E-Health Telecommunication Systems and Networks, 2012, 1, 27-36
http://dx.doi.org/10.4236/etsn.2012.13005 Published Online September 2012 (http://www.SciRP.org/journal/etsn)
Performance Evaluation of Healthcare Monitoring System
over Heterogeneous Wireless Networks
Sabato Manfredi
Faculty of Engineering, University of Naples Federico II, Napoli, Italy
Email: sabato.manfredi@unina.it
Received July 1, 2012; revised August 5, 2012; accepted August 12, 2012
The wide diffusion of healthcare monitoring systems allows continuous patient to be remotely monitored and diagnosed
by doctors. The problem of congestion, namely due to the uncontrolled increase of traffic with respect to the network
capacity, is one of the most common phenomena affecting the reliability of transmission of information in any network.
The aim of the paper is to build a realistic simulation environment for healthcare system including some of the main
vital signs model, wireless sensor and mesh network protocols implementation. The simulator environment is an effi-
cient mean to analyze and evaluate in a realistic scenario the healthcare system performance in terms of reliability and
Keywords: Modeling, Simulation and Management of Health-Care Systems; Applications of Information and
Communication Technologies to Health-Care Management; E-Health; Remote Health Monitoring;
Telemedicine; Chronic Disease Management
1. Introduction
The recent increased interest in distributed and flexible
wireless pervasive applications has drawn great attention
to the QoS (Quality of Service) requirements of WNCS
(Wireless Network Control System) architectures based
on WSANs (Wireless Sensor Actuator Networks) ([1]).
Wireless data communication networks provide reduced
costs, better power management, easier maintenance and
effortless deployment in remote and hard-to-reach areas.
Although WSAN research was originally undertaken for
military applications, as the field slowly matured and
technology rapidly advanced, it has been extended to
many civilian applications such as environment and
habitat monitoring, home automation, traffic control, and
more recently healthcare applications [2,3,5-8]. In par-
ticular, WBAN (Wireless Body Area Network) technol-
ogy has recently significantly increased the potential of
remote healthcare monitoring systems (e.g. [4,9,10]).
WBAN is a particular kind of WSAN consisting of stra-
tegically placed wearable or implanted (in the body)
wireless sensor nodes that transmit vital signs (e.g. heart
rate, blood pressure, temperature, pH, respiration, oxygen
saturation) without limiting the activities of the wearer.
The data gathered can be forwarded in real time to the
hospital, clinic, or central repository through a LAN
(Local Area Network), WAN (Wide Area Network) or
cellular network. Doctors and carers can at a distance
access this information to assess the state of health of the
patient. Additionally, the patient can be alerted by using
SMS, alarm, or reminder messages. In a more advanced
WBAN, a patient’s sensor can even use a neighbor sen-
sor to relay its data if the patient is too far away from the
central server (e.g. the hospital data storage). This com-
munication mode is called “multi-hop” wireless trans-
mission. Generally speaking, multi-hop not only extends
the communication distance but also saves energy con-
sumption since direct sensor-server long distance wire-
less communication is avoided through hop-to-hop relay.
WBANs will become increasingly pervasive in our daily
lives. Recently, WBAN (Wireless Body Area Network)
technology has significantly increased the potentiality of
the remote healthcare monitoring systems [12,18]. Pa-
tient is integrated from multiple sources of measure, PoC
(Point of Care) devices, enabling individuals to accu-
rately, easily, and efficiently generate and collect health-
care data. The microelectronics industry are providing an
increasing number of PoC devices that combine analysis,
power efficiency and testing functionalities with a simple
user interface, addressing constraints like device weara-
bility and networking [13-17]. Transmission needs to be
performed for communicating the collected physiological
signals from the PoC devices to the sink node (i.e. PDA,
a smart-phone, or a custom designed microcontroller-
based device) and eventually for sending the aggregated
measurements to a remote medical station. PoC nodes
opyright © 2012 SciRes. ETSN
form a cluster of Wireless Body Area Network (WBAN)
and are usually in the basic configuration of a star topol-
ogy, transmitting information to the sink node that pro-
vides the functionality of collecting data and routing
them to the remote station (i.e. Hospital terminal) by a
Wireless Mesh Network (WMN). There is a wide variety
of available wireless technologies that can serve data
transmission between the sink node and a remote station
such as WLAN, GSM, GPRS, UMTS, and WiMAX. On
the other side, wireless communication standards utilized
for short range intra-BAN communication (between PoC
node and sink terminal) are IEEE 802.15.1 (Bluetooth
[20]) and 802.15.4 (i.e. Zigbee [19]). The Zigbee stan-
dard [19] targets low-cost, low data-rate solutions with
multimonth-to-multiyear battery life, and very low com-
plexity. Bluetooth [20] is a low-power and low-cost RF
standard, operating in the unlicensed 2.4 GHz spectrum.
