in o rder to b e
facilitated in making decisions, i-MED keeps track of
past actions, so as to learn fromtheir implications, as dis-
cussed also in [27]. This is repeated in a machine learning

Intelligent Management Functionality for Emergency Medical Applications Based on Cognitive Networking Principles 27
Figure 3. Emergency medical scenario.
process [20] that leads to cognition. In this respect, it
reaches the appropriate decisions faster and even more
efficiently, as will be shown herein. Last, through the
cognitive process, i-MED is made even ready to proac-
tively act in future relevant in- cidents, being able to pre-
dict the potential evolution of an incident, which might
be of critical importance.
I-MED reflects a fully-distributed functionality, i.e., it
is located on one hand inside the ambulance domain,
aiming at the adaptation to the changing environment
during the ambulance’s heading towards the DGH, and
on the other hand in the infrastructure domain, i.e., 1) in
the DGH for the realization of the communication, and 2)
in the Network Operator (NO) premises, enabling the NO
to adapt the infrastructure on the basis of an emergency
application. Moreover, it should be noted that the focus
of i-MED is the utilization of cognitiv e management fun-
ctionality, where the explo itation of knowledge helps the
functionality reach faster, more stable and more reliable
decisions. Moving one step further, cognition may also
form part of the application itself, predicting the needs of
the patient upon the arrival at the DGH. A high level
view of the “i-MED” functionality is prov ided on Figure
4, while the next section presents it in detail.
3. I-MED Detailed Description
This section describes the “i-MED” functionality in de-
tail, presenting its input, outpu t and decision making me-
thod.
In general, as shown on Figure 4, the “i-MED” input
includes information on the anticipated context, the in-
frastructure/ambulance profiles, as well as the policies
imposed.
Context Acquisition. This functional block is respon-
sible for monitoring the managed reconfigurable network
elements/segments and provide any available information

Intelligent Management Functionality for Emergency Medical Applications Based on Cognitive Networking Principles 28
Figure 4. I-MED overview.
regarding the whole traffic in the network in terms of
number of hosts, active sessions as well as information
on the equipment that the sessions are initiated (reconfi-
gurable wireless terminals), including the ambulance in
question. Moreover, it is responsible to provide the ser-
vice or application type (in general: entertainment, public,
industrial administration, commercial, energy manage-
ment, security, safety, educational, financial, etc.; in our
case: medical) of the active sessions in the network as
well as their priorities. In order to evaluate the current
status of the network, several Key Performance Indica-
tors (KPIs) need to be defined, mostly in terms of effi-
cient network resources exploitation (delay, packet error
rate, etc.), as will explained in the sequel, based on the
sessions’ service types and priorities. The main point for
the operation of i-MED is the fact that medical applica-
tions appear to have a significantly high priority level
compared to others (for accuracy, velocity and security
reasons), as will be discussed in detail below.
Profile, goals and policies derivation. This functional
block provides information on 1) the QoS levels that
each user is willing to pay for each service type (the me-
dical service appears on top of this list), 2) the security
levels for each service type (the medical service is asso-
ciated with higher security levels), 3) the ambulance’s
radio capabilities and 4) whether new software download
is necessary in order the requested functionalities to be
supported. Moreover, this block includes also informa-
tion on the RAT profiles, which is necessary in order to
include information regarding the capabilities (capacity,
range, delay, supported service types, operational cost,
security levels, power efficiency etc) of the available
RATs.
In addition, this functio nal block is essential for giving
to a NO the potential to attribute high priorities to spe-
cific service types, one of which is an emergency medi-
cal service. It is quite easy to understand that service ty-
pes like medicine oriented applications are high priority
service types while the priorities of entertainment or
commercial service types are the minimum especially
when the traffic load is high. This can be efficiently ex-
ploited when taking decisions. Moreover, this functional
block provides to a NO several options to configure 1)
the maximum allowed QoS level per application depen-
ding on the traffic conditions, 2) billing functionalities
based on the service types and the corresponding QoS
levels (attributing the appropriate importance will make
allocation more fair) and 3) the end-to-end domains in-
volved.
