MONITORING QoS OVER WIRELESS SENSOR NETWORKS
For Medical Applications
Carlos Abreu
Polytechnic Institute of Viana do Castelo, Av. Atlântico, Viana do Castelo, Portugal
Manuel Ricardo
1
, Paulo Mendes
2
1
INESC Porto, Faculty of Engineering, University of Porto, Porto, Portugal
2
Industrial Electronics Dept., University of Minho, Guimarães, Portugal
Keywords: Quality of Service, Medical Applications, Wireless Sensor Networks.
Abstract: Wireless Sensor Networks (WSNs) for medical purposes enables real-time (RT) acquisition of vital signals.
In this context the network reliability, data integrity and RT delivery have extreme importance. The network
should guarantee an appropriate level of Quality of Service (QoS). This paper presents the QoS parameters
and metrics of standard telemedicine and WSNs based Medical Applications (MA), and a strategy, based on
QoS monitoring, to prevent QoS degradation in WSNs for MA.
1 INTRODUCTION
A Biomedical WSN (BWSN) is a small-size WSN
for MA. BWSNs have the potential to promote new
applications and services in different healthcare
scenarios, e.g., mobile telemedicine, emergence
response and management, location services and
patient monitoring (Varshney, 2009). In the case of
patient monitoring scenarios, a BWSN can be used
to monitor a wide range of biological and vital
signals.
This monitorisation can be done through various
situations, e.g., in emergency response providing
information about the health condition of patients,
monitoring physiological signals in people with
chronic illness or measuring the activity level of
elderly or disabled people (Gao et al., 2008). In any
of these situations, BWSN presents itself as a key
technology to the success of many ongoing
investigations. They are responsible to transport
information, which in MA have to obey to very strict
quality criteria (Liang, 2009). As noted in (Liang &
Balasingham, 2007), the main function of a BWSN
is to ensure that sensed signals are delivered reliably
and efficiently. There is a set of QoS requirements
that must be fulfilled.
In the rest of this paper we discuss the QoS needs
of MA and briefly present the main QoS parameters
and metrics in traditional networks and propose a
strategy to QoS monitoring in order to improve the
performance (prevent the degradation) of BWSNs.
2 QoS REQUIREMENTS OF
MEDICAL APPLICATIONS
Currently, most medical procedures are performed
based on information obtained from electronic
or/and informatics systems. This information, about
the health condition of an individual, must have
medical quality. According to the (American
College of Medical Quality, 2010), medical quality
can be defined as “the degree to which health care
systems, services and supplies for individuals and
populations increase the likelihood for positive
health outcomes and are consistent with current
professional knowledge”. This definition makes
clear that, healthcare systems and its communication
networks have a key role in the quality of healthcare
services provided to citizens. So, it is extremely
important that the communication networks used to
transport medical information ensure a service with
382
Abreu C., Ricardo M. and Mendes P..
MONITORING QoS OVER WIRELESS SENSOR NETWORKS - For Medical Applications.
DOI: 10.5220/0003163603820385
In Proceedings of the International Conference on Biomedical Electronics and Devices (BIODEVICES-2011), pages 382-385
ISBN: 978-989-8425-37-9
Copyright
c
2011 SCITEPRESS (Science and Technology Publications, Lda.)
quality. In the context of communication networks,
this characteristic is expressed in terms of QoS.
Recommendation E.800 of the International
Telecommunication Union (ITU) introduced the
following QoS definition: "totality of characteristics
of a telecommunication service that bear on its
ability to satisfy stated am implied needs of the user
of the service". According to the definitions
presented above, communication networks used to
transport information used by health providers are a
keystone to the quality of patient health and care
according to its medical condition.
The term QoS is understood and interpreted in
various ways by the scientific community; most
often it refers to the ability of a network to deliver
data efficiently and reliably (Liang, 2009). RFC2386
defines QoS as: "A set of parameters that the
network should ensure during the transport of a data
stream". Given this definition, it is necessary to
specify what parameters should the network ensure,
who imposes them and in what situations.