Recently, the 802.15.6 IEEE Task Group [21] is planning
the development of a communication standard optimized
aimed to define BAN that works at a range even shorter
than other wireless technologies that are already avail-
able in the market.
The wide diffusion of healthcare monitoring systems
allows continuous patients to be remotely monitored and
diagnosed by doctors. The problem of congestion,
namely due to the uncontrolled increase of traffic with
respect to the network capacity, is one of the most com-
mon phenomena affecting the reliability of transmission
of information and the loss of packets in any network. In
addition, in wireless sensor networks, it increases the
dissipated energy at the sensor node. In many health care
applications (i. e. fetal electrocardiogram monitoring, tele-
cardiology), communication links carry vital information
between patient and monitoring devices, that need to be
transmitted in short “bursts”, requiring a reliable connec-
tion. So it is a focal issue, especially in PoC health care
systems, to design an appropriate protocol solution ad-
dressing reliability, energy efficiency, scalability, re-
duced packet losses, timely delivery without failure. By
large the problem of congestion in both wireless and
wired communication networks has addressed in recent
years a number of research efforts (see i.e. [24-28,31]
and references therein). One of the generic approaches
for congestion control is to control the rate of flow of
traffic to a source node (i.e. [22,23]) by allocating the
available resource capacity following some fairness cri-
teria (i.e. max min, proportional). In those cases, conges-
tion control mechanisms are regarded as a distributed
algorithm carried out by sources and links in order to
solve a global optimization problem (see [29-32] and
references therein). Preliminary routing based appro-
aches to congestion control in healthcare system are pre-
sented in [33,34]. In this scenario we introduce a realistic
simulator for heathcare system evaluation. Specifically,
we build a realistic simulation environment including the
main vital signs and wireless networks protocols model-
ling. The simulator environment is used to analyze and
evaluate the effect of congestion phenomena on the
healthcare system performance in terms of reliability and
efficiency. The simulator can be used for validating
novel congestion control scheme to mitigate the conges-
tion phenomena and guarantee efficient healthcare ser-
vice delivery.
The rest of the paper is outlined as follows. In Section
2 the evaluation environment is described in terms of
healthcare network topology, communication protocols,
performance metrics and vital signals, while in Section 3
a simulation analysis of congestion effect on healthcare
system performance is shown. Finally, conclusions and
future work are outlined in Section 4.
2. Healthcare System Simulation and
Evaluation Environment
Most of healthcare system scenario is composed of a
cluster of WBANs relaying vital information to the Hos-
pital (H) by a WMN. Each WBAN is characterized by
the “many-to-one” traffic patterns with a single sink node
and multiple source PoC nodes which can be considered
to be affixed with the patients. In the paper we will pay
our attention to such representative healthcare topology
scenario in which all the PoC sensor nodes are stationary
and transmit data to the Hospital terminal (H) by the sink
terminal data collector. The communication between the
sinks and the Hospital terminal is guaranteed by a wire-
less mesh network. This results in heterogeneous wire-
less communication network as it is composed of devices
adopting different protocols such as Zigbee and Wifi.
