A comparative Study
Paulo A. C. S. Neves
Superior School of Technology, Polytechnic Institute of Castelo Branco, Avenida do Empresário, Castelo Branco, Portugal
Joel F. P. Fonseca
Department of Informatics,University of Beira Interior, Rua Marquês D’Avila e Bolama,Covilhã, Portugal
Joel J. P. C. Rodrigues
Institute of Telecommunications - Networks and Multimedia Group, Portugal
Department of Informatics, University of Beira Interior, Rua Marquês d'Ávila e Bolama, Covilhã, Portugal
Keywords: Wireless Sensor Networks, Simulation Tools, Medical applications of WSNs.
Abstract: This paper presents a study on three simulation tools for Wireless Sensor Nertworks (WSNs): Network
Simulator 2 (ns-2), Java Simulator (J-Sim) and Sensor Network Emulator and Simulator (SENSE). We
present the concept of WSNs, each simulator in terms of its features, a view on current applications of
WSNs on medicine and a comparative study on the simulators studied. We conclude that SENSE presents
the better approach for WSNs.
Heart diseases are leading mortality in the United
States, and aneurysm is the number one cause of
death in Europe. The aging population of developed
countries are posing significant weight in the budget
of healthcare systems (Istepanian et al., 2004).
A Body Sensor Network (Aziz et al., 2006) can
be used to monitor a patient in real world-life
activities. Such network gathers data from several
body parts for latter processing and detection of
possible heart problems.
On-site patient monitoring leads to efforts in the
concept of m-Health: “mobile computing, medical
sensor, and communications technologies for health-
care” (Istepanian et al., 2004).
Wireless Sensor Networks (WSNs) application
began in military areas and are spanning into
everyday life (Akyldiz et al., 2002). A WSN is
composed of intelligent mobile sensors that
comprise a processing part with memory, the
sensing block, a wireless communication transceiver
and a power module. The sensor nodes collaborate
to gather data to another node, typically with more
computational power and communication resources
that receive the sensing data (Khemapech et al.,
2005). This node is commonly named sink, since it
collects (sinks) data, or base station (since it can also
send network parameters to the sensor network
Simulation has always been very popular among
network-related research. However, WSNs presents
additional challenges, since they are energy
constrained, resource constrained and ideally, size
constrained. Energy concerns bring communication
challenges, since the majority of energy
consumption in a node comes from wireless
The rest of the paper is organized as follows.
Section 2 shows the studied simulation tools and
section 3 includes some applications of WSNs in
medicine. Section 4 presents the comparative study
and section 5 concludes the paper.
Eiber A. (2008).
In Proceedings of the First International Conference on Biomedical Electronics and Devices, pages 111-114
DOI: 10.5220/0001055601110114
This paper studies three of the main simulation tools
freely available: the Network Simulation 2 (ns-2,
January 2007), the Java Simulator (J-Sim, January
2007, Sobeih et al., 2005) and SENSE (SENSE,
March 2007).
2.1 Network Simulator 2 (ns-2)
The Network Simulator version 2 (ns-2) was
developed in the University of Berkeley, CA, USA,
and it is actually the the-facto standard on network
simulation in general. The simulator is object-
oriented and based in two languages: C++ as the
development language and Object Tool Command
Language (oTcl) as the simulation description
Ns-2 is in constant evolution and worldwide use.
Current version is 2.31 released in March 2007 and
version 2.32 is still a pending release. Some
extensions provide Sensor Network simulation, like
the one provided by the Naval research Laboratory.
The two languages approach may step up the
learning curve. However, Tool Command Language
(Tcl) is very appropriate for writing simulation code,
presenting a good learning curve, and C++ provides
execution performance.
2.2 Java Simulator (J-Sim)
The Java Simulator (J-Sim), developed by the Ohio
State University, USA and is construction is based
on the Autonomous Component Architecture.
This simulator also uses two languages, Java and
OTcl. J-Sim is component-oriented, so the basic
entities are components that communicate with each
other via send/receive data through ports. Ports are
also components whose behavior is defined by
another component named contract.
J-Sim also provides a script interface that allows
integration with different script languages such as
Perl, Tcl or Python. Furthermore, a friendly and
appropriate graphical interface for simulation
results, although the graphical interface leaves
something to desire. J-Sim provides a model to
simulate WSNs, as depicted in Figure 1. We clearly
define the nodes that will stimulate de WSN (target
nodes), the nodes that will constitute the sensor
network itself (sensor nodes), and the sink nodes
(also known as base stations). As with any
simulation, we clearly need to know simulation
Target nodes have only one communication
channel, the sensor channel, since they only send
stimulus to the sensor network, the sensor nodes
communicate in two ways, sensor and wireless
channel, and finally the sink nodes only
communicate in the wireless channel.
Figure 1: J-Sim simulation model for sensor networks.
2.3 Sensor Network Simulator
and Emulator (SENSE)
Sensor Network Simulator and Emulator (SENSE) is
the only simulator of the three that was specifically
designed for sensor network simulation (SENSE,
March 2007).
