Performance Evaluation of Healthcare Monitoring System over Heterogeneous Wireless Networks

doi:10.4236/etsn.2012.13005

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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: [email protected]

Received July 1, 2012; revised August 5, 2012; accepted August 12, 2012

ABSTRACT

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

efficiency.

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

S. MANFREDI

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-

vice.

S. MANFREDI 29

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-

ables:

 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

S. MANFREDI

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.

S. MANFREDI 31

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

requirements.

 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.

S. MANFREDI

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

PoCs

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.

S. MANFREDI 33

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.

S. MANFREDI

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

line).

S. MANFREDI

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.

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