Wireless Sensor Network, 2011, 3, 215-240
doi:10.4236/wsn.2011.37025 Published Online July 2011 (http: //www.SciRP.org/journal/ wsn)
Copyright © 2011 SciRes. WSN
An Exploratory Study of Experimental Tools for Wireless
Sensor Networks
A. K. Dwivedi1, O. P. Vyas2
1School of Studies in Computer Sc. & I.T., Pandit Ravishank ar Shukla Univer sity, Raipur, India
2Indian Institute of Inf or m ation Technology, Allahabad, India
E-mail: {anuj.ku.dwivedi, dropvyas}@gmail.com
Received March 5, 2011; revised June 5, 2011; accepted June 25, 20 11
The objective of this contribution is to present expositive review content on currently av ailable experimental
tools/services/concepts used for most emerging field Wireless Sensor Network that has capability to change
many of the Information Communication aspects in the upcoming era. Currently due to high cost of large
number of sensor nodes most researche s in wireless sensor networks ar ea is performed by using th ese expe-
rimental tools i n vari ous universities, institutes, and resear ch centers before implementing real o ne. Also the
statistics gathered from these experimental tools can be realistic and convenient. These experimental tools
provide the better option for studying the behavior of WSNs before and after implementing the physical one.
In this contribution 63 simulators/simulation frameworks, 14 emulators, 19 data visualization tools, 46 test-
beds, 26 debugging tools/services/concepts, 10 code-updation/reprogramming tools and 8 network monitors
has been presented that are used worldwide for WSN researches.
Keywords: Experimental Tools, Simulation, Emulation, Testbed, Data Visualizat ion, Debugger, Network
Monitor, Code-Updater, Wireless Sensor Network
1. Introduction
Wireless Sensor Networks (WSNs) employ a large
number of miniature disposable autonomous devices
known as sensor nodes to form the network without the
aid of any established infrastructure. In a WSN, the indi-
vidual nodes are capable of sensing their environments,
processing the information locally and sending it to one
or more collection points through a wireless link. Re-
search activities in the area of WSNs need expositive
performance statistics about scenario, systems, protocols,
gathered data and applications. There are various expe-
rimental tools for fulfilling these requirements, someone
are in practical use while others are in literatures.
There are some highly cited research contributions that
present comparative study of some simulators, testbeds,
and other tools but this is just a little bit of an emerging
broad area and also there is no any single literature that
presents an extensive survey or review study on experi-
mental tools available for WSN res earch purposes. The
objective of this contribution is to present an extensive
survey on experimental tools especially used for WSN
research purposes that are based on various criteria, sce-
nario conditions, parameters and other factors, also pre-
senting the other relevant contents related to experimen-
tal tools, as well as focusing on the highly summarized
pros and cons of mostly presented experimental tools
with respect to WSNs.
2. Simulators for Wireless Sensor Networks
A simulator is a software that imitates selected parts of
the behavior of the real world and is normally used as a
tool for research and development. Depending on the
intended usage of the simulator, different parts of the
real-world system are modeled and imitated. The parts
that are modeled can also be of varying abstraction level.
Earlier simulators especially designed for WSN imitates
the wireless media and the constraints nodes in the net-
work but currently sensor network simulators have a
detailed model of the wireless media including effects of
obstacles between nodes, while other simulators have a
more abstract model.
2.1. Necessity of Simulation
The emergence of wireless sensor networks brought
Copyright © 2011 SciRes. WSN
many new emerging issues to network designers. Tradi-
tionally, the three main techniqu es for analyzing the per-
formance of wired and wireless networks are analytical
methods, computer simulation, and physical measure-
ment. Due to many constraints imposed on sensor net-
works, such as energy limitation, decentralized collabo-
ration and fault toleran ce, algorithms f or se nso r n etwor ks
tend to be quite complex and usually defy analytical me-
thods that have been proved to be fairly effective for
traditional networks. Furthermore, few sensor networks
have come into existence, for there are still many un-
solved research problems, so measurement is virtually
impossible [1]. It appears that simulation is the only
feasible approach to the quantitative analysis of sensor
2.2. Limitations of the Simulation
The challenge of developing, deploying, and debugging
applications on the realistic environment will be unmet
with simulations. Many of the current simulators are un-
able to model many essential characteristics of the real
world. Simulations are based on common simplified as-
sumptions and these do not produce accurate results. The
simulation results are only as good as the model and they
are still only estimated or projected outcomes. Especially
for wireless sensor networks simulation models do not
capture the radio and sensor irregularity. In a research
contribution [2] some issues are arises that really in-
fluencing the simulation results when simulators are di-
rectly used:
The first one is that there should be an impact on si-
mulation results by operating system architecture on
which simulator is installed and result would have been
The second one is that all of cases simulators use a
simulated clock, which advances in constant increments
of time. Constant increment of time is decided by time
stamp of the earliest event. So we must replace simula-
tion clock with real system clock.
The third one is that all simulators has its own proto-
col stack in its core (kernel) called simulator protocol
stack. There are several problems in order to use a simu-
lator protocol stack as a real network protocol stack, such
as most of the simulators have various redundant proto-
cols at various levels of simulator protocol stack to sup-
port other types of networks for example TCP/IP based
networks, Wireless Mesh Networks, Mobile Ad hoc
Networks etc. The simulator also includes different radio,
mobility, and propagation models. So according to our
view these unnecessary redundant components must be
removed in order to use a simulator protocol stack as a
real protocol stack for WSNs.
The fourth one is that all simulators have its own ar-
chitecture and design objectives. We have not evaluated
but we can say that these factors could also influenc e the
simulation results.
2.3. Type of Simulation
Simulators either run as in an asynchronous mode, event
triggered mode, or in synchronous mode, where events
happen in parallel in fixed time slots [3]:
Synchronous Simulation: The synchronous simulation
is based on rounds. At the beginning of each round,
the simulators increments the glob al time by one unit.
Then, it moves the nodes according to their mobility
models and updates the connections according to the
connectivity model. After that, the framework iterates
over the set of nodes and performs these steps for
each node.
Asynchronous Simulation: The asynchronous simula-
tion is purely event based. The simulator holds a list
of message events and timer events, which is sorted
by the time when these events should happen (arrival
of message, execution of timer-handler). The simula-
tor repeatedly picks the most recent event and ex-
ecutes it.
In general, the asynchronous simulation mode runs
much faster than the synchronous mode. The main rea-
son lies in the fact that the synchronous simulation mode
loops over all nodes and performs for each node the set
of fixed steps even if most of the nodes may not do any-
thing at all. Whereas in asynchronous mode, only mes-
sage and timer events are processed and no unnecessary
cycles are wasted.
2.4. Cate gor iz ati on of Simu lat or s
In a research contribution WSN Simulators are catego-
rized [4] as:
Generic Network Simulators: Generic network simu-
lators simulate systems with a focus on networking
aspects. The user of the simulator typically writes the
simulation application in a high level language dif-
ferent from the one used for the real sensor network.
Since the focus of the simulation is on networking the
simulator typically provid e s detailed si mulation of the
radio medium, but less detailed simulation of the
Code Level Simulators: Code level simulators use the
same code in simulation as in real sensor network
nodes. The code is compiled for the machine that is
running the simulator, typ ically a PC workstation that
is magnitudes faster than the sensor node. Typically
code level simulators are operating system specific
Copyright © 2011 SciRes. WSN
since they need to replace driver code for the sensors
and radio chips available on the node with driver
code that instead have hooks into the simulator.
Firmware Level Simulators: These simulators are
based on emulation of the sensor nodes and the soft-
ware that runs in the simulator is the actual firmware
that can be deployed in the real sensor network. This
approach gives the highest level of detail in the simu-
lation and enables accurate execution statistics. This
type of simulation provides emulation of micropro-
cessor, radio chip and other peripherals and simulation
of radio medium. Due to the high level of detail pro-
vided by firmware level simulators, they are usually
slower than code level or generic network simulators.
In another research contribution [5], simulators have
been classified into the following three major categories
based on complexity:
Algorithm Level Simulators: Some simulators focus
on the logic, data structure and presentation of the al-
gorithms. For example, AlgoSensim analyzes specific
algorithms in WSNs, e.g. localization, distributed
routing, flooding, etc. Shawn is targeted to simulate
the effect caused by a ph enomenon, impro ve scalabil-
ity and support free choice of the implementation
model. Sinalgo offers a message passing view of the
network, which captures well the view of actual net-
work devic e s .
Packet Level Simulators: Some simulators implement
the Data Link and Physical Layers in a typical OSI
network stack. The most popular and widely used
network simulator NS-2 is not originally targeted to
WSNs but IP networks. SensorSim is an extension to
NS-2 which provides battery models, radio propaga-
tion models and sensor channel models. J-Sim ad op ts
loosely-coupled, component-based programming
model, and it supports real-time process-driven simu-
lation. GloMoSim is designed using the parallel dis-
crete-event simulation capability provided by PAR-
Instruction Level Simulators: Some simulators model
the CPU execution at the level of instructions or even
cycles. They are often regarded as emulators. They
compute the power of a particular sensor’s hardware
platform in WSNs. TOSSIM simulates the TinyOS
network stack at the bit level. Atemu is an emulator
that can run nodes with distinct applications at the
same time.
Several simulators exist that are either adjusted or de-
veloped specifically for wireless sensor networks. Here
is a list presenting 63 simulators/simulation frameworks
with their highly influential features related to WSNs:
Network Simulator [6,7] (specially higher versions,
like NS-3) has been used to evaluate WSNs but the
accuracy of results with lower versions (NS-2) are
questionable since the MAC protocols, packet for-
mats, and energy models are very different from
those of typical sensor network platforms. NS-3 is a
discrete-event network simulator for Internet systems.
NS-3 is a new simulator (not backwards-compatible
with NS-2).
Mannasim (NS-2 Extension for WSNs) [8] is a wire-
less sensor networks simulation environment based
on the NS-2. Mannasim extends NS-2 introducing
new modules for design, development and analysis of
different WSN applications. Having Script Generator
Tool (SGT) for TCL script creation.
TOSSIM [9,10] is a TinyOS mote simulator which is
useful for testing both the algorithms and implemen-
tations; however it does not simulate the physical
phenomena that are sensed.
TOSSF [11] is very similar to, and inspired by TOS-
SIM. TOSSF addresses the limitations of TOSSIM
but one limitation of TOSSF is that it no longer simu-
lates the devices as accurately as TOSSIM.
PowerTOSSIMz [12] is a power modeling extension
to TOSSIM. PowerTOSSIM accurately models power
consumed by TinyOS applications, i.e. eff icien t po w er
simulat ion for TinyOS appli c a tions.
ATEMU [13] is a Sensor Network Emulator/Simu-
lator/Debugg er. The primary strength of ATEMU is
that it is most accurate simulator for a particular
hardware platform. Conversely, the main limitation of
ATEMU is its dependence on the Mica-2 Mote hard-
ware architecture.
COOJA [14] is a Contiki OS simulator which allows
for cross-level simulation. It is a novel type of wire-
less sensor network simulation that enables holistic
simultaneous simulation at different levels. In
COOJA one simulation can contain nodes from sev-
eral different abstraction levels. These are the net-
work level, the operating system level, and the ma-
chine code level.
GloMoSim (Global Mobile Information Systems Si-
mulation) [15] suffers the same problems as NS, i.e.,
the packet formats, energy models, and MAC proto-
cols are not representative of those used in WSNs.
While GloMoSim has been used to evaluate WSNs
but the accuracy of results is questionable.
QualNet [16] is the commercial version of GloMo-
Sim with upgraded features such as, providing a
comprehensive environment for designing protocols,
creating and animating experiments, and analyzing
the results of those experiments.
SENSE [17] does not support sensors, physical phe-
nomena, or environmental effects. Overall, the MAC
protocol support and radio propagation make SENSE
less than ideal for accurate evaluation of WSNs re-
Copyright © 2011 SciRes. WSN
VisualSENSE [18] is a good framework but it does
not provide any protocols above the wireless medium,
nor sensor or physical phenomena other than sound.
AlgoSenSim [19] is a framework used to simulate
distributed algorithms. It is not protocol stack
oriented but algorithm oriented. I t fo cus es on n e twork
specific algorithms like localization, distributed
routing, flooding etc. AlgoSenSim is easily modularly:
It uses XML configuration file. It is efficiency
oriented, but optimizations are hidden to the user.
AlgoSenSim’s main purpose is to facilitate the im-
plementation and quality analysis of new algorithms.
Georgia Tech Network Simulator (GTNetS) [20] is a
full-featured network simulation environment that al-
lows researchers in computer networks to study the
behavior of moderate to large scale networks, under a
variety of conditions. The design philosophy of
GTNetS is to create a simulation environment that is
structured much like actual networks are structured.