This simulator presents a component-based
approach, created as a template class that allows the
use of the component with different kinds of data.
SENSE is still in an early stage of development.
When trying to use the simulator we found some
issues that were gladly solved by the developers.
This simulator provides three user types: high level,
network designers and component designers.
A component in SENSE communicates through
ports: this model frees the simulator from
interdependency. This also enables extensibility,
reusability and scalability. Component extension in
functionality is possible if the interface is compatible
and no inheritance between components is used.
SENSE only uses C++ language and the
interface only uses text, and the results are provided
in a text file. This contributes to the efficient use of
computational power, but greatly reduces the
perceived user-friendliness.
SENSE requires that all nodes are identical. A
common simulation engine stores the event queues
of the system. SENSE compares the received signal
strength with a threshold and decides if the packet
has reached its destination.
BIODEVICES 2008 - International Conference on Biomedical Electronics and Devices
The use of intelligent medical monitoring can
significantly decrease the number of hospitalizations
and nursing visits (Heidenreich et al., 1999), by
acting as an agent on human’s behalf and launch of
emergency alerts when appropriate. Human beings
don’t appreciate a visit to the hospital, even more
when after a surgery (Naftalin and Habiba, 2000).
If a given health threat situation can be identified
in advance, for instance the risk of an heart attack,
measures can be taken to minimize or even suppress
the risk. Some studies, namely on ECG (Zhou et al.,
2005) data retrieval, provide valuable insight on
current health condition of a human being.
Body Sensor Networks usually consist of several
implanted or wearable biosensors, such as ECG,
EEG, glucose sensors, accelerometers, blood
pressure and oxygen saturation sensors, temperature
sensors, among others (Huaming and Jindong,
Medical applications span over different areas,
such as heart-related, BSN applications, emergency
response applications, asthma monitoring and even
human error detection and correction. In (Lin et al.,
2006) authors present a solution for two important
challenges: the first the discomfort of using a wired
data-gathering system; and the achievement of the
best path for data to flow in peer-to-peer wireless
communication protocols.
A wearable ECG system, based on motes to
create a WSN presented in (Taylor and Sharif,
2006), using the tMote Sky developed at Berkley
University. In (Huaming and Jindong, 2006) authors
used a WSN with BSN-MAC that they developed. A
scheme collects context information through sensing
and applies it to help detect QRS complex in an
ECG. This information provides the means to build a
personalized heart diary of a person. In (Lee et al.,
2007) authors present a WSN to collect ECG and
body temperature. A server inside the hospital
wirelessly receives data. Using this approach the
patients are always being monitored and the server
can process data in order to send alerts to medical
staff. This presents great benefits when compared to
the patient-initiated alarm.
(Lorincz et al., 2004) present some challenges
and opportunities for sensor networks in emergency
response and a new architecture for wireless
monitoring and tracking. In (Chu et al., 2006), an
experimental scenario is presented for patients with
asthma or allergic situations, where a person is
carrying a GPS-enabled system. A wireless system
connects to a dedicated server in order to identify
possible hazardous areas in the person’s vicinity and
issue an alert. In (Ohmura et al., 2006), authors
developed a sensor network in a real hospital
environment that prevents medical accidents by
monitoring nursing activities. The paper describes
the design and implementation of the sensor
network. Therefore, we can conclude that there is a
broad range of applications for WSN in medicine,
and that the development of novel sensors and novel
ways to build sensors are of primordial importance,
together with the wireless communication challenges
and node deployment.
Table 1 summarizes the main features of simulation
tools ns-2, J-Sim and SENSE. J-Sim proves to be a
good match for ns-2, no wonder that many
researchers are using it instead of ns-2, mostly due
to its ACA architecture. In (Sobeih et al., 2005) a
study is presented on the performance of ns-2
compared to J-Sim, so we target our efforts on J-Sim
and SENSE.
Table 1: Summary of simulator tools features.
Event ns-2 J-Sim SENSE
Hard Easy Medium
Version for Microsoft® Windows®
No Yes No
Popular in scientific and academic’s
Yes Yes No
Object or component oriented
Object Comp Comp
Programming languages
++, oTc
Java, oTcl C++
Learning curve
Steep Moderate Moderate
Easy to create only sample
No No Yes
WSN simulation
Extern API Dedicated
Graphically-driven simulation build
No Yes No
Component diversity made to simulate
Yes Yes No
Supplies configuration network files
Yes Yes Yes
Easy to change a simulation model
Yes Yes No
Easy to define a simulation area
No Yes No
Easy to define number and position of
No Yes No
Easy to create/change a protocol
No No No
Steps to run a simulation
Few Few Some
Display graphical mode to see
simulation parameters
Yes Yes No
The first impression is that SENSE is more
difficult to use than J-Sim: the “text-only” results,
the C++ programming language for simulation
creation and no graphical support.
We tried to simulate the sample WSN depicted
in Figure 2 in J-Sim. On SENSE we managed to
simulate a 11 nodes network, but not the same one.