Knowledge Base. All learning functionalities involved
in i-MED are included in the knowledge base functional
block. This block is responsible for 1) learning the most
appropriate solutions regarding the functionalities or ca-
pabilities of the available services and /or applications, 2)
learning the problems characteristics addressed in the past
(e.g. past emergency situations) as well as their solutions
provided after the optimization procedures, 3) being ca-
pable to identify whether a context or a problem has al-
ready captured or addressed in the past and provide the
solution skipping optimization procedures and 4) keeping
information regarding contexts addressed in the past [24].
Through using this information the system is 1) capable
to predict future traffic conditions based on transition
probabilities, 2) apply a priory the corresponding opti-

Intelligent Management Functionality for Emergency Medical Applications Based on Cognitive Networking Principles 29
mum configurations, as well as 3) predict the necessities
of the patient upon the arrival at the DGH, based on tack-
ling previo us s imilar incide nt s.
Decision Making. This is the functional block where
optimization takes place, considering the input from all
the aforementioned blocks. Several algorithms can be
employ ed regarding the followi ng:
 Cross layer optimization. It includes reconfiguration
actions for the majority of the protocol stack layers
(data-link, network, transport and application). Ba-
sed on the requested services the appropriate proto-
cols are selected in order to increase the overall sys-
tem performance while keeping the QoS levels the
highest possible [23], which is extremely important
in medical applications.
 Protocol parameters configuration. The optimum
configuration of the protocol parameters are decided
after the optimum protocol selection in order to gua-
rantee the best possible network performance [25,
26].
 Software components optimum allocation. Several
software components like services or applications
should be efficiently allocated to the network enti-
ties (servers, proxies, routers etc.) of the system
based on the traffic conditions and the demand for
these software components [25,26].
 Service priorities optimization. Given that the prior-
ities of the sessions’ service types are available, they
can be considered during the optimization process,
so as to decide a) when the medical session will be
served and b) what is the appropriate QoS level for
the medical session.
 Context identification procedure. This procedure is
responsible to identify whether a similar incident
has already been addressed in the past and provide
the corresponding solution skipping optimization
procedures. In case that there is no identification,
the optimization procedures will be triggered [24].
It should be noted that distributed context acquisition,
and decision making at various degrees of distribution is
enabled by the p roposed architecture of the function ality,
i.e., either in a collaborativ e manner under a NO surveil-
lance, or autonomously on behalf of the ambulance. In
general, the aforementioned components cooperate with
each other, so as to generate knowledge from various
sources and result in useful directives towards the am-
bulance and the infrastructure. The way this can be rea-
lized is the subject of the next section.
4. Simulation Setup
This section describes the way in which simulations have
been prepared, in order to validate the performance of
i-MED against conventional reconfiguration techniques.
First some general parameters of the simulation envi-
ronment are presented. Second, some preliminary input
data describing the emergency application considered (in
accordance with the necessary i-MED input as described
above), are provided. Finally, specific QoS metrics are
presented in order to evaluate the proposed function a lity.
4.1. Preliminary Data
The topology comprises a Flexible Base Station (FBS), i.e.,
a base station with 3 reconfigurable transceivers, each of
which may operate with 2 wireless access systems in al-
ternative, i.e., either in UMTS or in WiMAX mode. A
total set o f 20 terminals are served by this FBS. The range
of UMTS is supposed to be 1000 m, while the range of
WiMAX is taken equal to 750 m. The propagation model
is set to Free Space, without effects fro m the environment,
and the terrain pr ofile is flat. Ta ble 1 summarize s the gen-
eral input parameters of the considered simulation envi-
ronment. Last but not least, a set of applications are consi-
dered to be offered to the terminals through the FBS and
further analyzed in the following sect ion.