2.1 QoS Support in BWSN
BWSNs can be considered small size WSNs, and
therefore they have the same constrains, e.g., limited
computational power, limited memory and severe
power supply limitations (in implantable nodes this
problem worsens) (Khan et al., 2009). However,
from the network viewpoint there are some
important differences, such as, the number of sensor
nodes on the network, its localization and the
number of hops between nodes, e.g., compared with
normal WSNs, BWSNs contain a reduced number of
nodes; nodes localization in BWSNs is well known
or restricted to a limited area, on the other hand, in a
normal WSN the sensor nodes are randomly
deployed in large areas. These differences, although
significant, do not invalidate the utilization of the
same QoS parameters and metrics. However, if
taken into account, they may facilitate the
development of more efficient QoS mechanisms.
Most of the ongoing investigation in BWSNs takes
advantage on these characteristics to implement QoS
mechanisms to specific monitoring applications.
There are several published works about BWSNs
based monitoring systems that, in most cases, are
individual or for a small number of patients (Khan et
al., 2009) (Alemdar & Ersoy, 2010). The authors of
(Liang & Balasingham, 2007), present a priorities
based routing protocol with QoS guaranties on the
following parameters, end-to-end delay, delivery
ratio and power consumption. This protocol was
validated by simulation with 20 sensor nodes.
According to scholarly information, this was the first
QoS enabled routing protocol to healthcare
monitoring applications. In (Ko et al., 2010), the
authors present a monitoring system to hospital use
where the nodes localization is precise and well
known. In this case, the authors conclude that the
existence of a backbone of static network nodes
improves the network performance.
2.2 QoS Support in WSN
WSNs differ from “traditional” networks in several
aspects, e.g., WSN nodes have strong constrains in
power consumption and consequently in the power
of RF signal emitted which limits its communication
range and transmission rate (Zhang et al., 2010);
they have strong computational and memory
limitations, so that the algorithms used must be
lightweight and efficient; the dynamic nature of
WSNs and the lack of centralized control present
additional challenges in the development of efficient
QoS solutions (Ben-Othman & Yahya, 2010).
Due to its specificities, in WSNs, the QoS must
be ensured in all layers of the protocol stack in use,
starting at physical and ending in application layer.
The authors of (Wang et al., 2006), present an
analysis of the QoS requirements at each layer based
on the Open System Interconnection (OSI) model.
Here, our concern is to discover how a WSN can
guarantee QoS to applications that depend on it.
Thus, in order to obtain some independence from the
network technology used we will focus on the
mechanisms to guarantee QoS at the network layer.
In (Asokan, 2010) the author analyses the most
relevant routing protocols in mobile Ad Hoc
networks (WSN can be regarded as an Ad Hoc
network). In his analysis he highlights that the
studied protocols take into account only a small set
of the total QoS parameters. Finally, he concludes
by saying that routing protocols are needed, namely,
those that take into account more ample QoS
parameters. Also among the authors of (Asokan,
2010) it’s consensual that there are still many
unsolved issues regarding routing protocols with
QoS guarantees.
3 QoS PARAMETERS
AND METRICS
QoS requirements are imposed to network by
applications that use it. They don't depend only on
the intrinsic characteristics of data to be transmitted
but also on its use, in other words, on its application.
MONITORING QoS OVER WIRELESS SENSOR NETWORKS - For Medical Applications
383
3.1 QoS Parameters Concerning the
Application Viewpoint
In (Ruiz, 2006) the author identifies the following
parameters, as the most important at this level: Peak
Data Rate (PDR), Sustainable Data Rate (SDR) and
Maximum Burst Size (MBS). Drawing upon these
parameters one can define traffic classes which can
be applied to different data flows and applications.
Different network technologies have adopted
different QoS classes. However, there is a common
feature among them, the distinction between classes
for applications with and without RT requirements.
Being the most widely used network technology, in
Table 1 we present the traffic classes defined for IP
networks as defined in (Marchese, 2007).
Table 1: IP QoS Classes.
QoS class Characteristics
0 RT, jitter sensitive, highly interactive.
1 RT, jitter sensitive, interactive.
2 Transaction data, highly interactive.
3 Transaction data, interactive.
4
Low loss only (short transactions, bulk
data, video streaming).
5 Traditional applications of IP networks.
Other technologies have defined different
classes. However, in essence, they have the same
division taking into account the application
characteristics.