It was not possible to simulate the same network
in SENSE. In terms of battery the model Linear
Battery was used, cpu base model for CPU, in the
physical layer duplex transceiver was used, in the
network layer a shr_ack and cbr in the application
Figure 2: Simulated network node placement.
We provide some insight on three simulation tools
used for simulating WSNs. The application on
WSNs to medicine seems a very promising way to
go, with clear benefits to users and medical staff.
Simulation rises as a good tool for early study of
applications of WSNs in medicine, mainly if a given
network is to be used and can be simulated.
In spite of a large user community and more
experience on the J-Sim, we consider the SENSE
simulator a better approach. Developed for WSN
from scratch, the developing team answered very
promptly to our requests and even released a new
download to address the identified issues.
Part of this work has been supported by the Group of
Networks and Multimedia of the Institute of
Telecommunications – Covilhã Lab, Portugal.
Akyldiz, I. F., Su, W., Sankarasubramaniam, Y. &
Cayirci, E. (2002) Wireless Sensor Networks: a
Survey. Computer Networks (Elsevier), 38, 393-422.
Aziz, O., Lo, B., King, R., Darzi, A. & Yang, G.-Z. (2006)
Pervasive Body Sensor network: an Approach to
Monitoring the Post-operative Surgical Patient.
International Workshop on Wearable and implantable
Body Sensor Networks.
Chu, H.-T., Huang, C.-C., Lian, Z.-H. & Tsai, T. J. P.
(2006) A Ubiquitous Warning System for Asthma-
Inducement. IEEE Int. Conf. on Sensor Nets,
Ubiquitous and Thrustworthy Computing. Taiwan.
Heidenreich, P., Ruggerio, C. & Massie, B. (1999) Effect
of a Home Monitoring System on Hospitalization and
Resource Use for Patients with Heart Failure.
American Heart Journal, 138, 633-640.
Huaming, L. & Jindong, T. (2006) Body Sensor Network
Based Context Aware QRS Detection. IN IEEE (Ed.)
Pervasive Health Conference and Workshops.
Innsbruck, Austria, IEEE.
Istepanian, R. S. H., Jovanov, E. & Zhang, Y. T. (2004)
Guest Editorial Introduction to the Special Section on
M-Health: Beyond Seamless Mobility and Global
Wireless Health-Care Connectivity. IEEE
Transactions on Information Technology in
Biomedicine, 8, 405-414.
The J-SIM Simulator,, (cited Jan' 2007).
Khemapech, I., Duncan, I. & Miller, A. (2005) A Survey
of Wireless Sensor Networks Technology. IN
PEREIRA, M. M. A. R. (Ed.) 6th Annual
Postgraduate Symposium on the Convergence of
Telecommunications, Networking and Broadcasting.
Liverpool, UK.
Lee, D.-S., Lee, Y.-D., Chung, W.-Y. & Myllyla, R.
(2007) Vital Sign Monitoring System with Life
Emergency Event Detection Using Wireless Sensor
Network. IEEE Conference on Sensors. Daegu, Korea.
Lin, J.-L., et al. (2006) The Development of Wireless
Sensor Network for ECG Monitoring. 28th Annual
International Conference of the IEEE, Engineering in
Medicine and Biology Society. New York, NY, USA.
Lorincz, K., Malan, D. J., Fulford-Jones, T. R. F., Nawoj,
A., Clavel, A., Shnayder, V., Mainland, G., Welsh, M.
& Moulton, S. (2004) Sensor Networks for Emergency
Response. IEEE Pervasive Computing, 3, 16-23.
Naftalin, N. J. & Habiba, M. A. (2000) Keeping Patients
out of the Hospital - Patients Like it. Bmj 2000, 320,
NS-2 Networking Simulator,
ns/, (cited Jan'2007=
Ohmura, R., Naya, F., Noma, H., Kuwahara, N.,
Toriyama, T. & Kogure, K. (2006) Practical Design of
A Sensor Network for Understanding Nursing
Activities. 31st IEEE Conference on Local Computer
Networks. Tampa, Florida, USA.
SENSE - Sensor Network Simulator and Emulator,, [cited
Sobeih, A., et al. (2005) J-Sim: A Simulation Environment
for Wireless Sensor Networks. 38th Annual Simulation
Symposium (ANSS '05). San Diego, CA, USA.
Taylor, S. A. & Sharif, H. (2006) Wearable Patient
Monitoring Application (ECG) using Wireless Sensor
Networks. 28th Annual International Conference on
the IEEE Engineering in Medicine and Biology
Society. New York, NY, USA.
Zhou, H., Hou, K. M., Ponsonnaille, J., Gineste, L. &
Vaulx, C. D. (2005) A Real-Time Continuous Cardiac
Arrhythmias Detection System: RECAD. IN IEEE
(Ed.) IEEE Engineering in Medicine and Biology
Society. Shanghai, China.
BIODEVICES 2008 - International Conference on Biomedical Electronics and Devices