Moreover, results are obtained through simulations
performed in NS-2 and run on a Pentium-4 3.0 GHz with
1.5 GB of RAM and a 32-bit operating system. NS-2 is a
discrete event, standard simulation tool, which allows the
accurate design and study of communication networks,
devices, protocols and applications [19]. By providing
the necessary input to NS-2, we can extract valuable re-
sults on the way in which i-MED can support an emer-
gency medicine application. The simulation time was 60
minutes, whereas the results are depicted at the time
scale of 1 min (exactly when the optimization and the
adaptation occur).
3 scenarios are used for evaluating i-MED performance,
mostly by showcasing the benefits of cognitive manage-
ment functionality against conventional reconfiguration
techniques during emergency situations. For the infras-
tructure-initiated scenario (scen ario 1), the alg orithms ap-
plied in the context of i-MED are the ones proposed in
[23] (pure reconfiguration algorithm) and [24] (cognitive
management algorithm). On the other hand, for the ter-
minal (ambulance)—initiated scen ario scen ario 2), the al-
gorithms proposed in [25] (cognitive terminal manage-
ment) and [26] (reconfigurable terminal management) are
recruited. Last, scenario 3 aims at exhibiting i-MED’s per-
Table 1. General parameters of the simulation environment.
Area 1250 × 1250 m
Simulation Time 60 min
Physical Characteristics UMTS, Wimax
Physical Layer Data Rate (Max) UMTS: 2 Mbps
Wimax: 54 Mbps
Reception Power Threshold –95 dbm

Intelligent Management Functionality for Emergency Medical Applications Based on Cognitive Networking Principles 30
formance in terms of proactively identifying appropriate
ways to tackle an incident using context matching algo-
rithms [24].
4.2. Application Aspects—Medical Sources Input
Typical medical information is associated with the trans-
mission of vital signs, such as electro-cardiogram (ECG),
blood pressure and glucose, temperature and blood flow.
Moreover, it includes live video, medical images, X-ray
and voice communication between the ambulance and the
DGH. A significant characteristic of medical data is the
diversity in the period of transmission time. Hence it is
possible to separate them in periodical and sp oradic data.
Periodic data may comprise vital signs such as ECG or
heart rate. On the other hand, sporadic data are sent in an
emergency situation when the patient’s condition deterio-
rates, a case in which it may consist of high-band-width
images and video.
Furthermore the services provided to users in the cov-
erage area, include VoIP (provided to 15 users) and In-
ternet Browsing (provided to 4 users). Internet browsing
is modelled as a so called “Internet light Browsing”. In
particular, it uses HTTP protocol to download a web page
of size equal to 500 bytes, plus an embedded, 5 small
images of 250 bytes. Other important properties for the
browsing application are listed in Table 2. The VoIP
application is based on the G.711 [28] voice encoding
scheme for both the caller and the callee. Further values
for the attributes of the modelled VoIP application are
given also in Table 2.
Moreover, the ambulance is assumed to be moving
towards the DGH, according to the indicative scenario
presented in Subsection 2.2. Two types of applications
are considered for the ambulance depending on the de-
mand in bandwidth. The services provided to the ambul-
ance are image transfer and video conferencing for diag-
nosis purposes. Further details are given in Table 2.
4.3. QoS Metrics
As also stated in the introductory sections, the cognitive
management proposed functionality is going to provide
seamless connectivity during critical situations. Accor-
dingly, in each of the scenarios we focus on specific QoS
metrics, which are used to evaluate conditions and assist
in coming up with useful results with respect to the pro-
posed functionality. Particularly, in this simulatio n study,
quality of service evaluation is carried out by the follow-
ing performance metrics:
1) Delay (sec): which is the one way, end to end delay
of data packets from the sending to the receiving node. It
includes: a) processing delays e.g. voice packet compres-
sion/decompression, packetization etc. b) queuing and
medium access delays in the AP as well as in the inter-
mediate nodes, c) TRx delay of the AP and the interme
diate nodes and d) the propagation delay for each conec-
tion between the AP an d the dest i nat i o n no d e.