OMNet++ [21] [22] is an extensible, modular, com-
ponent-based C++ simulation library and framework,
with an Eclipse-based IDE and a graphical discrete
event simulator.
Castalia [23] is OMNet++ Extension for WSNs and
can be used by researchers and developers who want
to test their distributed algorithms and/or protocols in
realistic wireless channel and radio models, with a
realistic node behavior especially relating to access of
the radio. Castalia can also be used to evaluate dif-
ferent platform characteristics for specific applica-
tions, since it is highly parametric, and can simulate a
wide range of pl a t forms.
J-Sim (formerly JavaSim) [24] is a truly platform-
neutral, component-based, compositional simulation
environment. J-Sim provides support for sensors and
physical phenomena. Energy modeling, with the ex-
ception of radio energy consumption, is also appro-
priate for sensor networks. However, the only MAC
protocol provided for wireless networks is IEEE
802.11. Therefore, accuracy of simulations still suf-
JiST/SWANS (Java in Simulation Time/Scalable
Wireless Ad hoc Network Simulator) [25]: JiST is a
high-performance discrete event simulation engine
that runs over a standard Java virtual machine. It is a
prototype of a new general-purpose approach to
building discrete event simulators, called virtual ma-
chine-based simulation. SWANS is a scalable wire-
less network simulator built atop the JiST platform.
Its capabilities are similar to NS-2 and GloMoSim but
are able to simulate much larger networks.
JiST/SWANS++ [26] is an extended version of JiST/
SWANS provides more realistic and meaningful si-
mulation results.
Avrora [27] is a cycle-accurate instruction level sen-
sor network simulator which scales to networks of up
to 10,000 nodes and performs as much as 20 times
faster than previous simulators with equivalent accu-
racy, handling as many as 25 nodes in real-time.
Avroras ability to measure detailed time-critical
phenomena can shed new light on design issues for
large-scale sensor networks.
Sidh [28] is a simulator specifically designed for
WSNs. Sidh is efficient; it scales to simulate net-
works with thousands of nodes faster than real-time
on a typical desktop computer. It is component based
and easily reconfigurable to adapt to different: levels
of simulation detail and accuracy; communication
media; sensors and actuators; environmental condi-
tions; protocols; and applications.
Prowler [29] is a probabilistic wireless sensor net-
work simulator. Prowler is written in MATLAB and
also running under MATLAB thus providing an easy
way of application prototyping with nice visualization
(J) Prowler [30] is a discrete event simulator similar
to Prowler but written in Java. The simulator suppor ts
pluggable radio models and MAC protocols and mul-
tiple application modules. Currently two radio models
are implemented: Gaussian and Rayleigh, and one
MAC protocol: Mica-2 with no acknowledgment.
Though it could be modified to simulate more general
systems. It does not provide support for sensors or
physical phenomena.
LecsSim [31] is a simulator for large wireless net-
works provides an easy way to simulate distributed
algorithms in wireless networks. It includes propaga-
tion models, modules for common node functionality
and documentation.
OPNET [32] is slightly different from NS and Glo-
moS im, it supports the use of modeling different
sensor-specific hardware, such as physical-link tran-
sceivers and antennas. It can also be used to define
custom packet formats. OPNET suffers from the
same object-oriented scalability problems as NS.
SENS [33] is a component-based simulator with four
main components: application, network, physical, and
environment. The former three components make up
the sensor node. SENS is less customizable than any
other simulator, providing no opportunity to change
the MAC protocol, along with other low level net-
work protocols.
EmStar/Em* [34,35] is a software environment for
developing and deploying complex WSN applications
on networks of 32-bit embedded Microserver plat-
Copyright © 2011 SciRes. WSN
forms, and integrating with networks of Motes. EmS-
tar consists of libraries that implement mes-
sage-passing IPC primitives, tools that support simu-
lation, emulation, and visualization of live systems,
both real and simulated, and services that support
networking, sensing, and time synchronization.
EmTOS [34,35] is an extension to EmStar. It can be
used either for deployment or simulation of WSNs. It
enables a complete NesC/TOS app to run unmodified
under EmStar.
SenQ [36] is an accurate and scalable evaluation
framework for sensor networks that integrates sensor
network operating systems with a very high-fidelity
simulation of wireless networks such that sensor
network applications and protocols can be executed,
without modifications, in a repeatable manner under a
diverse set of scalable environments. SenQ extends
beyond the existing suite of simulators and emulators
in four key aspects: it supports emulation of WSN
applications and protocols in an efficient and flexible
manner; it provides an efficient set of models of di-
verse sensing phenomena; it provides accurate mod-
els of both batter y power and clock drift effect wh ich
have been shown to have a significant impact on
sensor network studies; and finally it provides an ef-
ficient kernel that allows it to run experiments that
provide substantial scalability in both the spatial and
temporal contexts.
SIDnet-SWANS [37] is a simulation-based environ-
ment that enables run-time interactions with the net-
work for the purpose of observing the behavior of al-
gorithms protocols in the presence of various condi-
tions such as phenomena fluctuations, or a sudden
loss of service both at an individual node, as well as a
collection of nodes.
SensorSim [38] is a simulation framework that inhe-
rits the core features of traditional event driven net-
work simulators, and builds up new features that in-
clude ability to model power usage in sensor nodes,
hybrid simulation that allows the interaction of real
and simulated nodes, new communication protocols
and real time user interaction with graphical data dis-
Shawn [39] is a discrete event simulator for sensor
networks. Due to its high customizability, it is ex-
tremely fast but can be tuned to any accuracy that is
require d by the simulation or applicatio n.
SSFNet (Scalable Simulation Framework) [40] is a
command-line-based simulator. Accordingly, the rea-
lization of specific application scenarios and the user
interaction is difficult. SSFNet focuses on static ap-
plication scenarios. An important feature of SSFNet
is the possibility to parallelize the simulation. This
speedup enables the analysis of large scale network
behaviour. Both toolkits are limited to a single com-
munication interface per node.
Atarraya [41] simulator is specifically focused on the
evaluation of topology control protocols in WSNs.
NetTopo [42] is a research oriented sensor network
simulator. NetTopo has the functions of general sen-
sor networks but specially reflects the research results
of following: Streaming Data Gathering and Topolo-
gy Prediction in WSNs within Expected Lifetime,
Reward Oriented Packet Filtering Algorithm for He-
terogeneous Sensor Networks, VIP Bridge: Integrat-
ing Several Sensor Networks into One Virtual Sensor
Network, Transmitting Streaming Data in Wireless
Sensor Networks with Holes and many m ore .
WiseNet [43] is a software simula tor that can be very
useful to carefully plan and select the right type of
motes and sensors in a cost-effective manner. WiSe-
Net simulates random distribution of sensors. Through
repeated experimentation it is possible to arrive at an
optimal spatial configuration of the sensors that is
most effective for a given application. WiSeNet also
allows the wireless range of a sensor to be varied and
study the effects on the application.
SimGate [44] is a full-system simulator for the Intel
Stargate, intermediate-level, resource-constrained,
sensor network device.
SimSync [45] is a time synchronization simulator for
wireless sensor networks. SimSync models the dis-
tribution of packet delay and the frequency of crystal
oscillator as Gaussian.
SNetSim [46] is event-driven simulation software for
WSNs running on Windows based operating systems.
SensorMaker [47] is a simulator for wireless sensor
networks. It supports scalable and fine-grained in-
strumentation of the entire sensor networks.
TRMSim-WSN (Trust and Reputation Models Simu-
lator for Wireless Sensor Networks) [48] is a Ja-
va-based simulator aimed to test trust and reputation
models for WSNs.
PAWiS [49] is a simulation framework for WSN that
provides functionality to simulate the network nodes
with their internal structure as well as the network
between the nodes. One main feature is the contem-
poraneous simulation of the power consumption of
every single node. The framework is based on the
discrete event simulator OMNeT++. The user defined
model (expressed with C++ classes) is compiled to an
executable simulator.
OLIMPO [50 ] is a discrete-event s imulator for WSN,
designed to be easily reconfigured by the user, pro-
viding a way to design, develop and test communica-
tion protocols.
Copyright © 2011 SciRes. WSN
DiSenS (Distributed SENsor network Simulation) [51]
is a complete scalable and extensible distributed si-
mulation system for sensor networks, which provides
a cycle-accurate device emulator that is extendable by
various fidelity-enhancing models (radio, power etc.)
for tunable simulation accuracy. A key distinguishing
feature of DiSenS is that it is implemented for distri-
buted-me mory p a rallel cluster syste ms .
WISDOM [52] simulator is written in Java and uses
the recursive porous agent simulation toolkit (Repast)
O as the simulation engine to perform discrete
event-driven simulations. WISDOM can be to simu-
late and verify middleware services f or routing, sens-
ing activity scheduling, group formation and man-
agement, target detection and tracking, and collabor a-
tive classification and fusion in a wireless sensor
network. The versatility and performance of WIS-
DOM for middleware service protocol development
and evaluation have proven to be valuable.
Sinalgo [3] is a simulation framework for testing and
validating network algorithms f or WSN. Unlike most
other network simulators, which spend most time si-
mulating the different layers of the network stack,
Sinalgo focuses on the verification of network algo-
rithms, and abstracts from the underlying layers: It
offers a message passing view of the network, which
captures well the view of actual network devices?
Sensoria [53] is a fully fledged simulator for WSNs
that has considerable differences to all other existing
simulators. Sensoria is very powerful in simulating a
range of small to large scale WSNs based on a simple
and complete Graphical User Interface (GUI) . Senso-
ria's GUI allows users to design various simulation
scenarios and display the simulation results graphi-
cally with many formats. Sensoria is a component-
based simulator and it can be easily reconfigured to
adapt to different levels of simulation details and ac-
Capricorn [54] is a large-scale discrete-event wireless
sensor network simulator developed at Wayne State
H-MAS (a Heterogeneous, Mobile, Ad-hoc Sen-
sor-Network Simulation Environment) [55] provid e a
convenient platform on which to evaluate a variety of
MAS (Mobile A d-hoc Sensor nets) configurations at
the physical, medium access, network, and applica-
tion layers, and to extract meaningful design rules
from the experimental data. Also provide an intuitive
visualization that can give insight to the design engi-
neer and casual observer alike.
Stargate Simulator (starsim) [56] is a full-system si-
mulator for Stargate, the XScale-based gateway de-
vice for WSN. It also boots original Linux image
from xbow. It also features an XScale pipeline simu-
lator to provide cycle estimation.
Mote simulator (mo te si m) [56] is a full-system,
cycle-accurate simulator for Mica-2 and Mica-Z
motes. It runs with original TinyOS binaries.
SNSim [57] is a prototype software tool, designed to
support the balance the lifetime of a WSN and the
quality of data (QoD) that is sampled and processed.
Including elements of power consumption characte-
ristics and built to mimic real performances of Mica
motes (both in data transfer rate and power consump-
tion - on/off modes), this graphical interface tool is
created towards investigating various aspects of de-
velopment, as well as building applications/simu- la-
tions for such networks. Both SNSim and its event
driven simulation engine are written in Java, which
offers an enhanced portability and efficiency of de-
velopment time.
SNIPER-WSNSim [58] is a less known simulator that
is specifically designed for WSNs and benefits from
the richness of the .Net framework 3.5 and from the
portability of the C# language. It is a graphical inter-
face based simulator that deals with particular sector
of WSN development such as sensor nodes distribu-
tion, rout ing protocols a nd clustering.
SNAP [59] is defined as an integrated hardware si-
mulation and deployment platform. It is a micropro-
cessor that can be used in two ways: as the core of a
deployed sensor or as a part of an array of processors
that performs parallel simulation. Again, “real” code
for sensors can be simulated. By combining arrays of
SNAPs (called Network on a Chip), it is claimed to
be able to simulate networks on the order of 100,000
SimPy [60] is a bare simulation written in Python. In
SimPy, the basic simulation entities are processes.
They are executed in parallel and may exchange Py-
thon objects among each other. Most processes in-
clude an infinite loop in which the main actions of the
process are performed. Besides abstractions for
processes and the related exchange of objects, SimPy
provides instructions for the synchronization of si-
mulation processes and commands for the monitoring
of simulation data. Unlike the other simulators, there
is no public available network models exist for it.
Mule [61] is a hybrid simulator that combines the
ease of debugging multiple simulated motes on a host
PC with high fidelity of message transmission and
sensor data acquisition of physical motes.
CaVi [62] provides a uniform interface to state-of-
the-art simulation methods and formal verification
methods for WSN. Due to the probabilistic behavior
of WSNs systems, however, the simulation covers
Copyright © 2011 SciRes. WSN
only a small fraction of all possible behaviors. Formal
model checking techniques, based on Markov Deci-
sion Processes, use less detailed and more abstract
models and compute exact probabilities and expected
values for the entire behavior, where simulation can
only give averages. It allows for creating a single
model for simulation, Monte-Carlo simulation, and
model checking.