3.2 QoS Parameters Concerning the
Network Viewpoint
According to (Marchese, 2007) and (Ruiz, 2006),
from the communication network viewpoint, the
most important metrics used to measure and ensure
that the QoS required for a given application is
guaranteed, are: Packet Transfer Delay (PTD),
Packet Delay Variation (PDV), Packet Loss Ratio
(PLR), Packet Error Ratio (PER) and Bandwidth
(BW). There are no unique values to these metrics.
Rather, they depend on the network characteristics
and particular requirements of each application.
3.3 QoS Parameters in WSN
In the context of WSNs, due to its specificities, the
QoS metrics presented earlier are insufficient. In
(Chen & Varshney, 2004) the authors identify new
QoS metrics that reflect the collective effort of all
network nodes to perform a given task. They are:
collective delay or latency, collective packet loss
rate, collective bandwidth, collective data rate and
information throughput. WSNs routing protocols
with QoS requirements have to take into account
these metrics in order to provide more efficient QoS
mechanisms.
3.4 Telemedicine QoS Metrics
QoS requirements depend on the application
characteristics and network technology in use. Since
we are interested in the development of mechanisms
for QoS support in BWSNs, a good starting point is
to study how 'traditional' networks are used in
medicine. There are countless applications and
services using information systems and
communications networks in medicine. However, in
this work, the focus is on applications and services
that allow remote monitoring of biological and vital
signals, such as, ECG, EEG, BP and T. According
(Ruiz, 2006) and (Varshney, 2009), taking into
account the characteristics of each one of these
signals and the requirements of the application at
stake, the following metrics,
Table 2, are sufficient to
telemedicine and telemonitoring applications and
services in “traditional” IP networks.
Table 2: QoS Metrics to Telemedicine.
PTD Application or service characteristics
50ms Audio transmission with interaction.
100ms Interactive application.
150ms Video transmission with interaction.
400ms Default value to real-time applications.
PLR
3% Tx of medical images and signals.
10%
Tx of radiology images with compression
ratios of 10:1 and 20:1.
15% Interactive video and audio transmission.
20% Default to real-time applications.
BW
15Kb/s Tx of medical images and signals.
60Kb/s
Tx of radiology images with compression
ratios of 10:1 and 20:1.
100Kb/s Interactive video and audio transmission.
200Kb/s Default value to real-time applications.
4 QoS MONITORING
As mentioned in (Asokan, 2010), QoS provision
over low-rate Wireless Personal Area Networks
(LoWPANs) can be done over different layers on the
communication protocol stack. However, by
themselves, these mechanisms do not provide QoS
in all situations, e.g., in individual monitoring
applications it is necessary to know how many
signals we can transmit without degrading the
BIODEVICES 2011 - International Conference on Biomedical Electronics and Devices
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network QoS, if we need to monitor multiple
patients then we also need to know how many
patients may be on the network. In a more complex
scenario, the patients are distributed in a given area:
in this case, it is necessary to determine if and where
the QoS can be guaranteed.
It is necessary to develop management and
supervision tools to manage and control the network.
These management and supervision tools must
provide sufficient information to add new
monitoring services or applications to network
without degrading the existing QoS.
Figure 1: QoS Monitoring System.
Figure 1 presents, in terms of high level blocks,
the QoS monitoring system architecture and
principal components. Each sensor node has, stored
in a QoS Database (QoS DB), information about its
QoS requirements. This information can be accessed
by the QoS Manager (QoSM) and transmitted
through the LoWPAN to the QoS Monitoring
Software (QoSMoS). To add a new patient to the
network, the healthcare provider must insert the
patient QoS needs on the QoSMoS. Then, taking
into account the network QoS status (based on
information collected from the sensor nodes in the
network) and new patient QoS needs the Patient
Admission and Control (PAC), decides if and where
the patient can be inserted in the network.
5 CONCLUSIONS
This paper provides a review of the state-of-the-art
in QoS for telemedicine and BWSNs, presenting the
principal QoS parameters and metrics. Finally, we
have argued that existing QoS mechanisms do not
guarantee QoS in all utilization scenarios and a
different approach to increase QoS or prevent its
degradation has been proposed. Due limited capacity
of WSNs it’s necessary to develop monitoring tool
to allow QoS management and supervision.
ACKNOWLEDGEMENTS
Portuguese Foundation for Science and Technology,
FCT, PhD Grant SFRH/BD/61278/2009.
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