2) Data Dropped (Kbps): which is defined as the rate
at which data is dropped due to full higher layer data bu-
ffers or because of too many retransmission attempts.
3) Throughput (Kbps): which corresponds to the total
data traffic in bits per sec, successfully received by the
destination excluding packets for other destination MACs,
duplicate and incomplete frames.
4) Capacity (Mbps): which represents the total load in
bits per sec in the simulated network.
All the above metrics are averaged to the set of termi-
nals in the simulated network. Moreover, Table 3 sum-
marizes the requirements for specific QoS metrics and for
each of the considered applications. This table will be
used as a reference throughout the rest of this paper and
will assist in extracting conclusions from the derived sta-
tistics.
5. Results and Discussion
This section showcases the efficiency of the proposed
functionality, through using 3 scenarios, as mentioned
above.
Table 2. Traffic input parameters.
Browsing
Light, Protocol Version: Http 1.1
Page Interval Time: Exponential 720 sec
Pages Per Server: Exponential 10
Page prope-
ties:
1 constant part : 500 bytes
5 Small Images : 250 bytes each
VoIP
Encoder Scheme G.711 (silence)
Compression/Decompression delay 0.02 sec
1 voice frame per packet
Incoming and outcoming Conversation
environment: Land Phone – Quiet room
No control signalling (e.g. for setup/release)
included
Video
Conferencing
High Resolution Video: 704x480 pixels,
24 bits/pixel
30 frames/sec
Image
Transfer
Inter-Request Time: 10 sec
File Size: 500.000bytes
Table 3. QoS requirements per application type.
ApplicationsTechnology QoS Metrics
Delay
(ms) Jitter (ms) Bit Rate (Kbps) Packet Loss
Image
Transfer Non real time and AsymmetricMedium N/A < 300%
Video
Conferencing Real Time and Symmetric< 150 < 400 < 2Mbps< 1%

Intelligent Management Functionality for Emergency Medical Applications Based on Cognitive Networking Principles 31
Figure 5. Service area.
Figure 6. Optimization and adaptation time.
5.1. Scenario 1: Initiation from the Infrastruture
The scenario begins with the terminal (ambulance) that
moves inside the service area of Figure 5, being pro-
vided a Video Conference service through UMTS. At
some point, the “Context Acquisition” functional block
of i-MED at the infrastructure domain (at the NO pre-
mises) identifies (through continuous monito ring of KPIs,
such as delay, packet error rate, etc.) that there is need
for guaranteeing a higher quality communication, due to
an emergency medical situation. To do so, the “Decision
Making” functional block of i-MED decides that a re-
configurable transceiv er of the base station should switch
to WiMAX, through the process described in [23], ex-
ploiting also the information from the “Profiles Man-
agement” and the “Policies Derivation” functional blocks.
Moreover, if a similar incident has been confronted also
in the past, i-MED will identify the appropriate solution
through the “Knowledge Base” functional block faster,
using algorithms such as the one proposed in [24].
In general, the whole process as depicted in Figure 6
is associated with an amount of optimization time (run-
ning time of the algorithm) and an additional amount of
adaptation time (SDR adaptation time). We assume that
the optimization time starts at the Topt_start point and
finishes at the Topt_end point. Then, i-MED should com-
mand the change of the operating wireless access system
of the base station transceiver and inform the ambulance
(and potentially other terminals, also), accordingly. This
additional process starts at the Tadapt_start point and
ends at the Tadapt_end point. As shown on Figure 7, the
optimization time needed without the help of I-med, i.e.,
exploiting only the reconfiguration algorithm ([23]), is
about 10.1 sec, which is comparatively larger (about
500%) than the optimization time with the existence of
i-MED (about 1.5 sec). This can be justified from the fact
that i-MED exploits past knowledge and experience to
reach the appropriate decision faster (with the use of
cognitive algorithms such as the one described in [24]),
which is of high importance in critical situations. This is
achieved by completely avoiding running of the reconfi-
guration algorithms ([23]) and thus reducing the overall
optimization time.