Ptolemy [63] is a discrete event simulator and a de-
sign tool for concurrent, real time, embedded systems.
It could be used to simulate WSNs. In fact Visual-
Sense, which is a framework built on top of Ptolemy,
is intended to assist researchers in the design, visua-
lization and simulation of wireless sensor networks.
In Visual Sence sensor nodes could be defined using
either the discrete event blocks or the continuous time
and real time blocks available on Ptolemy. In addition,
the sensor nodes could also be written in Java to meet
specific needs.
Maple [64] is a simulator which allows researchers
and WSNs developers to focus on particular aspects
such as sensor nodes distribution and WSNs lifetime
estimation. Moreover, the parallel processing capabil-
ity presents an important feature for further distri-
buted simulations. Furthermore, the intuitive and
convivial user interface makes the simulator accessi-
ble for the average users while being flexible and
scalable for improvements for advanced users.
WISENES (W ireless Sensor N etwork Simulator) [65]
simulates high level WSN protocol and application
designs and provides accurate information about their
performance in a real environment. The WISENES
framework implements models for transmission me-
dium (for modeling wireless communications), sens-
ing channel (for physical phenomena) and nodes (for
physical node platforms). The designer selects the
protocols from the library or implements new ones in
SDL and integrates them to the WISENES frame-
work. The framework components and node proto-
cols communicate using SDL signals. A node model
can be dynamically instantiated separately for each
simulated node. Thus, virtually any number of nodes
can be simulated simultaneously.
WSNet-Worldsens and WSim [66]: WSNet is an
event driven, large scale wireless sensor network si-
mulator. WSNet uses models for applications, proto-
cols and radio medium communication with a para-
meterized accuracy. WSim can be connected to
WSNet, in place of the application and protocol mod-
els used during the high level simulation to achieve a
full distributed application simulation. WSNet and
WSNet+WSim allow a continuous refinement from
high level estimations down to low level real-time
LSU Sensor Simulator [67] is a framework for simu-
lating WSNs. It is a customizable and extendible si-
mulator, which allows testing and analyzing software
for WSNs. The users can subclass the framework
classes and customize the behavior of various net-
work layers. This sub classing gives a way to the de-
velopers and an opportunity to analyze and investi-
gate phenomenological, networking, robustness and
scaling issues, to explore arbitrary algorithms for dis-
tributed sensors, independent of hardware constraint.
WSNGE [68] is a flexible and extensible environ-
ment that provides a highly scalable simulator with
unique characteristics such as: focuses on user friend-
liness, providing every function in both scriptable and
visual way, allowing the researcher to define simula-
tions and view results in an easy to use graphical en-
vironment. Unlike other solutions, WSNGE does not
distinguish between different scenario types, allowing
multiple different protocols to run at the same time. It
enables rich online interaction with running simula-
tions, allowing parameters, topologies or the whole
scenario to be altered at any point in time.
TikTak [69] is a scalable simulator for W SNs includ-
ing h ard war e/so ftware interaction. Specifically allows
the design exploration and the complete micropro-
cessor-instruction-level debug of network formation,
data congestion, nodes interaction, all in one simula-
tion environment. An innovative feature is the
co-emulation of selected nodes at
clock-cycle-accurate hardware processing level, al-
lowing code debug and exact execution latency eval-
uation (considering both protocol stack and applica-
tion), together with other nodes at abstract protocol
level, meeting a designer’s needs of simulation speed,
scalability and reliability. The simulator is centered
on the Zigbee protocol and can be retargeted for dif-
ferent node micro-architectures.
3. Emulators for Wireless Sensor Networks
As a networked embedded system, a WSN application
involves sensor node hardware, its drivers, operating
systems, and networking protocols. As a result, the per-
formance of the WSN application depends on all of these
factors in addition to its implementation. An emulator is a
special type of simulator whose aims is to enable realistic
perform anc e eval uati on f or WS N appl ica tions . Em ula tion
environm ent or em ulators are go od choice, i n which WS N
applications can be directly run for testing, debugging,
and performance evaluation. Additionally, studies on the
lower layers (e.g., hardware drivers, OS, and networking)
Copyright © 2011 SciRes. WSN
as well as cross-layer techniques can also be done in this
environment by plugging the target modules into the
emulator. For example, emulat or s c an compute t he power
of a particular sensor’s hardware platform in WSNs
Here is a list which presents 14 emulators with their
highly vital features related to WSNs:
VMNET (Virtual Mote Network) [70,71] has a highly
modularized architecture for assembling virtual
hardware components. Target WSN is emulated as a
virtual m ote ne twork. The CPU of a mote (se nsor node)
is emulated at the CPU clock cycle level, and the
sensing units and other hardware peripherals are also
emulated in sufficient detail. The radio signal trans-
mission is emulated by the communication between
VMs with the effects of signal loss and noise. More-
over, VMNet takes parameter values from the real
world and logs detailed running status of application
code. As a result, the binary code of the target WSN
application can be run directly on the VMN, and the
application perfor mance, both in r esponse time and in
power consumption, can be reported realistically in
ATEMU [13] is a sensor network emulator/simu-
lator/debugger for AVR processor based systems.
Along with sup port for the AVR pr ocessor, it also in-
cludes support for other peripheral devices on the
Mica-2 sens o r node platf o rm such as the radio. Atemu
can be used to perform high fidelity large scale sensor
network emulation studies in a controlled environment.
The atemu emulator co re can simulat e arbitrary num-
bers of nodes and can model their execution and in-
teractions between them, such as radio communica-
tions in extremely fine detail. It offers nearly complete
emulation of the Mica-2 hardware platform and as a
result provides results that are closer to real life oper-
ation of a distributed sensor network. The only dif-
ference between running an actual network of the
Mica-2 sensor nodes and emulating it in Atemu is the
operation of the air”.
Ems tar [ 34,35] is a programming model and software
framework for creating Linux-based sensor network
applications that are self configuring, reactive to dy-
namics, and can either be interactively debugged or
operate without user interaction. The goal of EmStar is
to facilitate a more direct interaction with underlying
modules by doing away with strict layering, but in a
way that does not sacrifice very much modularity or
layers of conceptual abstraction. The EmStar execu-
tion environment makes code easier to debug. The
same code and configuration can be run on real nodes
(either using low-power radios such as motes, or
802.11) , a s a pure simulation, or in a hybrid mode that
combines processing done in simulation and commu-
nication, sensing, and actuation on real (physical)
channels. The same source code c an b e used in an y of
these modes without changes. Developers can seam-
lessly iterate between simulation and reality.
TOSSIM [9,10]: Initially, work on TinyOS was very
low level, exploring things such as media access,
sensor filtering, and timer implementations. The initial
design of TOSSIM was focused on this work: it si-
mulates every bit of the Mica platform radio i stack.
As this work was matured, more and more effort has
been spent on higher layers, such as complex applica-
tions. In order to support developers of these larger
systems, TOSSIM currently implementing a packet-
level simulation for the Mica-2 platform. The chal-
lenge is to capture all of the issues and problems that
can arise i n c o m muni ca ti on (t iming, packet c orruption,
MAC) while remaining efficient.
AvroraZ/Avrora [27]: AvroraZ, is an extension of the
Avrora emulator - the AVR Simulation and Analysis
Frameworkwhich allows the emulation of the Atmel
AVR microcontroller based sensor node platforms
with IEEE 802.15.4 compliant radio chips thus al-
lowing emulation of sensor nodes such as Crossbow's
Mica-Z. AvroraZ is based on design, implementation
and verification of several extensions to Avrora such
as address recogn ition algorithm, indoor radio model,
clear channel assessment (CCA) and link quality in-
dicator (LQI) of the IEEE 802.15.4 standard.
Freemote [72] emulator is a lightweight and distri-
buted Java based emulation tool for developing WSN
softwares. The objective of this platform is to support
the emerging Java based Motes based on optimized
JVM (for example, Squawk, Sentilla Point) and plat-
forms (for e xam ple, Java C ards, SunSP OT). T he Free-
mote emulator focuses on behavior credibility by
mixing emulated nodes and real nodes reachable
through a specialized bridge rather than on time based
performance evaluation accuracy. This emulator splits
the Software architecture of a Mote in three indepen-
dent layers connected through well defined interfaces
(Application, Routing and Data Link and Physical).
Freemote is a fully configurable WSNs emulator. It
can easily be used to develop new algorithms for
WSNs but is also capable to support large scale expe-
riments (up to 10,000 nodes) including all kind of real
nodes based on the IEEE 802.15.4 communication
standard. It also allows the developer following the
behavior of WSNs and debugging tricky impleme n ta-
tion pro bl e ms.
EmPro [73] is an environment/energy emulation and
profiling system for WSNs. It accurately outputs
electrical signals to emulate not only digital and ana-
Copyright © 2011 SciRes. WSN
log inputs to the sensors but also the power sources as
well as RF attenuation according to pre-programmed
sequences. This emulation approach enables re-
searchers to run the networke d sensors i n real-time in a
realistic manner with full contr ollability and reprodu-
cibility. EmPro in profiling mod e can also capture the
observable behavior of WSNs for detailed analysis.
Experimental results on the Eco and Mica-2 WSN
platforms show that EmPro can drive these hardware
systems in real-time with high accuracy.
NetTopo [42]: With respect to the simulation module,
users can easily define a large number of on-demand
initial parameters of sensor nodes, e.g. residential
energy, transmission bandwidth, and radio radius.
Users also can define and extend the internal
processing behavior of sensor nodes, such as energy
consumption, bandwidth management. It allows users
to simulate extremely large scale heterogeneous net-
works. Since the sensor node attributes and internal
operations are user definable, this feature guarantees
that the simulated virtual nodes have the same prop-
erties with those of real sensor nodes. The sensed data
captured from the real sensor nodes can drive our si-
mulat ion in a p re-deployed vi rtual WSN. A dditionally ,
topology layouts and algorithms of virtual WSN are
customizable and work as user-defined plug-ins, both
of which can easily match the corresponding topology
and algorithms of real WSN testbed.
OCTAVEX [74] wireless sensor framework is de-
signed to assist end users, systems integrators, soft-
ware developers, and OEMs (Original Equipment
Manufac ture rs) i n the depl oy ment and m anagement of
WSNs. The framework provides a backbone for WSN
applications while taking a n a pproa ch t hat i s ha rdwa re
and stan dards agnosti c, allowing t he user to im plement
an end to end solution quickly, easily and at a much
lower cost than developing one in-house. It is able to
simultaneously support any number of sensor points
using different types of wireless protocols (including
mesh networks like Zigbee, Wireless HART, point to
point RF sensors, Bluetooth, WiFi, RFID tags). The
framework captures incoming sensor data at the
OCTAVEX Universal Gateway. Once the data comes
into the framework, the OCTAVEX Core Services
provide built in business logic such as archiving, re-
porting, alerting and trending. Through Web Services
and softwa re APIs, t he OCTAVEX F ramework ca n be
easily integrated into other enterprise applications,
building autom ation systems, or industrial process and
control systems.
SENSE [17] does not support sensor’s physical phe-
nomena, or environmental effects. Overall, the MAC
protocol support and radio propagation make SENSE
less than ideal for accurate evaluation of WSNs re-
UbiSec&Sens [75] will also prototype implementa-
tions on emulators and on actual sensor networks.
Emuli [76] is a method of effectively substituting
sensor data by synthetic data on physical wireless
nodes (motes). That is, a method of emulating sensor
stimuli of sensors. Emuli implements a model of a
sensor behavior. In contrast to the earlier ap-proaches,
does not record and play back spot measurements. In-
stead, Emuli stores the model parameters. This results
in a rather compact data memory footprint and a
convenient and flexible sensor model. Emuli is de-
signed to increase the capability of sensor testbeds and
other de-ployments to experiment with environment
sensing and monitoring.
MSPSim [77] is a Java-based instruction level emu-
lator of the MSP430 series microprocessor and emu-
lation of some sensor networking platforms. Supports
loading of IHEX and ELF firmware files, and has
some tools for monitoring stack, setting breakpoints,
and profiling.
MEADOWS [78] is a software framework for mod-
eling, emulation, and analysis of data of wireless
sensor networks. This software framework is moti-
vated by the unique need of intertwining modeling,
emulation, and data analysis in studying sensor data-
4. Data Visualization Tools for Wireless
Sensor Networ ks
With the increase in applications for sensor networks,
data manipulation and representation have become a cru-
cial component of sensor networks. The data gathered
from WSNs is usually saved in the form of numerical
form in a central base station. There are many programs
that facilitate the viewing of these large amounts of data.
These special programs are called data visualization tool
for WSNs. Visualization tools can support different data
types, and visualize the information using a flex ible mul-
ti-layer mechanism that renders the information on a
visual canvas.