Herein we set up an evaluation environment in Mat-
lab/Simulink-based simulator TrueTime [41], which fa-
cilitates co-simulation of controller task execution in
real-time kernels and wireless network environment. The
simulations are performed using the above topology of
sensors randomly transmitting their information to the
sink. The intra BAN protocol used for PoC-Sink com-
munication is the standard Zigbee, while the protocol of
WMN supporting sink-remote Hospital terminal com-
munication is Wifi 802.11. Specifically, we build the
simulation environment including the following models:
The intra WBAN standard protocol Zigbee used for
PoC sensors-Sink communication;
The wireless mesh protocol Wifi 802.11 supporting
sinks-remote Hospital terminal communication;
The Ad-hoc On-Demand Distance Vector Routing
Protocol (AODV) to route packets in the network;
The models of the main vital signs such as respiration,
electrocardiogram, fetal electrocardiogram, the oxy-
gen saturation of the pacemaker control system de-
Copyright © 2012 SciRes. ETSN
In addition, the simulation model takes the path-loss of
the radio signals into account. The radio model includes
support for 1) ad-hoc wireless networks; 2) isotropic an-
tenna; 3) inability to send and receive messages at the
same time; 4) path loss of radio signals modelled as
where d is the distance and α is a parameter cho-
sen to model the environment ranging in [2,4]; 5) inter-
ference from other terminals. In what follows we will
describe the details of the above components and we will
present the main performance metrics considered in the
paper. Moreover, we have enhanced the simulation envi-
ronment by introducing realistic modelling of PoC sensor
power consumption.
2.1. Wifi Protocol Model Simulation
The IEEE 802.11b is modeled taking into account of the
channel access method CSMA/CA (Carrier Sense Multi-
ple Access with Collision Avoidance). Specifically, in
the simulation, a transmission is modelled like this: The
node that wants to transmit a packet checks to see if the
medium is idle. The transmission may proceed, if the
medium is found to be idle, and has stayed so for 50 μs.
If, on the other hand, the medium is found to be busy, a
random back-off time is chosen and decremented in the
same way as when colliding. When a node starts to
transmit, its relative position to all other nodes in the
same network is calculated, and the signal level in all
those nodes are calculated according to the path-loss
formula 1d
. The signal is assumed to be possible to
detect if the signal level in the receiving node is larger
than the receiver signal threshold. If this is the case, then
the signal-to-noise ratio (SNR) is calculated and used to
find the block error rate (BLER). Note that all other
transmissions add to the background noise when calcu-
lating the SNR. The BLER, together with the size of the
message, is used to calculate the number of bit errors in
the message and if the percentage of bit errors is lower
than the error coding threshold, then it is assumed that
the channel coding scheme is able to fully reconstruct the
message. If there are (already) ongoing transmissions
from other nodes to the receiving node and their respec-
tive SNRs are lower than the new one, then all those
messages are marked as collided. Also, if there are other
ongoing transmissions which the currently sending node
reaches with its transmission, then those messages may
be marked as collided as well. Note that a sending node
does not know if its message is colliding, therefore ACK
(Acknowledge) messages are sent on the MAC protocol
layer. From the perspective of the sending node, lost
messages and message collisions are the same, i.e. no
ACK is received. If no ACK is received during ACK
timeout, the message is retransmitted after waiting for a
random back-off time within a contention window. The
contention window size is doubled for every retransmis-
sion of a certain message. The back-off timer is stopped
if the medium is busy, or if it has not been idle for at
least 50 μs. There are only “Retry limit” number of re-
transmissions before the sender gives up on the message
and it is not retransmitted anymore.
2.2. ZigBee Protocol Model Simulation
ZigBee has a rather low bandwidth, but also a really low
power consumption. Although it is based on CSMA/CA
as 802.11 b/g, it is much simpler and the protocols are
not the same. The packet transmission model in ZigBee
is similar to WLAN, but the MAC procedure differs and
is modeled taking into account of the following vari-
NB, number of backoffs;
BE, backoff exponent;
MacMinBE, the minimum value of the backoff ex-
ponent in the CSMA/CA algorithm. The default value
is 3;
AMaxBE, The maximum value of the backoff exponent
in the CSMA/CA algorithm. The default value is 5;
MacMaxCSMABackoffs, the maximum number of
backoffs the CSMA/CA algorithm will attempt before
declaring a channel access failure. The default value
is 4;
In a Rayleigh fading, the relative speed of two nodes
and the number of multiple paths that the signal takes
from the sender to the receiver is taken into account.
The basic battery uses a simple integrator model, so it
can be both charged and recharged. We have enhanced the
model by implementing the power consumption model
described in [35] including the energy spent by the PoC
sensor to transmit and receive packets. The main protocol
simulation parameters are summarized in Table 1.