Moreover, Figure 8(a) and Figure 8(b) depict the av-
erage capacity (in Mbps) of the network segment (service
area) during the simulation time for Video conference
application. It is obvious that the averag e capacity is sig-
nificantly increased with the choice of activating Wi-
MAX in a transceiver. However, the i-MED gain lies
in that the increased capacity is reached significantly
faster (the curve in Figure 8(a) is shifted towards the

Intelligent Management Functionality for Emergency Medical Applications Based on Cognitive Networking Principles 32
Av. Optimization Time
0
2
4
6
8
10
12
legacy i-Med
Av. Topt = Topt_end - Topt_start ( sec)
Figure 7. I-MED optimization time gain.
left), through the aforementioned decrease in the optimi-
zation time. Moreover, this contributes also to achiev-
ing a higher overall average capacity during the whole
simulation, as shown on Figure 8(b). Furthermore, Fig-
ure 9(a) depicts the average capacity in case of image
transfer through the network. As can be observed this
application seems not to occur a prohibitive state for the
network, due to the fact that the demand in bandwidth is
low than the previous application. However as also de-
picted in Figure 9(b), image transfer application achi-
eves a higher overall capacity in cell in case of i-MED
functionality.
Finally, Figure 10(a) and Figure 10(b) depict the av-
0
2
4
6
8
10
12
14
16
18
051015 20 25 30 35 4045 50 55
Time (sec)
Av. Capa c i t y (M bps )
legacy
i-Med
Topt_start
Tadapt_end
(legacy)
Tadapt_end
(
i-med
)
(a)
0
1
2
3
4
5
6
7
8
9
10
legacy i-Med
Av. Capaci ty ( Mbps)
(b)
Figure 8. (a) Av. Capacity vs time; (b) Av. Capacity without
(legacy) and with i-MED, for Video Conference Applica-
tion.
0
1
2
3
4
5
6
7
8
9
10
0510 15 20 25 30 35 40 45 50 55
Time (sec)
Av. Capacity (Mbps)
legacy
i-Med
Topt_start
Tadapt_end
(
i- med
)
Tadapt_end
( legacy)
(a)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
legacy i-Med
Av. Ca pacity (Mb ps )
(b)
Figure 9. (a) Av. capacity vs time; (b) Av. capacity without
(legacy) and with i-MED, for image transfer application.
erage data dropped (in Kbps) in the network segment
during the simulation time for Video conference applica-
tion. As can be observed in Figure 10(a), at the time
when the optimization begins, the average data dropped
increases, until the end of optimization. In this respect,
since the optimization time is significantly decreased
through i-MED (as explained above), the average data
dropped becomes also decreased, accordingly. All in all,
considering the whole simulation time, i-MED contri-
butes to a total decrease of the average data dropped, as
also shown on Figure 10(b). The same situation appears
also in Figures 11(a),(b) for image transfer application,
in which the average data dropped in the network for the
whole simulated time is decreasing.
5.2. Scenario 2: Initiation from the Ambulance
This scenario considers the same as above input, being
differentiated only by the fact that i-MED is now placed
inside the ambulance (terminal). Again, the “Context
Acquisition” functional block of i-MED (at the ambul-
ance domain) identifies the necessity to better guarantee
a seamless data transfer with the DGH (through the video
conferencing service). Considering also the necessary
information from the “Profiles Management” and the

Intelligent Management Functionality for Emergency Medical Applications Based on Cognitive Networking Principles 33
0
20
40
60
80
100
120
140
0510 15 2025 3035 40 45 50 55
Time (sec)
Av. Drop Packets (Kbps)
legacy
i-Med
Topt_start Tadapt_end
(legacy)
Tadapt_end
(
i-med
)
(a)
0
10
20
30
40
50
60
legacy i-Med
Av. Drop Packe t s ( Kbps)
(b)
Figure 10. (a) Av. Data Dropped vs time; (b) Av. Data.