Here is a list presenting 19 data visualization tools [79]
that are especially designed and developed for WSNs
SpyGlass’s [80] aim is to ease the life for sensor
network debugging, evaluation and deeper under-
standing of the software by visualizing the sensor
network, its topology, the state and the sensed data.
SpyGlass has a very flexible drawing and plug-in ar-
chitecture. The visualization framework consists of
three major functional entities: The sensor network,
Copyright © 2011 SciRes. WSN
the gateway nodes located in the sensor network and
the visualization software.
MoteView [81] monitoring software is a Crossbow’s
product to visualize WSNs which provides users to
simplify deployment and monitoring. It also makes it
easy to connect to a database, to analyze, and to graph
sensor readings. The Mote-view features topology
and network statistics visualiz ation as well as logging
of sensor readings and the viewing of the logged data.
The statistics function includes the end-to-end data
packet yield, a prediction for the future and the RF
link quality, but is limited to these features. It allows
querying the sensor network for collected data in a
database-like manner, hiding the distribution of the
data collection software on the sensor nodes.
TinyViz [9,10] is not just a visualization tool but a
software framework to which application specific us-
er plugins can be added to suite specific simulation
requirements. It visualizes sensor readings; LED
states; radio links and allows direct interaction with
running TOSSIM simulations. The architecture of
TinyViz allows adding application specific visualiza-
tion functionality. This functionality includes specia-
lized drawing op erations, subscription and reaction to
events and providing feedback to the TOSSIM simu-
lator. It is very tightly coupled to the TinyOS soft-
ware, the TOSSIM simulator and the Mica sensor
network hardware.
Surge Network Viewer [76] is Crossbow’s product to
visualize wireless sensor networks. It is a Java appli-
cation that comes standard in the TinyOS Tools dis-
tribution. The Surge Network Viewer is useful for
monitoring a sensor network and analyzing mesh
network pe rformance.
MonSense [82] applications are very modular and
have various extension points. It reuses various soft-
ware libraries in order to reach the intended beha-
viour. The MonSense application displays the exist-
ing connections (routes) as an undirected graph,
whose nodes are the sensor devices and edges are the
current connections. MonSense can be used for dif-
ferent goals like planning, deployment, monitoring
and control of WSNs. This application is intended to
serve two different types of users: WSN Customers
and WSN Researchers. The gathered data must be
easily understood by the final users and, optionally,
this data can be published in the intern et allowing the
access to the information without the need to any
previous software installation, through the use of html,
plain text or images.
NetTopo [42] is an extensible integrated framework
for the Simulation of virtual WSN, the visualization
of real testbed, and the interaction between simulated
WSN and testbed to assist investigatio n of algorithms
in WSNs.
Octopus [83] is also a WSN Visualization and Con-
trol tool. Its main Objective is to provide flexible
access and c o ntrol of deployed sensor networks.
TOSGUI [82] project is composed of modular com-
ponents that can be used to create a customized ap-
plication. Unfortunately, the component architecture
is tightly connected with the TinyOS operating sys-
tem and the MOTE hardware platform.
MSR Sense [84] project is also able to collect data
from a WSN and visualize it, but the visualization
can’t be done in real time and the software is not
platform independent.
Trawler [85]: The Trawler application from MoteIV
is well suited for monitoring small sized WSNs but,
as the size increases the current network state be-
comes less obvious.
SNAMP (self-developed Sensor Network Analysis
and Management Platform) [86] is a novel multi-sni-
ffer and multi-view visualization p latform for WSNs.
In SNAMP, data emitted by individual sensor nodes
is collected by a multi-sniffer data collation network
and passed to a flexible multi-view visualization me-
chanism. SNAMP indicates network topology, sens-
ing data, network performance, hardware resource
depletion, and other abnormalities in WSNs and al-
lows developers adding application specific visuali-
zation functions, wh ich will facilitate the research and
development of various sensor networks and shorter
the time from laboratory to applications.
MeshNetics WSN Monitor [87] tool shows the net-
work topology, sensor data and the signal quality
between the nodes. The WSN Monitor automatically
generates network topology diagrams as network
nodes are detected and added to the system. These
nodes are then regularly monitored, with any sensor
data received automatically displayed in charts and
tables on a PC screen. MeshNetics WSN Monitor
features an XML-based framework for rapid custo-
mization of user interfaces and measured sensor pa-
Mica Graph Viewer [88] is a 2D visualization and
monitoring tool for WSN.
MARWIS [89] is a management architecture for he-
terogeneous WSNs, which supports common man-
agement tasks such as visualization, monitoring,
(re)configuration, updating and reprogramming. It
uses a wireless mesh network as a backbone and of-
fers mechanisms for visualization, monitoring, recon-
figuration and updating program code. Using a
graphical user interface, the topology of the hetero-
geneous WSN with all the sensor sub-networks is vi-
Copyright © 2011 SciRes. WSN
Oscilloscope [90] tool is also used to show the sensing
data graphical ly on host screen and visua l izing tool for
the nodes.
GSN [91] is a software middleware for a variety of
WSNs. It facilitates the viewing of large amount of
data that is gathered form WSNs and saved in the
form of numerical data in a central base station.
WiseObserver [92] tool visualizes and analyzes data
collected by a WSN in a generic scope of application.
It also tries to establish a sensor network control in-
terface. The tool will include several facilities to treat
sensor network data. It allows the generation of evo-
lution charts, interpolation maps, evolution data vid-
eos, and report generation. It also includes modules to
add external data not collected by nodes, but related
to the network conditions. Node Management will be
possible thanks to the execution of commands in
network nodes, to perform changes in network opera-
SenseView [93] is a tool that en ables hierarchical and
visual browsing of physical location information and
sensor values. Visual maps can be created by com-
posing polygons, each with the ability to link to a dif-
ferent view. Access to real-time data is provided by
directl y s ubs c ri bing to event nodes c a pt ured as links in
the map. The event nodes also provide attribute in-
formation describing the sensors. Map information is
fetched from a dedicated map server with its own
access control lists based on SOX authentication.
Much like a web browser with hyperlinks, SenseView
allows a user to traverse through different views by
clicking on different parts of the map. The user can
select and subscribe to available event nodes given the
correct permissions.
XbowNet [94] is a CrossBow’s sensor network visua-
lization tool for xbow sensor nodes. A corresponding
software driver called xServe is installed on gateway
for the purpose of converting sensed data into XML
stream and providing a TCP/IP service on port 9005,
which can be used for visualization.
5. Testbeds for Wireless Sensor Networks
To achieve high-delity in WSN experiments use of
testbed is very prolific. Testbeds are an environment that
provides support to measure number of physical para-
meters in controlled and reliable environment. This en-
vironment contains the hardware, instrumentations, si-
mula t ors, various software and other support elements
needed to conduct a test. Generally, testbeds allow for
rigorous, transparent and replicable testing. Obstacles to
using testbeds are:
Large Scale (LS): Until today, due to limited financial
support it is very expensive to buy and maintain a
testbed with large num be r of s e nsor nodes.
Not Replicable Environment (NRE): For hazardous
applications deploying a real testbed can cause se-
rious damage of sensor nodes and testbeds.
By providing the realistic env ironments for testing the
experiments, the testbeds bridge the gap between the
si mul ation and deployment of real devices. The testbeds
thus deployed can improve the speed of innovation and
productive research.
Here is a list presenting 28 testbeds in highly conclu-
sive manner used for experimental purposes in various
universities, colleges, research institutions or by ind ivid-
Motelab [95] is an experimental WSN deployed in
Maxwell Dworkin Laboratory, the Electrical Engi-
neering and Computer Science building at Harvard
University. MoteLab consists of a set of permanent-
ly-deployed sensor network nodes connected to a
central server which handles repr ogramming and data
logging while providing a web interface for creating
and scheduling jobs on the testbed. MoteLab accele-
rates application deployment by streamlining access
to a large, fixed network of real sensor network de-
vices; it accelerates debugging and development by
automating data logging, allowing the performance of
sensor network software to be evaluated offline. Ad-
ditionally, by providing a web interface MoteLab al-
lows both local and remote users access to the testbed,
and its scheduling and quota system ensure fair shar-
ing. The MoteLab source is freely available, easy to
install, and already in use at several other research in-
Tutornet: A Tiered Wireless Sensor Network Testbed
[96] currently c onsists of 13 clusters, with each clus-
ter consisting of a stargate and several motes attached
to it via USB cables. These stargates communicate
with a central PC over 802.11 b, from where any
node on the testbed can be programmed. Thus a
testbed consisting of 13 stargates and 104 motes (91
tmoteSky and 13 Mica-Z). Tiered sensor network
testbed: consists of 3 tiers. It provide remote and pa-
rallel programming mote.
WUSTL [97] testbed at Washington University
currently consists of 79 wireless sensor nodes (motes).
This testbed deployment is based on the TWIST
architecture originally developed by the telecommu-
nications group (TKN) at the Technical University of
Berlin. It is hierarchical in nature, consisting of three
different levels of deployment: sensor nodes, micro-
servers, and a desktop class host/server machine.
CitySense [98] is an urban scale sensor network
testbed that is being developed by researchers at
Copyright © 2011 SciRes. WSN
Harvard University and BBN Technologies. City-
Sens e will consist of 100 wireless sensors will cover
the city of Cambridge, MA, with wireless-sensor
nodes mounted to telephone poles that could allow
researchers to see the specific locations and times of
day when pollution peaks. Each node will consist of
an embedded PC, 802.11 a/b/g interfaces, and vari-
ous sensors for monitoring weather conditions and
air pollutants. Most importantly, CitySense is in-
tended to be an op en te stbed that researchers from all
over th e world can u se to evalu ate wireles s network-
ing and sensor network applications in a large-scale
urban setting.
Kansei [99] at the Ohio State University is a
large-scale testbed including both 210 Extreme Scale
Motes (XSM) and Extreme Scale Stargates (XSS).
The devices are specially designed for Kansei testbed.
The topology is using both Ethernet and 802.11b
wireless LAN to control the testbed. Kansei also pr o-
vide a web interface for users to upload programs,
scheduled jobs, and retrieve results with EmStar
software framework.
MistLab [100] consists of a mixture of 47 Mica-2
nodes and 14 Cricket nodes spread across multiple
rooms located on the 9th floor of MIT’s CS depart-
Orbitlab [101] is short for Open-Access Research
Testbed for Next-Generation Wireless Networks (in-
cluding WSN also). It supports experimental resear ch
on a broad range of wireless networking issues and
application concepts with various network topologies
and network layer protocol options. It also supports
virtual mobility for mobile network protocol and ap-
plication research.
Emulab [102] is a network emulation testbed, giving
researchers a wide range of experimental environ-
ments in which to develop, debug, and evaluate their
systems. In addition to fixed wireless nodes (currently
predominantly 802.11), Emulab also features wireless
nodes attached to robots that can move around a
small area. These robots consist of a small body
(shown on the right) with an Intel Stargate that hosts
a mote with a wireless network interface. The goal of
this mobile wireless testbedis to give users an op-
portunity to conduct experiments with wireless nodes
that are truly mobile. TrueMobile and Mobile Emulab
are some modified versions for dynamic WSNs.
WISEBED (Wireless Sensor Network Testbeds) [103]
provides a multi-level infrastructure of interconnected
testbeds of large-scale wireless sensor networks for
research purposes, pursuing an interdisciplinary ap-
proach that integrates the aspects of hardware, soft-
ware, algorithms, and data.
REALnet [104] is an embryonic environmental WSN
at the “Campus del Baix Llobregat” of the UPC (U n-
iversitat Politècnica de Catalunya). The technical ob-
jective of REALnet is to monitor ph ysical parameters
from the air (atmospheric temperature, humidity and
pressure, and ambient light), ground (humidity, tem-
perature) and water (level, temperature, conductivity).
KonTest [105] is a 60-node indoor wireless sensor
network testbed, distributed among six office rooms
located on the fourth floor of the Faculty of Sciences
of Vrije Universiteit Amsterdam. The testbed in-
cludes 60 TelosB -class nodes.
SANDbed (Sensor Actuator Network Development
Testbed) [106] is an integrated testbed system for
WSN monitoring and management. SANDbed con-
sists of 3 levels of hardware components organized in
a hierarchical tree. The root level comprises the user
interface, where the management of the testbed and
configuration of the experiments is taking place.
Management nodes connected to the Internet form the
second level. They are responsible for managing the
testbed nodes and controlling the execution of expe-
riments. The leaves of the tree are the testbed nodes,
consisti ng of a mote and the SNM D.
BANAID [107] consists of seven Mica-2 motes and
two Stargate sensor devices. It is the first actual
testbed that shows the visibility of the Wormhole at-
tack in WSN and mainly used to simulate the worm-
hole attack on a wireless sensor network.