2.3. AODV Routing Protocol
The Ad-hoc On-Demand Distance Vector Routing Pro-
tocol (AODV) [11] is one common routing algorithm in
ad hoc networks and is based on the principle of discov-
ering routes as needed. AODV is a reactive algorithm
that has a low network utilization, processing and mem-
ory overheads. The request is made on-demand rather
than in advance, to take into account the dynamic chang-
ing of a network structure. In the AODV routing algo-
rithm, the source node issues a route request packet to the
destination node at the time a path is needed and this
allows mobile nodes to pass messages through their
neighbors to nodes with which they cannot directly
communicate. AODV does this by discovering the routes
(the discovery phase) along which messages can be
passed. AODV makes sure these routes do not contain
loops and tries to find the shortest route possible. AODV
Copyright © 2012 SciRes. ETSN
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performs route discovery if necessary. is also able to handle changes in routes and can create
new routes if there is an error. Because of their limited
range, each node can only communicate with the nodes
next to it. A node keeps track of its neighbors by listen-
ing for a HELLO message that each node broadcasts pe-
riodically. When one node needs to send a message to
another node that is not its neighbor, it initiates a path
discovery phase by broadcasting a route request (RREQ)
packet to its neighbors. The request (RREQ) message
contains several fields such as the source, destination and
lifespan of the message and a Sequence Number that
serves as a unique ID. When intermediate nodes receive
a RREQ packet, they update their routing tables for a
reverse route to the source and, in the same way, when
the intermediate nodes receive a route reply (RREP),
they update the forward route to the destination. If multi-
ple RREPs are received by the source, the route with the
shortest hop count is chosen. If a route is not used for
some period of time, a node cannot be sure whether the
route is still valid; consequently, the node removes this
route from its routing table. Sequence Numbers serve as
time stamps allowing nodes to determine the timeliness
of each packet and to prevent the creation of loops. Every
time a node sends out any type of message it increases its
own “Sequence Number”. Each node records the Se-
quence number of all the other nodes. A higher Sequence
Number refers to a fresher route. The Route Error Mes-
sage (RERR) allows the AODV to adjust routes when
node/link failure occurs. Whenever a node receives a
RERR, it looks at the routing table and removes all the
routes that contain the bad nodes. When the next hop link
breaks, RERR packets are sent by the starting node of the
link to a set of neighboring nodes that communicate over
the broken link with the destination. If data is flowing
and a link break is detected, a Route Error (RERR)
packet is sent to the source of the data in a hop-by-hop
fashion. As the RERR extends towards the source, each
intermediate node invalidates routes to any unreachable
destinations. When the source of the data receives the
RERR, it invalidates the route or routes in question and
2.4. Vital Signs Model Simulation
In the follows we will describe the main vital signs im-
plemented in the simulator following the model given in
the original references.
2.4.1. Respir a ti on
Respiration is an important physiologic function that
quantifies the physiological states by volume, timing and
shape of the respiratory waveform. It is associated with
the kinematics of the chest thereby bringing about
changes of the thoracic volume. Among sensors used to
measure respiration there are ones based on inductive
plethysmography ([36]) or magnetometers ([37]). Re-
cently, a wearable based piezo-resistive sensor has been
developed [38]. This signal requires a reporting rate
ranging from 10 Hz to 50 Hz [39]. An example of breath
signal implemented in the simulator is shown in Figure 1.
Table 1. ZigBee and Wifi protocol main parameters.
ZigBee (802.15.4) Wifi (802.11 b/g)
Path loss exponent 3 3
Sink/wifi Router
Transmission Power0 dbm 20 dbm
Sink capacity 30 pkt/s -
Sensor Transmission
Power –3 dbm -
Receiver signal
threshold –48 dbm –48 dbm
Sensor Buffer Size 30 -
Sink/wifi Router
Buffer Size 300 500
Retry Limit 3 3
Ack timeout 0.000864 sec 0.000864 sec
Packet size 150 byte 2.5 MB
Data rate 250,000 bit/s 10 Mbit/s
Figure 1. Respiration vital sign dynamic evolution.
2.4.2. Electrocardiogram
The electrocardiogram (ECG) is a time-varying signal
reflecting the ionic current flow which causes the cardiac
fibres to contract and subsequently relax. The ECG sur-
face is obtained by recording the potential difference
between two electrodes placed on the surface of the skin.