0
20
40
60
80
100
120
140
05101520 25 30 35 40 45 50 55
Time (sec)
Av. Drop Packet s ( Kbps)
legacy
i-Med
Topt_start Tadapt_end
(legacy)
Tadapt_end
(
i-med
)
(a)
0
2
4
6
8
10
12
14
16
18
20
legacy i-Med
Av. Drop Packet s ( Kbps)
(b)
Figure 11. (a) Av. Data Dropped vs time; (b) Av. Data
Dropped without (legacy ) and with i-MED for Image trans-
fer Application.
0
0.2
0.4
0.6
0.8
1
1.2
0510 15 20 2530 35 4045 50 55
T ime (sec)
Av. Downl ink Throughp ut ( Mbps )
legacy
i-Med
Topt_start
Tadapt_end
(
i-med
)
Tadapt_end
(legacy)
(a)
0
0.1
0.2
0.3
0.4
0.5
0.6
legacy i-Med
Av. Downli nk Thr oughput ( M bps)
(b)
Figure 12. (a) Av. Downlink Throughput vs time; (b) Av.
Throughput without (legacy) and with i-MED for Video
conference application.
“Policies Derivation” functional blocks, the “Decision
Making” functional block decides that the ambulance
transceiver should switch from UMTS to WiMAX, em-
ploying reconfiguration algorithms, such as the one de-
scribed in [26]. Moreover, if a similar incident has been
confronted also in the p ast, i-MED will identify the most
appropriate solution through the “Knowledge Base”
functional block faster, using cognitive management al-
gorithms, such as the one proposed in [25].
Figure 12 depicts the average downlink throughput of
the ambulance (in Mbps), for Vi deo conference, duri ng t he
simulation time in the legacy (pure reconfiguration) case,
as well as in the i-MED existence case. In general, the ave-
rage downlink throughput depends on the wireless access
system, packet size, as well as the time interval between
packets. However, as can be clearly observed in Figure
12(a), the throughput increases in conjunction with the
switching from UMTS to WiMAX. Moreover, the in-
creased throughput is reached earlier in the case that i-
MED utilizes past information, since it avoids running the
reconfiguration algorithms ([26]) and identifies the most
appropriate solution (switching to WiMAX) faster. There-
fore, I-med contributes also to achieving an increase in the
overall average downlink throughput, compared to the le-
gacy case, as shown on Figure 12(b). Furthermore Fig-
ures 13((a),(b)) depict the average downlink through put

Intelligent Management Functionality for Emergency Medical Applications Based on Cognitive Networking Principles 34
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0510 15 20 2530 35 4045 50 55
Time (sec)
Av. Downl ink Throughp ut ( Mbps )
legacy
i-Med
Topt_start
Tadapt_end
(
i- med
)
Tadapt_end
(legacy)
(a)
0
0.05
0.1
0.15
0.2
0.25
0.3
legacy i-Med
Av. Downl ink Th r oughp ut ( M bps )
(b)
Figure 13. (a) Av. Downlink Throughput vs time; (b) Av.
Downlink Throughput w ithout (legacy) and with i-MED for
Image transfer application.
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
0510 15 20 25 30 35 4045 50 55
Time (sec )
Appropriat eness Achieved
legacy
i-Med
Topt_start
z
Tadapt_end
(i-med )
Tadapt_end
(legacy)
(a)
0
0.5
1
1.5
2
2.5
3
3.5
legacyi-Med
Networ k Changes
(b)
Figure 14. (a) Appropriateness Achieved vs time; (b) Net-
work Changes without (legacy) and with i-MED.
but this time for Image transfer application. As can be ob-
served this application has the same tendency as the pre-
vious one and generally seems also to achieve with i-MED
functionality an overall increase.
Last, Figure 14(a) depicts the degree of suitability
(appropriateness) of the RAT, which the ambulance rates
when it is inside the network [25,26]. Appropriateness
comes also in line with the number of changes (hand
overs) imposed without and with i-MED, shown on Fig-
ure 14(b). The only handover that is necessary occurs
when i-MED indicate that the actual capabilities of the
second network can guarantee a higher degree of reliabil-
ity and dependability, which is of crucial importance
when it comes to a demanding application, as in our case
the medical one.