CENSE (a Century of Sensor nodes) [108] providing
flexible modular platfor m for testing and optimization
of nodes for Sensor Network applications. `CENSE'
currently provides for processing, communication,
sensing and power modules but the design can be
easily extended to add more modules like mobility
and localization. This test bed is very flexible, cost
efficient, easy to use, power efficient, provides ex-
cellent debugging facilities and covers all major re-
quirements of sensors networks. Each node of the
testbed consists of four modules: Power, Processor,
Sensor and Communication. System has been design
to accommodate more modules , i f ne eded.
WINTeR (Wireless Industrial Sensor Network
Testbed for Radio-Harsh Environments) [109] is an
open access, multi-user experimental facility (MXF)
that supports the development and evaluation of
wireless sensor networks (WSNs) for radio-harsh en-
vironments (RHEs). The testbed supports the R&D of
emerging WSN technologies, including protocols,
security, physical layer, the validation of wireless so-
lutions for industrial processes, propagation models,
and cross-layer optimization.
NESC-Testbed [110] 1.0 provides an actual platform
Copyright © 2011 SciRes. WSN
for the testing and developing of algorithm, protocol
in WSN. B/S 3-tier and wireless-wired combined
framework are used in this testbed, which makes the
system user-friendly, easily used, robust and stable.
SWOON (Secure Wireless Overlay Observation Net-
work) [111] is an emulation-based testbed for real
world experiences and scalable tests over an overlay
network, consisting of wireless sensor networks,
802.11 a/b/g, etc. It can evaluate protocols, mechan-
isms and techniques for secure wireless communica-
tion. Researchers and designers can create their own
topologies and run experiments on the SWOON
testbed without re-establishing and re-installing
hardware and software modules required for their
wireless networks. In addition, the SWOON testbed
also allows researchers to monitor the network traffic,
evaluate the performance of the protocols under test
and validate the researches they presented.
INDRIYA [112] is a large-scale 3D WSN testbed
with 140 TelosB nodes deployed at the National
University of Singapore. The Testbed facilitates re-
search in sensor network programming environments,
communication protocols, system design, and applica-
tions. It provides a public, permanent framework for
development and testing of sensor network protocols
and applications. Users can interact with the Testbed
through an intuitive web-based interface designed
based on Harvard's Motelab's interface.
CLARITY [113] Centre for Sensor Web Technology
in Ireland is currently constructing a ubiquitous ro-
botics testbed by integrating a collective of mobile
robots with a WSN and a number of portable devices.
The new, mixed testbed will be hosted at University
College Dublin, (UCD), and will also avail itself of
the laboratory facilities hosted in Dublin City Univer-
sity (DCU) and Tyndall, Co rk. This testbed integrates
and extends some pre-existing facilities, specifically:
WSN of 70 Berkeley motes measuring humidity, ligh t
and temperature, 10 mobile robots, equipped with an
array of state-of-the-art sensors, including USB cam-
eras, laser range finders, sonar, infrared, odometers
and bumpers. Each robot carries a mote able to
measure ambient variables, which is also equipped
with triple-axis accelerometers, magnetometer, com-
pass and microphone, a variable number of Internet
gateways, a variable number of PDAs and mobile
phones equipped with Bluetooth.
Imote2 Sensor Network Testbed [114]: In its current
version, the testbed consists of a set of Crossbow Im-
ote2 nodes programmed with a Linux kernel and run-
ning localization and routing codes w ritten in C. One
node is connected to a PC via USB and acts as a base
node for the network. The PC runs a Ja va-based GUI
intended as an interface for a user to read data from
the nodes and to issue commands to the network. The
user can control and observe the performance of var-
ious localization protocols in the network which are
run locally in the Linux operating system on each de-
WSNTB [115] is designed for heterogeneous WSN
experiments. It involves two WSNs and three gate-
ways. Each WSN has 17 sensor nodes. According to
users’ requirements, users can choose the single one
or both of WSNs, with or without the gateways to
experiment. Users can use both the web-based inter-
face and the special function, called local mode, to
run their applications on testbed.
TWIST [116] testbed is owned by Technical Univer-
sity Berlin. They help users load programs and run
experiments such as time synchronization and power
control. The system is divided into two major parts.
The first part is the server to serve the demands of
users and control all of nodes. The second part in-
cludes two types of sensor nodes, eyesIFX v2 and
Telos motes which are plugged onto the switch. The
architecture is extended form the UC Berkeley’s
Omega testbed and Motescope testbed.
ENL Sensor Network Testbed [117] is intended to
provide a multi-hop sensor network that could be
used for the real time analysis and evaluation of sen-
sor network application. The ENL sensor network
testbed consist of a number of mote assemblies
hanging from the ceiling forming a grid pattern. Each
mote assembly consists of a Mica mote and the stan-
dard programming board. The testbed provides means
of remotely programming the motes and collecting
data from the testbed.
X-sensor [118] is a new sensor network testbed
integrates multiple sensor networks deployed at
different sites. X -sensor provides three functionalities:
(a) a sensor network search which enables users to
find a sensor networks appropriate for experiment
and data acquisition, (b) a sensor data archive which
provides users with various sensor data acquired by
sensor nodes, and (c) an experimental testbed which
enables remote users to evaluate their network and
data management protocols.
GNOMES [119] is a lowcost hardware and software
testbed. This testbed was designed to explore the
properties of heterogeneous wireless sensor networks,
to test theory in sensor networks architecture, and be
deployed in practical application environments.
PICSENSE [120] is a single hop WSN testbed which
will send the sensor information to the gateway node.
The gateway node will act as an embedded web serv-
er which serves the web pages with the dynamic data.
Copyright © 2011 SciRes. WSN
Most of the testbed uses commercial gateway nodes
like stargate which is not as flexible as the Rab bit ga-
teway node, which is used in this testbed to integrate
the WSNs with the IP networks. The firmware in the
Rabbit gateway node can be designed to make the
configuration of the gateway node either as a HTTP
server or a FTP server or a simple router.
SOWNet [121] Technologies T301 Testbed is pri-
marily a WSN testbed cons isting of SOWNet G-Node
G301 wireless sensor nodes. Each sensor node is at-
tached to a G-Node testbed adaptor module for con-
necting the sensor emulation feature. Up to 4 G-
Nodes and GTA301 modules can be interfaced to a
single mini PC. The mini PC is connected to an IP
network using its wired or wireless networking capa-
bility. This allows many mini PCs and a vast number
of G-Nodes to form a WSN testbed together and still
be managed from a single management console, pos-
sibly over the internet.
NetEye [122] is a h igh-fid elity testbed cons ists of 130
TelosB motes at Wayne State University. In addition
to providing a local facility for supporting research
and educational activities, NetEye is be ing connected
to Kansei as a part of the Kansei consortium. NetEye
testbed consists of a controlled indoor environment
with a set of sensor nodes and wireless nodes dep-
loyed permanently. NetEye testbed provides a web
interface to create and schedule a job on the testbed
while automated reprogramming of the sensor devic-
es and storing the experimental data on to the server.
In addition to above discussed 28 WSN testbeds there
are also a number of other academic and industrial
testbed deployments. Some of these WSN testbeds are:
SenseNet [123], Omega [124], Motescope [124], Share-
sense [125 ], Trio [126], sMote [127], CTI-WSN Testbed
[128], FEEIT WSN Testbed [129], Roulette [130], Big-
Net [131], UCR Wireless Networking Research Testbed
[132], IP-WSN [133], WHYNET [134], CENS-Testbed
[135], SCADDS WSN Testbeds [136], Crossbow WSN
Testbed [5], GaTech Testbed [137], Intel Research
Berkeley’s 150-mot e SensorNet Te s tbed [138].
6. Debuggers for Wireless Sensor Networks
Due to extreme resource constraints nature, deployment
in harsh and unattended environments, lack of run-time
support tools and limited visibility into the root causes of
system and application level faults make WSNs noto-
riously difficult to debug. Currently, most debugging
systems in WSNs are aimed at diagnosing specific faults,
such as detection of crashed nodes, sensor faults, or
identifying faulty behavior in nodes. There are few de-
bugging solutions for WSNs available, with a fairly wide
range of goals and feature sets. Debuggers for WSNs
have been categorized [139] into three distinct catego-
Source-level debuggers
Query-oriented debuggers, and
Decision-tree debugg e rs.
Here is a list presenting 26 debuggers and debugging
concepts with their summarized content related to
Clairvoyant [140] is comprehensive source-level de-
bugger for wireless, embedded networks. With
Clairvoyant, a developer can wirelessly connect to a
sensor network and execute standard debugging
commands including break, step, watch, and back
trace, as well as new commands that are specially de-
signed for debugging WSNs. Clairvoyant attempts to
minimize its effect on th e program being debugg ed in
terms of network load, memory footprint, execution
speed, clock consistency, and flash lifetime.
Dustminer [141] is a tool for uncovering bugs due to
interactive complexity in networked sensing applica-
tions. Such bugs are not localized to one component
that is faulty, but rather result from complex and un-
expected interactions between multiple often indivi-
dually non-faulty components. Because of the distri-
buted nature of failure scenarios, this tool looks for
sequences of events that may be responsible for faulty
behavior, as opposed to localized bugs such as a bad
pointer in a module. With this tool an extensible
framework is developed where front-end collects run-
time data logs of the system being debugged and an
offline back-end uses frequent discriminative pattern
mining to uncover likely causes of failure. The tool
helped uncover event sequences that lead to a highly
degraded mode of operation. Fixing the problem sig-
nificantly improved the performance of the protocol.
Sympathy [142] is a tool for detecting and debugging
failures in sensor networks. Sympathy has selected
metrics that enable efficient failure detection, and in-
cludes an algorithm that root-causes failures and lo-
calizes their sources in order to reduce overall failure
notifications and point the user to a small number of
probable causes.
FIND [143] is a novel method to detect nodes with
data faults that neither assumes a particular sensing
model nor requires costly event injections. After the
nodes in a network detect a natural event, FIND ranks
the nodes based on their sensing readings as well as
their physical distances from the event. It works for
systems where the measured signal attenuates with
distance. A node is considered faulty if there is a sig-
nificant mismatch between the sensor data rank and
the distance rank.
Copyright © 2011 SciRes. WSN
REDFLAG [144] is the fault detection service for
WSN applications, a Run-timE, Distr ibuted, Flexible,
detector of faults that is also Lightweight and Generic.
REDFLAG addresses the two most worrisome issues
in data-driven wireless sensor applications: abnormal
data and missing data. REDFLAG exposes faults as
they occur by using distributed algorithms in order to
conserve energy. Simulation results show that RED-
FLAG is lightweight both in terms of footprint and
required power resources while ensuring satisfactory
detection and diagnosis accuracy. Being unrestrictive,
REDFLAG is gen erically available to a myriad of ap-
plications and scenarios. As a matter of fact, RED-
FLAG has been applied into a subsurface contaminant
transport model to improve the model performance in
the presence of erroneous sensor data.
Chowkidar [145] is a stabilizing protocol that pro-
vides accurate and efficient network health monitor-
ing in WSNs. This approach adapts the well-known
problem of message-passing rooted spanning tree
construction an d its use in propagation of infor mation
with feedback (PIF) for the case of a WSN. The
Chowkidar protocol is initiated upon demand; that is,
it does not involve ongoing maintenance, and it ter-
minates with accurate results, including detection of
failure and restart during the monitoring process.
Chowkidar is distinguished from others in two im-
portant ways. Given the resource constraints of
WSNs, it is message-efficient in that it uses only a
few messages per node. Also, it tolerates ongoing
node and link failure and node restart, in contrast to
requiring that faults stop during convergence. Chow-
kidar protocol has been implemented as part of
enabling a network health status service that is tightly
integrated with a remotely accessible wireless sensor
network testbed, Kansei, at the Ohio State University.
ActorNet [146] is an agent based framework for dy-
namically programming and debugging WSNs. end-
users (WSN operators but not necessarily program-
mers) define actors in an expressive, high-level lan-
guage to specify debugging logic. The framework al-
lows actors to move through the network in order to
accomplish their objective.
Debugging WSNs Using Mobile Actors [147] ap-
proach is for post-mortem debugging of WSNs using
autonomous and mobile actors. By allowing the
computation (mobile actor) to move to the nodes
where the data is located, to overcome the necessity
of moving the data while still providing the flexibility
necessary to diagnose errors in WSNs. In this ap-
proach two mechanisms are defined for debug-
gingnamely, forward tracking and backward track-
ing in which an actor, starting at an error state, tracks
the causal events, respectively, forward or backward
in time in order to determine the root cause of the er-
Monitored External Global State (MEGS) [14 8] [149 ]
is a tool that leverages existing debugging techniques
to recreate (part of) the global state of a WSN on an
external PC. A global state is the combined local
states of all nodes in a system. The idea is that the
global state of a system does not only allow a devel-
oper to see what is happening inside a WSN at a giv-
en time, but also relevant events that happened earlier.