Here, for simulation purpose, we have used a dynamical
model proposed in the literature [40], based on three
coupled ordinary differential equations which is capable
of generating realistic synthetic electrocardiogram (ECG)
signals. Standard clinical ECG application can require
reporting rate from 200 Hz to 300 Hz [39]. In Figure 2 it
is shown the dynamic of ECG signal implemented into
the proposed simulator by using the above model.
2.5. Performance Metrics
Depending on the type of target application, QoS in
healthcare system can be characterized by, among other
factors, reliability, energy efficiency, timeliness, robust-
ness, availability, and security. Among the different per-
formance indices measuring the level of QoS, the fol-
lowing are particularly significant and will be evaluated
by the evaluation environment discussed above:
Throughput is the effective amount of data transmit-
ted in a specific unit of time. In healthcare monitoring,
to provide a better observation of a patient’s health
condition, a sensor can transmit data at a high report-
ing frequency and then use a high data rate to send
out the large amount of data sensed.
Delay is the time elapsing from the departure of a
data packet from the source node to its arrival at the
destination node, including queueing delay, switching
delay and propagation delay, etc. Delay sensitive ap-
plications are common in healthcare environments
requiring the monitoring system to deliver the data
packets in real-time in order to fulfill specific timing
Reliability is the packet reception ratio (the number of
“received” packets divided by the number of “trans-
mitted” packets), related also to the two above per-
formance indices.
Energy consumption is the energy spent in the time to
permit the network to work. The nodes must be capa-
ble of playing their role for a sufficiently long period
using the energy provided by their battery. Conse-
quently, energy efficiency is one of the main re-
quirements of WBANs. Packet collision at the MAC
layer, routing overhead, packet loss, and packet re-
transmission reduce energy efficiency.
System lifetime. It is strictly related to the nodes av-
erage and variance of the energy consumption and it
can be defined as the duration of time until some node
depletes all its energy.
Network coverage. It is related to the nodes average
and variance of the energy consumption and it means
that the entire network space can be monitored by the
sensor nodes.
Packet loss rate is the percentage of data packets that
are lost during the process of transmission. It can be
used to represent the probability of packets being lost.
A packet may be lost due to e.g. congestion, bit error,
or bad connectivity. This parameter is closely related
to the reliability of the network.
Scalability. This is the ability of the healthcare system
to guarantee acceptable performance (i.e. a reliability
>80%) with the increasing number of patient sensors.
It indicates if the healthcare system will be suitable
for a large nursing system.
3. Evaluation of Congestion Effect on
Healthcare System Performance
Firstly we analyze by using the simulator exploited
above, the effect of congestion phenomena on the
healthcare network performance degradation. Notice that
Figure 2. ECG vital sign dynamic evolution.
Copyright © 2012 SciRes. ETSN
for each signal at each PoC sensor, we appropriately
package the sampled piece of vital sign information into
packet to be sent to the Hospital terminal. We have
evaluated the network reliability and scalability by in-
creasing the number of the PoC sensors (and so the over-
all reporting rate) accessing to the sink. As we note from
Figure 3, there is a threshold of 30 pkt/sec for the overall
PoC sensors reporting rate that produces network con-
gestion with reducing reliability and scalability, and in-
creasing of packet loss: this threshold corresponds to the
capacity C = 30 pkt/s of the sink to manage packets. In
the same way there is an increasing of time delivery
(Figure 4).
The worsening of the performance in terms of reliabil-
ity is mainly due to the buffer overflow and collision
packet losses. On the other side the time delay for the
delivered packet is due to the time of packet spent wait-
ing at the sink queue before to be transmitted to the Hos-
pital. Indeed, if we consider an overall sensors reporting
rate close to sink capacity of 30 pkt/s, it results a time
delay of 10 time unit (see Figures 5 and 6) correspond-
ing to the backlog delay at the sink queue of
sin k300 3010
Buffer Rr. For increasing value of
the input reporting rate, it will be a collapse of sink with
heavy reduction in reliability and time delivery perform-
ances due to the increasing of packet losses, packet re-
transmission and collision effects. The increasing of
packet losses and packets retransmitted increases the PoC
sensors average energy (Figure 7) and its variance (Fig-
ure 8) consumed by the nodes with consequently heavy
reduction of network life time and network coverage.