5.3. Scenario 3: Prediction of Potential Incident’s
Necessities through Context Matching
As described also in the indicative emergency medical
scenario, i-MED is capable of identifying the best way to
handle the medical incident, based on potential matching
with similar incidents addressed in th e past.
As the DGH gathers information, it initiates an identi-
fication process inside the CRP, which a Data Base (DB)
is used, in order to remember the medical procedures that
have been taken in every data set collected by the patient.
Context matchin g can be based in well known techn iques
with the k-Nearest Neighbour(s) (k-NN) algorithm being
a firm candidate. A pertinent solution which is based on
k-NN and also exhibits non-prohibitive complexity has
been provided in [24,29].
In order to validate our concept and give some evi-
dence on the potentials arising from context matching
functionality in our case of the emergency medical ap-
plication, the overall end to end delay (in msec) is meas-
ured for a Data Base Access Application. The transaction
inter-arrival time has been set equal to 30 sec and the
transaction size to 100 bytes. This d elay, except from the
metrics that have been presented in 4.3, it also includes
the response time from the DB query and entry, respec-
tively.
Figure 15(a) depicts the average end-to-end delay of
the application in the network segment during the simu-
lation time. As can be observed, since the optimization
time is significantly decreased through i-MED (as de-
scribed above), the average data end-to-end delay is also
decreased, accordingly. The same tendency appears also
in Figure 15(b) for the overall end to end delay for the
whole simulated time.
This proves in general i-MED’s capability to 1) be
ready to propose solutions to a medical problem based on
previous tackling of similar incidents, as well as to 2)
predict future similar situations.

Intelligent Management Functionality for Emergency Medical Applications Based on Cognitive Networking Principles 35
0
0.05
0.1
0.15
0.2
0.25
0.3
051015 20 25 3035 40 455055
T ime (sec)
Av. End to End Delay (m sec)
legacy
i-Med
Tadapt_end
(
i- med
)
Topt_start
Tadapt_end
(legacy)
(a)
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
legacy i-Med
Av. End to End Del ay ( m s ec)
(b)
Figure15. (a) Av. End to End Delay vs time; (b) Av. End to
End Delay without (legacy) and with i-MED.
6. Conclusions
The focus of this paper was the investigation of the
potential contribution of cognitive wireless networks
operating in the FI era, aiming at the enhancement of the
efficiency and dependability of medical applications. In
this respect, the paper has proposed novel intellig ent ma-
nagement functionality (i-MED), b ased on cognitive net-
working principles, which is targeted at emergency med-
ical applications. It has described the architecture of the
proposed functionality and its operational principles,
while it has also utilized two scenario variations for the
extraction of simulation results that showcase i-MED’s
capability to provide fast and reliable solutions, utilizing
the NS-2 simulator.
In general, i-MED can improve the performance of
legacy reconfiguration management functionality in terms
of throughput and capacity, since it can reach the appro-
priate decisions comparatively faster. In doing so, it can
prove itself absolutely necessary in the case of critical
situations, such as an emergency medical incident, where
high gains in timely service provision and performance
can be obtained. Therefore, i-MED enhances the perfor-
mance of management functionality for medicine appli-
cations, by improving its overall levels of efficiency, re-
liability and dependability.
Aspects of future work involve the further investigation
in the exchange of information among i-MED functional
blocks, in terms of interfaces definition and description.
Moreover, novel algorithms will be explored in terms of
accuracy and velocity, so as to f urthe r e n hance the pe rfor -
mance of i-MED. Last, cognition will be further extended
to the application domain, so as to draw even more impor-
tant medical conclusi ons (e.g. during the patient’s tra nsfer
to the DGH) and propose alternative emergency treatment
options, even with regards to surgery, based on previous
knowledge and experience.
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