Using the recreated state the developer of a WSN can
gain insight into the operation of the WSN. MEGS
also allows the developer to define assertions and
predicates on the recreated state to easily find lo ca-
tions in the execution where anomalous behavior oc-
Declarative Tracepoints [150] is a debugging system
that allows the user to insert a group of action-asso-
ciated checkpoints, or tracepoints, to applications be-
ing debugged at runtime. Tracepoints do not require
modifying application source code. Instead, they are
written in a declarative, SQL-like language called
TraceSQL independently. By triggering the associated
actions when these checkpoints are reached, this sys-
tem automates the debugging process by removing
the human from the loop. Declarative tracepoints are
able to express the core functionality of a range of
previously isolated debugging techniques, such as
EnviroLog, Nod e MD, Sympathy, and StackGuard.
Envirolog [151] is a distributed service that improves
repeatability of experimental testing of sensor net-
works via asynchronous event recording and replay.
To use EnviroLog, an application programmer needs
only to specify two types of simple annotations to the
source code. Automatically, the preprocessor embeds
EnviroLog into any desired level of an event-driven
architecture. It records all events generated by lower
layers and can replay them later to upper layers on
NodeMD [152] is a fault management system de-
signed to improve node debugging capabilities prior
to deployment, and enable remote debugging on
in-situ sensor nodes that fail. This system successfully
implements lightweight run-time detection, logging,
and notification of software faults on wireless mote-
class devices. It introdu ces a debug mode that catches
a failure before it completely disables a node and
drops the node into a state that enables further diagno-
sis and corre c tion, thus avoiding on-site redeployment.
It offers simple annotations to trace and avoid critical
errors in s pe c ific parts of the c ode.
Copyright © 2011 SciRes. WSN
StackGaurd [153] is a more generic tool for detecting
stack corruption caused by buffer overruns (e.g.,
when the return address of a fun ction is over-written).
This problem is also known as the buffer overrun se-
curity exploit. Having received intensive attention,
this problem is addressed in multiple ways, and
StackGuard is one of the better-known techniques in
that it virtually eliminates all buffer overflows with
the help of the canary word. More specifically,
StackGuard modifies the generated prologue and ep-
ilogue code for functions to insert canary words. The
assumption held by StackGuard is that if some code
in a function modifies the return address, it must have
modified the canary word as well, assuming that the
application does not know the value and size of the
canary word. By checking the integrity of the canary
word, StackGuard can detect malfunctioning code.
KleeNet [154] is a debugging environment for high-
coverage testing of sensor network applications be-
fore deployment. It enables the detection of bugs that
result from complex interactions of multiple nodes,
nondeterministic events in the network, and unpre-
dictable data inputs. KleeNet executes unmodified
sensor network applications on symbolic input and au-
tomatically injects non-deterministic failures. As a re-
sult, KleeNet generates distributed execution paths at
high-coverage, including low-probability cornercase
situations. Built on the symbolic virtual machine
KLEE, KleeNet makes the following four key contri-
butions and facilitates rigorous testing of distributed
WSN applications and protocols: Coverage, Non-de-
terminism , Di stribut ed Ass ertions and Repeat ability.
Marionette [155] is a system that allows calling fun c-
tions and inspecting and changing memory locations
in deployed nodes through an RPC-based system. A
developer can use this much like a debugger, al-
though because it uses the wireless channel of the
network it has a large impact on the operation of the
Passive Distributed Assertions (PDA) [156] allows
developers to detect such failures and provides hints
on possible causes. PDA allow a programmer to for-
mulate assertions over distributed node states using a
simple declarative language, causing the sensor net-
work to emit information that can be passively col-
lected (e.g., using packet sniffing) and evaluated to
verify that assertions hold. This passive approach al-
lows us to minimize the interference between the ap-
plication and assertion verification. Further, this sys-
tem provides mechanisms to deal with inaccurate
traces that result from message loss and synchroniza-
tion inaccuracies.
Nucleus [157] network management system (NMS)
facilitates monitoring of running WSN applications
by providing access to the internal data structures of
TinyOS nesC components over the network. To re-
duce interference of the Nucleus system and the WSN
application, data is only sent over the network in re-
sponse to a user query. In addition to this query ap-
proach, unexpected events are logged to persistent
local storage and can be retrieved later from the nod e
on demand.
MDB [158] is a GDB like post-mortem debugging
system for wireless embedded networks that exploits
macro programming to provide four abstract views of
a system state: 1) the temporally synchronous view , 2)
the logic ally synchronou s view, 3 ) the historical view,
and 4) the hypothetical view. It enables application
development and debugging at a single level of ab-
straction. It eliminates the need for a programmer to
reason about low-level event traces and message
passing protocols, instead allowing debugging in
terms of abstract data types.
SNTS (Sensor Network Troubleshooting Suite) [159]
uses distributed sniffer sensor nodes that record
overheard traffic in local Flash storage. After an ex-
periment, the nodes are collected and the packet trac-
es are transferred to a central server. SNTS decodes
the raw packet dumps based on a text file that de-
scribes the packet format. As an example for a p ossi-
ble processing of the packet traces, the authors em-
ployed machine-learning algorithms to identify bad
sequences of events, which lead to an observed bug in
the protocol/system, allowing them to fix the prob-
ANDES [160] is a framework for detection and find-
ing the root causes of anomalies in operational WSNs.
The key novelty of ANDES is that it correlates in-
formation from two sources: one in the data plane as
a result of regular data collection in WSNs, the other
in the management plan implemented via a separate
routing protocol, making it resilient to routing ano-
maly in the data plane. Unlike existing WSN diagno-
sis tools, it does not distinguish among faults due to
software or hardware bugs and those induced by se-
curity threats or intrusion. Rather, it utilizes specifi-
cations of the targeted application known apriori and
normal behaviors established by a self-learning algo-
rithm to identify potential anomalies. Localizing the
faulty entities is made possible by incorporating in-
ferred knowledge of routes and fault signatures on a
central node (typically the sink).
EvAnT [161] allows for specifying queries that are
executed on the collected traces. EvAnT is speci-
cally tailored to WSN testing and debugging.
Copyright © 2011 SciRes. WSN
Storage-centric method for Debugging [162]: In this
method performance and log data are stored locally
on the node. This method opens new possibilities for
debugging: A node can process the data itself;
Neighbors can share performance and log data to
detect root causes of problems and maybe even re-
solve problems; An operator can in an efficient
one-hop batch download the data using a mobile node;
A node can decide on its own when it is time to send
stored log data to the sink for processing; The sink
can request data on demand at the desired level of
Model-based diagnosis for WSNs [163] is a tech-
nique where a model of a system is combined with
observations from that system, to generate diagnoses
for failures of the system. The basic premise of this
solution is to use the distributed nature of a WSN to
solve these problems. By running a simple inference
engine on every node, with a small local model, nodes
can generate c onflict sets. If the model can be made to
only use local observations, observations no longer
need to be sent over the network. As a final step, con-
flicts generated by the local inference engine can be
sent to the sink node, which then calculates the minim-
al hitting set of these conflicts to produce the diag-
Post-Deployment Performance Debugging (PD2)
[164] is a data-centric approach that focuses on the
data flows that an application generates, and relates
poor application performance to significant data
losses or latencies of some data flows (problematic
data flows) as they go through the software modules
on individual nodes and through the network. PD2
derives a few inference rules based on the data de-
pendencies between different software modules, as
well as between different nodes, and use them to trace
back in each problematic flow. Then, PD2 turns on
the performance monitoring of, and collects debug-
ging information from, only those modules and nodes
that the problematic flows go through. Finally, PD2
provides the de bugging information to help users iso-
late the causes of poor performance.
S2DB [165] is a debugger based on a distributed full
system WSN simulator with high fidelity and scalable
performance, DiSenS. By exploiting the potential of
DiSenS as a scalable full system simulator, S2DB ex-
tends conventional debugging methods by adding
novel device level, program source level, group level,
and network level debugging abstractions.
Wringer [139] is an integrated debugging framework
that allows for rapid prototyping and deployment of
debugging tools: passive, active , in-network, and gate-
way-based. The goal is to utilize these rap-
id-prototyping capabilities to discover the core set of
debugging primitives that can detect and fix the ma-
jority of WSNs bug s.
7. Code-Updaters for Wir eless Sensor
Network s
Large scale WSNs may be deployed for long periods of
time during which the requirements from the network or
the environment in which the nodes are deployed may
change. This may necessitate modifying the executing
application or re-tasking the existing application with
different sets of parameters, which will collectively ref er
to as code-updation/reprogramming. The relevant forms
of code-updation/reprogramming are [166]:
Remote Multi-hop Reprogramming: It is the most
relevant form of code-updation/reprogramming which
uses the wireless medium to reprograms the nodes as
they are embedded in their sensing environment.
Incremental Reprogramming: It is also attractive be-
cause it transfers a small delta (difference between
the old and the new software) so that code-upda-
tion/reprogramming time and energy can be mini-
Incremental Reprogramming poses several challenges.
A class of operating systems, including the widely used
TinyOS, does not support dynamic linking of software
components on a node. SOS and Contiki, do support dy-
namic linking, however, their reprogramming support
also does not handle chang e s t o t he kernel modules .
Here is a list presenting 10 code-updaters/ reprogram-
ming with their highly conclusive features related to
Trickle [167] is an algorithm for propagating and
maintaining code updates in WSNs. Borrowing tech-
niques from the epidemic/gossip, scalable multicast,
and wireless broadcast literature, Trickle uses a “po-
lite gossip” policy, where motes periodically broad-
cast a code summary to local neighbors but stay quiet
if they have recently heard a summary identical to
theirs. When a mote hears an older summary than its
own, it broadcasts an update. Instead of flooding a
network with packets, the algorithm controls th e send
rate so each mote hears a small trickle of packets, just
enough to stay up to date.
MARWIS (Management ARchitecture for WIreless
Sensor Networks) [89] supports common management
tasks such as visualization, monitoring, (re)configure-
tion, updating and reprogramming. One of the main
features of MARWIS is its hierarchical architecture.
The applications running on the sensor nodes or net
work properties can be reconfigured using the user in
terface. Furthermore, updating and reprogramming the
sensor nodes is a very important issue. In large WSNs
manual execution of this task is unfeasible, and a
Copyright © 2011 SciRes. WSN
mechanism to handle it automatically and dynamically
over the network is required. Both the OS and applica
tions must be updated, either fully or partially.
Multihop Over-the-Air Programming (MOAP) [168]
is a code distribution mechanism specifically targeted
for Mica-2 Motes. These are the following imple-
mentation choices for MOAP: Ripple dissemination
protocol, Unicast retransmission policy and Sliding
Window for segment management. The current ver-
sion of MOAP has been successfully used to repeat-
edly reprogram motes up to four hops away from the
base station, using code images of various sizes,
ranging f r om 600 up to 30K byt e s .
FlexCup [169] is a flexible and efficient code update
mechanism for WSNs that enable on fly reinstallation
of software components in TinyOS-based nodes.
FlexCup is an application that consists of a compi-
ler-extension (FlexCup-Analyzer), a middleware
component (code distribution algorithm), a stand-
alone operating system (FlexCup-Linker), and a ker-
nel component (FlexCup-Bootloader). It is able to re-
configure exchange or reinstall parts of an application.
It has two phases: Code generation phase and linking
Zephyr [166] is a multi -hop incremental reprogra-
mming protocol. It reduces the delta size by using
application-level modifications to mitigate the effects
of function shifts. Then it compares the binary images
at the byte-level with a novel method to create small
delta that is then sent over the wireless network to all
the nodes.
Deluge [170] is an epidemic protocol and operates as
a state machine where each node follows a set of
strictly local rules to achieve a desired global beha-
vior: the quick, reliable dissemination of large data
objects to many nodes. Deluge considers many subtle
issues to achieve high performance. The first is its den-
sity-aware capability, where redundant advertisement
and request messages are sup pressed to minimize con-
tention. Second, Deluge’s three-phase handshaking
protocol helps ensure that a bidirectional link exists
before transferring data. Third, Deluge dynamically
adjusts the rate of advertisements to allow quick prop-
agation when needed while consuming few resources
in the steady state. Fourth, Deluge attempts to minim-
ize the set of nodes concurrently broadcasting data
within a given cell. Finally, Deluge emphasizes the use
of spatial multiplexing to allow parallel transfers of
Stream [171] is a sensor network reprogramming
protocol that significantly reduces the number of
bytes to be transmitted over the wireless medium for
reprogramming. It addresses a fundamental problem
in all existing network reprogramming protocols,
whereby the application image together with the re-
programming protocol image is transferred. Stream
pre-installs the reprogramming protocol image in a
node and transfers the application image with a small
addition. Consequently, it reduces the reprogramming
time, the number of bytes transferred, the energy ex-
pended, and the usage of program memory. Stream is
implemented on TinyOS for the Mica-2 sensor node.