The main effect of the congestion at the sink bottleneck
node on the healthcare system performance is the
reduction of the quality of the vital signs received at the
Hospital. This makes hard to reassemble the vital signs at
the Hospital server as so as the estimation of the patient
pathologies by doctor. Indeed an increasing of reporting
rate and therefore of traffic in the network leads to a
worsening of the quality of vital signs, even at the high
priority, that requires more bandwidth as it appears from
Figure 10 for the case of the ECG signal. On the other
side, the Breath sign presents low degradation level al-
though it requires low priority and bandwidth require-
ments (Figure 9). Therefore, the shape of the signals
with high bandwidth requirement can strongly deteriorate
loosing significant characteristics for the correct patient
diagnosis. For instance the congestion effect on the qual-
ity of ECG is the loss of many peaks (e.g. compare Fig-
ures 2 and 10) that are of main importance for the cor-
rect patient diagnosis about the cardiac pathologies (e.g.
ventricular tachycardia, and ventricular fibrillation). More-
over, as shown in Table 2, the average latency is the
same irrespective of the different bandwidth/priority re-
quirement of the vital signs (i.e. ECG signal requires
more bandwidth and more responsiveness than the respi-
ration sign one). We remark that also for low reporting
rate, might occur packets loss due to MAC error and/or
collision. For instance, in this case, the overall packet
loss due to the collision effect is about of 24% as shown
in Table 2.
4. Conclusion and Future Work
Due to the “many-to-one” nature of the traffic patterns in
healthcare system architecture, congestion at the sink
ottleneck node can occur when the PoC nodes traffic b
Figure 3. Healthcare remote system reliability as function of the PoC sensors reporting rate.
Copyright © 2012 SciRes. ETSN
Figure 4. Time delivery delay as functi on of the PoC sensors reporting rate.
Figure 5. Time delivery delay for PoC sensors reporting rate close to the sink capacity of 30 pkt/s.
Figure 6. Time delivery delay dynamic for P o C sensors reporting rate close to the sink capacity of 30 pkt/s.
Copyright © 2012 SciRes. ETSN
Figure 7. PoC sensors average energy consumption as function of the reporting rate.
Figure 8. Variance of sensors energy consumption as function of the reporting rate.
Figure 9. Breath vital signal received at the Hospital (continues line), breath vital signal sampled at the PoC sensor (dashed
Copyright © 2012 SciRes. ETSN
Copyright © 2012 SciRes. ETSN
Figure 10. ECG signal received at the Hospital.
Table 2. Time Delivery to the Hospital terminal of each vital
sign class with different pr iority.
Signal/Priority Delay
Breath/1 40 s
ECG/10 40 s
Packet Collision loss 24%
increases with respect to the sink capacity. So, it is a fo-
cal issue for health care application to design an appro-
priate sink capacity allocation strategy addressing reli-
ability and timely delivery without failure. We have built
a realistic simulation environment for healthcare remote
system evaluation including the main vital signs and
wireless network protocol modelling. The simulator is
used to analyze and evaluate the effect of congestion
phenomena on the healthcare system performance in
terms of reliability and efficiency. The proposed simula-
tor is suitable to support and validate the design of a
novel management control law for healthcare applica-
tions that is object of ongoing work.
[1] U.S. DOE, “Industrial Wireless Technology for the 21st
Century,” Report, Technology Foresight, 2004.
[2] K. Hung and Y. Zhang, “Implementation of a WAP-
Based Telemedicine System for Patient Monitoring,”
IEEE Transactions on Information Technology in Bio-
medicine, Vol. 7, No. 2, 2003, pp. 101-107.
[3] E. Jovanov et al., “Stress Monitoring Using a Distributed
Wireless Intelligent Sensor System,” IEEE Engineering
Medicine and Biology Magazine, Vol. 22, No. 3, 2003, pp.
[4] E. Jovanov, A. Milenkovic, C. Otto and P. C. de Groen,
“A Wireless Body Area Network of Intelligent Motion
Sensors for Computer Assisted Physical Rehabilitation,”
Journal of NeuroEngineering and Rehabilitation, Vol. 2,
No. 1, 2005, pp. doi:10.1186/1743-0003-2-6
[5] T. Gao et al., “Vital Signs Monitoring and Patient Track-
ing over a Wireless Network,” IEEE Proceedings of the
27th Annual International Conference of the Engineering
in Medicine and Biology Society, Shanghai, 17-18 Janu-
ary 2006, pp. 102-105.