Hermes [172] is a multi-hop incremental reprogram-
ming protocol. It reduces the delta by using tech-
niques to mitigate the effects of function and global
variable shifts caused by the software modifications.
Then it compares the binary images at the byte level
with a method to create small delta that needs to be
sent over the air to all the nodes.
FIGARO [173] is a programming mode l sup porte d by
an efficient run-time system and distributed protocols,
collectively enabling an unprecedented fine-grained
control over what is being reconfigured, and where.
Using FIGARO, the programmer can deal explicitly
with component dependencies and version con-
MNP [174] is a multi-hop network reprogramming
service designed for Mica-2/XSM motes. To reduce
the problem of collision and hidden terminal problem
it implements a sender selection algorithm that
attempts to guarantee that in a neighborhood there is
at most one source transmitting the program at a time.
Further, this sender selection is greedy in that it tries
to select the sender that is expected to have the most
impact. It also uses pipelining to enable fast data
propagation. MNP is energy efficient because it
reduces the active radio time of a sensor node by
putting the node into “sleep” state wh en its neighbors
are transmitting a segment that is not of interest.
8. Network Monitoring Tools for Wireless
Sensor Networks
WSNs are typically composed of low cost tiny hardware
devices and tend to be unreliable, with failures a com-
mon phenomenon. Accurate knowledge of network
health status, including nodes and links of each type, is
critical for correctly configuring applications on really
deployed WSN and/or WSN testbeds and for interpreting
the data collected from them.
Here is a list presenting 8 network monitoring tools
with their summarized features related to WSNs:
Memento [175] is a failure detection system that re-
quires nodes to periodically send heartbeats to the so
called observer node. Those heartbeats are not di-
rectly forwarded to the sink node, but are aggregated
in form of a bitmask (i.e. bitwise OR operation). The
Copyright © 2011 SciRes. WSN
observer node is sweeping its bitmask every sweep
interval and will forward the bitmask with the node
missing during the next sweep interval if the node
fails sending a heartbeat in between. Hence the in-
formation of the missing node is disseminated every
sweep interval by one hop, eventually arriving at the
sink. Memento is not making use of acknowledge-
ments and proactively sends multiple heartbeats every
sweep interval, whereas this number is estimated
based on the link’s estimated worst-case performance
and the targeted false positive rate.
NUCLEUS [157] is one of the network management
systems for data-gathering applicati on of WSN.
DiMo [176] is a distributed and scalable solution for
node and topology monitoring, especially designed
for use with even t-triggered WSNs. The monitoring is
done by so called observer nodes that monitor whe-
ther the target node has checked in by sending a
heartbeat within a certain monitoring time.
MARW IS (Management Architecture for hetero-
geneous Wireless Sensor Networks) [89] supports
common management tasks such as visualization,
monitoring, (re)configuration, updating and repro-
gramming. The status information about every sensor
node is monitored and displayed. This includes hard-
ware features (micro-controller, memory, transceiver),
software details (operating system versions, protocols,
applications), dynamic properties (battery, free mem-
ory) and, if available, geographical position informa-
Sympathy [142] is a tool for detecting and debugging
failures in pre- and post-deployment sensor networks,
especially designed for data gathering applications.
The nodes send periodic heartbeats to the sink that
combines this information with passively gathered
data to detect failures. For the failure detection, the
sink requires receiving at least one heartbeat from the
node every so called sweep interval, i.e. its lacking
indicates a node failure. Direct-Heartbeat performs
poorly in practice without adaptation to wireless
packet losses. To meet a desired false positive rate,
the rate of heartbeats has to be increased also in-
creasing the communication cost.
HERMES [172] is a lightweight framework and pro-
totype tool that provides fine-grained visibility and
control of a sensor node’s software at run-time.
HERMES’s architecture is based on the notion of in-
terposition, which enables it to provide these proper-
ties in a minimally intrusive manner, without requir-
ing any modification to software applications being
observed and controlled. HERMES provides a gener-
al, extensible, and easy-to-use framework for speci-
fying which software components to observe and
control as well as when and how this observation and
control is d one.
LiveNet [177] is a set of tools and techniques for re-
constructing complex dynamics of live sensor net-
work deployments. LiveNet is based on the use of
passive sniffers co-deployed with the network. Snif-
fer nodes can be temporary or permanent, fixed or
mobile, and wired or un-tethered. Sniffers record
traces of all packet activity observed on the radio
channel. Traces from multiple sniffers are merged
into a single trace to provide a global picture of the
network’s behavior. The merged trace is then subject
to a series of analyses to study application behavior,
data rates, network topology, and routing protocol
Chowkidar [145] is a reliable and scalable health
monitoring protocol for wireless sensor network test-
beds. It provides accurate and efficient network
health monitoring in WSNs. The Chowkidar protocol
is initiated upon demand and adapts the well-known
problem of message-passing rooted spanning tree
construction and its use in propagation of information
with feedback (PIF) for the case of a WSN; that is, it
does not involve ongoing maintenance, and it termi-
nates with accurate results, including detection of
failure and restart during the monitoring process.
Chowkidar is distinguished from others in two impor-
tant ways. Given the resource constraints of WSNs, it
is message-efficient in that it uses only a few mes-
sages per node. Also, it tolerates ongoing node and
link failure and node restart, in contrast to requiring
that faults stop during convergence.
9. Educt of this Exploratory Study
Simulation tools are widely used for the purpose of ex-
ploratory analysis in validating algorithms and protocols
due to their rapid prototyping and tackling large scale
systems. However, even the best simulator is still not
able to simulate real wireless communication environ-
ments in terms of completeness, complexity, accuracy
and authenticity. Researchers use emulators of WSNs to
selectively track whether their applications have ex-
ecuted as intended. These emulators simulate the hard-
ware environments to facilitate the development and
checking software applications. The emulator approach
is quite laborious since extensive prior profiling is re-
quired. Taking these drawbacks of simulators and emu-
lators into account, using WSN testbeds to evaluate algo-
rithms and protocols of WSNs is essentially necessary
before applying them into real world applications. The
other experimental tools are also necessary before or
after real deployment during whole life of real or virtual
Copyright © 2011 SciRes. WSN
WSN. The objective of this study has clearly brought
forth important findings that are very useful for re-
searchers involve in any level of WSN experiments to
find an appropriate tool. In real, efforts are on synchronic
aspects for beginners than presenting highly technical
10. Acknowledgements
The full credit of this work is dedicated to all researchers
whose research content or contribution is used in any
form. Full citation is mentioned in reference section.
11. References
[1] Upal, Blog Post at 06:17A.M., August 24, 2009.
[2] A. K. Dwivedi, V. K. Patle and O. P. Vyas, “Investigation
on Effectiveness of Simulation Results for Wireless Sen-
sor Networks,” Information Processing and Management,
Vol. 70, 2010, pp. 202-208.
[3] Distributed Computing Group, “Sinalgo-Simulator for
Network Algorithms.
[4] J. Eriksson, “Detailed Simulation of Heterogeneous
Wireless Sensor Networks,” Dissertation for Licentiate of
Philosophy in Computer Science, Uppsala University,
Sweden, April 2009.
[5] L. Shu, M. Hauswirth, Y. Zhang, S. Mao, N. Xiong and J.
Chen, NetTopo: A Framework of Simulation and Visua-
lization for Wireless Sensor Networks,” ACM/Springer
Mobile Networks and Applications (MONET), Vol. 13, No.
3-4, 2008, pp. 306-322.
[6] Network Simulator.
[7] I. Downard, Simulating Sensor Networks in NS-2,”
Naval Research Laboratory Formal Report 5522, April
[8] Mannasim Simulator. http://www.mannasim.dcc.ufmg.br
[9] P. Levis, N. Lee, M. Welsh and D. Culler, TOSSIM:
Accurate and Scalable Simulation of Entire TinyOS Ap-
plications,” Proceedings of the First ACM Conference on
Embedded Networked Sensor Systems, 2003.
[10] UC Berkeley
[11] L. F. Perrone and D. M. Nicol, A Scalable Simulator for
TinyOS Applications,” Proceedings of the 2002 Winter
Simulation Conference, Vol. 1, San Diego, 2002, pp. 679-
[12] E. Perla, A. Ó Catháin, R. S. Carbajo, M. Huggard and C.
M. Goldrick, “PowerTOSSIM Z: Realistic Energy Mod-
elling for Wireless Sensor Network Environments,” Pro-
ceedings of the 3nd ACM workshop on Performance
Monitoring and Measurement of Heterogeneous Wirele ss
and Wired Networks, Vancouver, 2008, pp. 35-42.
[13] J. Polley, D. Blazakis, J. McGee, D. Rusk, J. S. Baras,
and M. Karir, ATEMU: A Fine-Grained Sensor Net-
work Simulator,” Proceedings of the 1st Annual IEEE
Communications Society Conference on Sensor and Ad
Hoc Communications and Networks, City, 7 October
2004, pp. 145-152.
[14] F. Osterlind, A. Dunkels, J. Eriksson, N. Finne and T.
Voigt, “Cross-Level Sensor Network Simulation with
COOJA,” Proceedings of the 31st IEEE Conference on
Local Computer Networks, 14-16 November 2006, pp.
641-648. doi:10.1109/LCN.2006.322172
[15] X. Zeng, R. Bagrodia and M. Gerla, “GloMoSim: A Li-
brary for Parallel Simulation of Large-Scale Wireless
Networks,” Proceedings of the 12th Workshop on Paral-
lel and Distributed Simulation (PADS), 29 May 1998.
[16] QualNet Simulator by Scalable Network Technolo-
[17] G. Chen, J. Branch, M. J. Pflug, L. Zhu and B. Szymanski,
SENSE: A Sensor Network Simulator,” In: B. K. Szy-
manski and B. Yener, Eds., Advances in Pervasive Com-
puting and Networking, Springer, New York, 2004, pp.
[18] P. Baldwin, S. Kohli, E. A. Lee, X. Liu and Y. Zhao,
VisualSense: Visual Modeling for Wireless and Sensor
Network Systems,Technical Memorandum UCB/ERL
M05/25, University of California, July 2005.
[19] AlgoSenSim.
[20] Georgia Tech Network Simulator
[21] OMNeT++ simulation system.
[22] A. Varga, “The OMNeT++ Discrete Event Simulation
System,” Proceedings of the European Simulation Mul-
ticonference (ESM’2001), Prague, 6-9 June 2001.
[23] D. Pediaditakis, S. H. Mohajerani and A. Boulis, “Poster
Abstract: Castalia: the Difference of Accurate Simulation
in WSN,” Proceedings of the 4th European Conference
on Wireless Sensor Networks (EWSN), Delft, 29-31 Janu-
ary 2007.
[24] A. Sobeih, M. Viswanathan, D. Marinov and J. C. Hou,
“J-Sim: An Integrated Environment for Simulation and
Model Checking of Network Protocols,” Proceedings of
the IEEE International Parallel and Distributed
Processing Symposium, 2007, pp. 1-6.
[25] JiST/SWANS.
[26] JiST/SWANS++. http://www.aqualab.cs.northwestern.ed
[27] B. L. Titzer, D. K. Lee and J. Palsberg, Avrora: Scalable
Sensor Network Simulation with Precise Timing,” Pro-
ceedings of the 4th International Symposium on Informa-
tion Processing in Sensor Networks, USA, April 2005.
[28] T. W. Carley, “Sidh: A Wireless Sensor Network Simu-
lator,” ISR Technical Report, Depa rtment of Electri cal &
Computer Engineering University of Maryland at College
Copyright © 2011 SciRes. WSN
Park, 2004.
[29] J. Prowler, Institute for Software Integrated Systems,
Vanderbilt University, Nashville, Tennessee, 2002.
[30] J. Prowler, Institute for Software Integrated Systems,
Vanderbilt University, Nashville, Tennessee.
[31] A. Cerpa and D. Braginsky, “LecsSim-Wireless Network
Simulator,” 2002.
[32] OPNET Modeller, OPNET Technologies
[33] S. Sundresh, W. Kim and G. Agha, “SENS: A Sensor,
Environment and Network Simulator,” Proceedings of
the 37th Annual Symposium on Simulation, Virginia,
USA, 2004, p. 221. doi:10.1109/SIMSYM.2004.1299486
[34] L. Girod, N. Ramanathan, J. Elson, T. Stathopoulos, M.
Lukac and D. Estrin, “Emstar: A Software Environment
for Developing and Deploying Heterogeneous Sen-
sor-Actuator Networks,” ACM Transactions on Sensor
Networks, Vol. 3, No. 3, August 2007, pp.
13-es. doi:10.1145/1267060.1267061
[35] L. Girod, T. Stathopoulos, N. Ramanathan, J. Elson, D.
Estrin, E. Osterweil and T. Schoellhammer, A System
for Simulation, Emulation and Development of Hetero-
geneous Sensor Networks,” Proceedings of the 2nd ACM
Conference on Embedded Networked Sensor Systems
(SenSys’04), Baltimore, November 2004.