[6] Kafeza et al., “Alerts in Mobile Healthcare Applications:
Requirements and Pilot Study,” IEEE Transactions on
Information Technology in Biomedicine, Vol. 8, No. 2,
2004, pp. 173-181. doi:10.1109/TITB.2004.828888
[7] U. Varshney, “Wireless Networks for Enhanced Moni-
toring of Patients,” International Journal of Healthcare
Technology and Management, Vol. 6, No. 4-6, 2005, pp.
489-499. doi:10.1504/IJHTM.2005.007009
[8] C. Poli, A. Pietrabissa, D. G. Ferriero and M. Grigioni,
“An Optimization Framework for Hospital Assets Track-
ing with Sensor Networks,” 2012.
[9] T. O’Donovan, J. O’Donoghue, C. Sreenan, P. O’Reilly,
D. Sammon, K. O’Connor, “A Context Aware Wireless
Body Area Network (BAN),” Proceedings of the Perva-
sive Health Conference, London, 1-3 April 2009, pp. 1-8.
[10] K. Yazdandoost et al., “Channel Model for Body Area
Network (BAN),” Proceedings of the Annual Interna-
tional Conference of the IEEE Engineering in Medicine
and Biology Society, 20-25 August 2008, pp. 1549-1552.
[11] Y. Zhang and T. A. Gulliver, “Quality of Service for Ad
Hoc On-Demand Distance Vector Routing,” IEEE Inter-
national Conference on Wireless and Mobile Computing,
Montreal, 22-24 August 2005, pp. 192-196.
[12] Age Data, “U. S. Census Bureau Statistics,” 2010.
[13] L. Gatzoulis and I. Iakovidis, “Wearable and Portable
eHealth Systems,” IEEE Engineering in Medicine and
Biology Magazine, Vol. 26, No. 5, 2007, pp. 51-56.
[14] C. P. Price, A. St John and J. M. Hicks, “Point-of-Care
Testing,” 2nd Edition, AACC Press, Washington DC,
[15] A. J. Tudos, G. A. J. Besselink and R. B. M. Schasfoort,
“Trends in Miniaturized Total Analysis Systems for
Point-of-Care Testing in Clinical Chemistry,” Lab on a
Chip, Vol. 1, No. 2, 2001, pp 83-95.
[16] S. Rhee, B.-H. Yang and H. H. Asada, “Artifact-Resistant
Power-Efficient Design of Finger-Ring Plethysmographic
Sensors,” IEEE Transactions on Biomedical Engineering,
Vol. 48, No. 5, 2001, pp. 795-805.
[17] S. Guillen, M. T. Arredondo, V. Traver, J. M. Garcia and
C. Fernandez, “Multimedia Telehomecare System Using
Standard TV Set,” IEEE Transactions on Biomedical En-
gineering, Vol. 9, No. 12, 2002, pp. 1431-1437.
[18] C. Lau, R. S. Churchill, J. Kim, F. A. I. Matsen and Y.
Kim, “Asynchronous Web-Based Patient-Centered Home
Telemedicine System,” IEEE Transactions on Biomedical
Engineering, Vol. 49, No. 12, 2002, pp. 1452-1462.
[19] “Zigbee Alliance,” 2009. www.zigbee.org
[20] “Bluetooth,” 2009. www.bluetooth.com
[21] IEEE 802.15, “WPAN Task Group 6,” 2009.
[22] A. Woo and D. C. Culler, “A Transmission Control
Scheme for Media Access in Sensor Network,” Proceed-
ings of the 7th Annual International Conference on Mo-
bile Computing and Networking, New York, 16-21 July
2004, pp. 221-235.
[23] C.-Y. Wan, S. B. Eisenman and A. T. Campbell, “CODA:
Congestion Detection and Avoidance in Sensor Net-
works,” Proceedings of the 1st International Conference
on Embedded Networked Sensor Systems, 15 October
2003, pp. 266-279.
[24] S. M. ElRakabawy and C. Lindemann, “A Practical
Adaptive Pacing Scheme for TCP in Multihop Wireless
Networks,” IEEE/ACM Transactions on Networking,
August 2011, pp. 975-988.
[25] J. Zhao, L. Wang, S. Li, X. Liu, Z. Yuan and Z. Gao, “A
Survey of Congestion Control Mechanisms in Wireless
Sensor Networks,” Sixth International Conference on In-
telligent Information Hiding and Multimedia Signal
Processing, Darmstadt, 15-17 October 2010, pp. 719-722.
[26] C. T. Ee and R. Bajcsy, “Congestion Control and Fairness
for Many-to-One Routing in Sensor Networks,” Pro-
ceedings of the 2nd International Conference on Embed-
ded Networked Sensor Systems, New York, pp. 148-161.
[27] B. Hull, K. Jamieson and H. Balakrishnan, “Mitigating
Congestion in Wireless Sensor Networks,” Proceedings
of the 2nd International Conference on Embedded Net-
worked Sensor Systems, New York, 3-5 November 2004,
pp. 134-147.
[28] P. Levis, N. Patel, D. Culler and S. Shanker, “Trickle: A
Self Regulating Algorithm for Code Propagation and
Maintenance in Wireless Sensor Networks,” Proceedings
of the 1st Symposium Networked Systems Design Imple-
mentation, San Francisco, 29-31 March 2004, pp. 15-28.
[29] S. H. Low and D. E. Lapsley, “Optimization Flow Con-
trol I. Basic Algorithm and Convergence,” IEEE/ACM
Transactions on Networking, Vol. 7, No. 6, 1999, pp.
861-874. doi:10.1109/90.811451
[30] F. P. Kelly, A. K. Maulloo and D. K. H. Tan, “Rate Con-
trol in Communication Networks: Shadow Prices, Pro-
portional Fairness, and Stability,” Journal of the Opera-
tional Research Society, Vol. 49, No. 3, 1998, pp. 237-
[31] S. Low, F. Paganini and J. C. Doyle, “Internet Congestion
Control,” IEEE Control Systems Magazine, Vol. 22, No.
1, 2002, pp. 28-43.
[32] J. Mo and J. Walrand, “Fair End-to-End Window-Based
Congestion Control,” IEEE/ACM Transaction on Net-
working, Vol. 8, No. 5, 2000, pp. 556-567.
[33] S. Manfredi, “Reliable and Energy-Efficient Cooperative
Routing Algorithm for Wireless Monitoring Systems,”
IET Wireless Sensor Systems, Vol. 2, No. 2, 2012, pp.
128-135. doi:10.1049/iet-wss.2011.0103
[34] S. Manfredi, “A Cooperative Routing Algorithm to In-
crease QoS in Wireless E-Healthcare Systems,” E-Health
Care Systems and Wireless Communications: Current and
Future Challenges, IGI Global, 2012.
[35] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam and E.
Cayirci, “Wireless Sensor Networks: A Survey,” Com-
puter Networks, Vol. 38, No. 4, 2002, pp. 393-422.
[36] F. H. Wilhelm, M. C. Pfaltz and P. Grossman, “Continu-
ous Electronic Data Capture of Physiology, Behavior and
Experience in Real Life: Towards Ecological Momentary
Assessment of Emotion,” Interacting Computers, Vol. 18,
No. 2, 2006, pp. 171-186.
[37] A. Matsubara and S. Tanaka, “Unconstrained and Nonin-
vasive Measurement of Heartbeat and Respiration for
Drivers Using a Strain Gauge,” Proceedings of the 41st
SICE Annual Conference, 5-7 August 2002, pp. 1067-
1068. doi:10.1109/SICE.2002.1195323
[38] C. T. Huang, C. L. Shen, C. F. Tang and S. H. Chang, “A
Wearable Yarn-Based Piezo-Resistive Sensor,” Sensors
and Actuators A: Physical, Vol. 141, No. 2, 2008, pp.
396-403. doi:10.1016/j.sna.2007.10.069
[39] M. Patel and J. Wang, “Applications, Challenges, and
Prospective in Emerging Body Area Networking Tech-
nologies,” IEEE Wireless Communications, Vol. 17, No.
1, 2010, pp 80-88. doi:10.1109/MWC.2010.5416354
[40] P. E. McSharry, G. D. Clifford, L. Tarassenko and L.
Smith, “A Dynamical Model for Generating Synthetic
Electrocardiogram Signals,” IEEE Transactions on Bio-
medical Engineering, Vol. 50, No. 3, 2003, pp. 289-294.
[41] M. Ohlin, D. Henriksson and A. Cervin, “TrueTime 1.5—
Reference Manual,” Department of Automatic Control,
Lund University, 2007.
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