[36] M. Varshney, D. Xu, M. Srivastava and R. Bagrodia,
SenQ: A Scalable Simulation and Emulation Environ-
ment for Sensor Networks,” Proceedings of the 6th In-
ternational Conference on Information Processing in
Sensor Networks, Cambridge, 25-27 April 2007, pp. 196-
[37] O. C. Ghica, G. Trajcevski, P. Scheuermann, Z. Bischof
and N. Valtchanov, “SIDnet-SWANS: A Simulator and
Integrated Development Platform for Sensor Networks
Applications,” Proceedings of the 6th ACM Conference
on Embedded Network Sensor Systems, New York, 2008,
pp. 385-386.
[38] S. Park, A. Savvides and M. B. Srivastava, “SensorSim:
A Simulation Framework for Sensor Networks,” Pro-
ceedings of the 3rd ACM International Workshop on
Modeling, Analysis and Simulation of Wireless and Mo-
bile Systems, Boston, August 2000, pp. 104-111.
[39] S. Fekete, P. Kroller, A. Fischer, S. Pfisterer and D.
Braunschweig, “Shawn: The Fast, Highly Customizable
Sensor Network Simulator,” Proceedings of the 4th In-
ternational Conference on Networked Sensing Systems,
Brunswick, 2007,
[40] SSFNet. http://www.ssfnet.org/
[41] P. M. Wightman and M. A. Labrador, “Atarraya: A Si-
mulation Tool to Teach and Research Topology Control
Algorithms for Wireless Sensor Networks,” Proceedings
of the SIMUTools, Rome, 2009.
[42] L. Shu, M. Hauswirth, Y. Zhang, S. Mao, N. Xiong and J.
Chen, NetTopo: A Framework of Simulation and Visua-
lization for Wireless Sensor Networks,” Proceedings of
the ACM/Springer Mobile Networks and Applications,
[43] V. Ramesh,WiSeNet: A Software Simulator for Wire-
less Sensor Net- work Applications,” 2005.
[44] Y. Wen, S. Gurun, N. Chohan, R. Wolski and C. Krintz,
SimGate: Full-System, Cycle-Close Simulation of the
Stargate Sensor Network Intermediate Node,” Proceed-
ings of the International Conference on Architectures,
Modeling and Simulation, Greece, July 2006.
[45] C.-N. Xu, Z Lei, Y.-J. Xu and X.-W. Li, “Simsync: A
Time Synchronization Simulator for Sensor Networks,”
Acta Automatica Sinica, Vol. 32, No. 6, 2006, pp. 1008-
[46] SNetSim. Naval Science and Engineering Institute, Is-
key. http://www.dho.edu.tr/enstitunet/snetsim/index.htm
[47] S. Yi, H. Min, Y. Cho and J. Hong, SensorMaker: A
Wireless Sensor Network Simulator for Scalable and
Fine-Grained Instrumentation,” Computational Science
and Its Applications, Berlin, 2008.
[48] F. Gómez and G. Martínez, TRMSim-WSN: Trust and
Reputation Models Simulator for Wireless Sensor Net-
works,” Proceedings of the IEEE International Confe-
rence on Communications, Germany, June 2009.
[49] D. Weber, J. Glaser and S. Mahlknecht, Discrete Event
Simulation Framework for Power Aware Wireless Sensor
Networks,” Proceedings of the 5th IEEE International
Conference on Industrial Informatics, Vol. 1, July 2007,
pp. 335-340. doi:10.1109/INDIN.2007.4384779
[50] J. Barbancho, F. J. Molina, C. León, J. Ropero and A.
Barbancho, “OLIMPO: An Ad-hoc Wireless Sensor
Network Simulator for Optimal SCADA-Applications,”
Proceedings of the Communication Systems and Net-
works, Spain, 2004.
[51] Y. Wen, R. Wolski and G. Moore, DiSenS: Scalable
Distributed Sensor Network Simulation,” UCSB Com-
puter Science Technical Report, University of California,
[52] H. B. Lim, B. Wang, C. F. Phull and A. D. Ma, WIS-
DOM: Simulation Framework for Middleware Services
in Wireless Sensor Networks,” Proceeding of the 5th
IEEE Consumer Communications and Networking Con-
ference, 2008.
[53] J. N. Al-Karaki and G. A. Al-Mashaqbeh, SENSORIA:
A New Simulation Platform for Wireless Sensor Net-
works,” Proceedings of the International Conference on
Sensor Technologies and Applications, 2007, pp. 424-
[54] K. Sha, J. Du and W. Shi, “Capricorn: A Large Scale
Wireless Sensor Network Simulator,” Technical Report
MIST-TR-2004-001, Wayne State University, January
Copyright © 2011 SciRes. WSN
[55] B. C. Mochocki and G. R. Madey, “H-MAS: A Hetero-
geneous, Mobile, Ad-hoc Sensor-Network Simulation
Environment,” Proceedings of the 7th Annual SWARM
Users/Researchers Conference, Notre Dame, Indiana,
April 2003.
[56] Stargate Simulator (starsim) and Mote Simulator (mote-
sim). http://www.cs.ucsb.edu/~wenye/
[57] L. Chuan-wen, G. Yu, L. Fang-fang, X. Jia and Y. Ge,
“SNSim: Study and Implementation of a Wireless Sensor
Network Simulator,” Journal of Chinese Computer Sys-
tems, No. 6, 2010, pp. 1025-1029.
[58] S. N. Sinha, Z. Chaczko and R. Klempous, SNIPER: A
Wireless Sensor Network Simulator,” Proceedings of the
EUROCAST, 2009, pp. 913-920.
[59] C. Kelly, V. Ekanayake and R. Manohar, “SNAP: A
Sensor Network Asynchronous Processor,” Proceedings
of the 9th ACM Int. Symposium on Asynchronous Circuits
and Systems, Ithaca, 12-15 May, 2003, pp. 24-33.
[60] SimPy Documentation. By K.
ler. http://simpy.sourceforge.net/discuss.htm
[61] D. Watson and M. Nesterenko, Mule: Hybrid Simulator
for Testing and Debugging Wireless Sensor Networks,”
Proceedings of the Second International Workshop on
Sensor and Actor Network Protocols and Applications,
Boston, 2004, pp. 67-71.
[62] A. Boulis, A. Fehnker, M. Fruth and A. McIver, “CaVi –
Simulation and Model Checking for Wireless Sensor
Networks,” Proceedings of the Quantitative Evaluation of
Systems (QEST), 2008.
[63] P. Baldwin, S. Kohli, E. A. Lee, X. Liu and Y. Zhao,
“Modeling of Sensor Nets in Ptolemy II,” Proceedings of
the Information Processing in Sensor Networks (IPSN),
2004, pp. 359-368.
[64] Maple Simulator. http://www.maplesoft.com/
[65] WISENES Simulator.
[66] A. Fraboulet, G. Chelius and E. Fleury, Worldsens: De-
velopment and Prototyping Tools for Application Specif-
ic Wireless Sensors Networks,” Proceedings of the 6th
International Conference on Information Processing in
Sensor Networks, USA, April 2007.
[67] A. Suri, Simulation Study for Wireless Sensor Networks
and Load Sharing Routing Protocol to Increase Network
Life and Connectivity,” Master’s Thesis, Department of
Computer Science, Louisiana State Uni versity, December
[68] M. Karagiannis, I. Chatzigiannakis and J. Rolim,
“WSNGE: A Platform for Simulating Complex Wireless
Sensor Networks Supporting Rich Network Visualization
and Online Interactivity,” Proceedings of the Spring Si-
mulation Multiconference (SpringSim09), Article 35,
[69] F. Menichelli and M. Olivieri, “TikTak: A Scalable Si-
mulator of Wireless Sensor Networks Including Hard-
ware/Software Interaction,” Wireless Sensor Network,
November 2010, pp. 815-822.
[70] H. Wu, Q. Luo, P. Zheng and L. M. Ni, VMNet: Realis-
tic Emulation of Wireless Sensor Networks,” IEEE
Transaction on Parallel Distributed System, Vol. 18, No.
2, 2007, pp. 277-288.
[71] VMNET: A WS N Emulator ,
2004. http://www.cse.ust.hk/vmnet/
[72] T. Maret, R. Kummer, P. Kropf and J. F. Wagen, Free-
mote Emulator: A Lightweight and Visual Java Emulator
for WSN. Wired/Wireless Internet Communications,”
Springer LNCS, Vol. 5031, 2008, pp. 92-103.
[73] C. Park and P. H. Chou, “EmPro: an Environment/Energy
Emulation and Profiling Platform for Wireless Sensor
Networks,” Proceedings of the IEEE SECON, 2006, pp.
[74] OCTAVEX Wireless Sensor
work. http://www.octavetech.com/solutions/octavex.html
[75] UbiSec & Sens. http://www.ist-ubisecsens.org/
[76] Surge Network Viewer. By Crossbow Technology
Inc. http://www.hoskin.qc.ca/uploadpdf/Instrumentation/
[77] J. Eriksson, A. Dunkels, N. Finne, F. Österlind and T.
Voigt, Mspsim―An Extensible Simulator for Msp430-
Equipped Sensor Boards,” Proceedings of the European
Conference on Wireless Sensor Networks, Poster/Demo
session, The Netherlands, January 2007.
[78] Q. Luo, L. M. Ni, B. He, et al ., MEADOWS: Mode ling,
Emulation, and Analysis of Data of Wireless Sensor
Networks,” Proceedings of the First Workshop on Data
Management for Sensor Networks (DMSN 2004), Toronto,
[79] B. Parbat, A. K. Dwivedi and O. P. Vyas, Data Visuali-
zation Tools for WSNs: A Glimpse,” International Jour-
nal of Computer Applications, Vol. 2, No. 1, 2010, pp.
[80] C. Buschmann, D. Pfisterer, S. Fischer, S. P. Fekete and
A. Kröller, “SpyGlass: A Wireless Sensor Network Visu-
alizer,” Acm Sigbed Review, Vol. 2, No. 1, 2005, pp.
1-6. doi:10.1145/1121782.1121784
[81] M. Tuton, MOTEVIEW: A Sensor Network Monitoring
and Management Tool,” Proceedings of Second IEEE
Workshop on Embedded Networked Sensors (EmNetS-II),
2005, pp. 11-18.
[82] J. Pinto, A. Sousa, P. Lebres, G. M. Gonçalves and J.
Sousa, “MonsenseApplication for Deployment, Moni-
toring and Control of Wireless Sensor Networks,” Poster
in ACM RealWSN’06, 2006.
[83] R. Jurdak, A. G. Ruzzelli, A. Barbirato and S. Boivineau,
Octopus: Monitoring, Visualization, and Control of
Sensor Networks,” Wireless Communications and Mobile
Computing, John Wiley & Sons, 2009.
[84] MSR Sense: MSR Networked Embedded Sensing Tool-
kit. http://research.microsoft.com/nec/msrsense/
Copyright © 2011 SciRes. WSN
[85] MoteIV Corporation. http://www.sentilla.com/
[86] Y. Yang, P. Xia, L. Huang, Q. Zhou, Y. Xu and X. Li,
SNAMP: A Multi-sniffer and Multi-view Visualization
Platform for Wireless Sensor Networks,” Proceedings of
the 1st IEEE Conference on Industrial Electronics and
Applications, Singapore, 24-26 May 2006, pp. 1-4.
[87] Meshnetics. http://www.meshnetics.com/press_releases/
ics. http://www.meshnetics.com/press_releases/MeshNeti
[88] D. Davcev and S. Gancev, “Monitoring of environment
by energy efficient usage of Wireless Sensor Networks,”
Information Technologies in Environmental Engineering,
Berlin, 2009, pp. 229-237.
[89] G. Wagenknecht, M. Anwander, T. Braun, T. Staub, J.
Matheka and S. Morgenthaler, MARWIS: A Manage-
ment Architecture for Heterogeneous Wireless Sensor
Networks,” Proceedings of the 6th International Confe-
rence on Wired/Wireless Internet Communications, Fin-
land, 2008, pp. 177-188.
[90] Oscilloscope. From TinyOS Community Forum, “Tiny-
OS: An open-source OS for the networked sensor re-
gime”. http://www.tinyos.net
[91] K. Aberer, M. Hauswirth and A. Salehi, “Infrastructure
for Data Processing in Large-Scale Interconnected Sensor
Networks,” Proceedings of the 8th International Confe-
rence on Mobile Data Management, Mannheim, Germa-
ny, May 2007. doi:10.1109/MDM.2007.36
[92] J. A. Castillo, A. M. Ortiz, V. López, T. Olivares and L.
Orozco-Barbosa, “WiseObserver: A Real Experience
with Wireless Sensor Networks,” Proceedings of the 3nd
ACM Workshop on Performance Monitoring and Mea-
surement of Heterogeneous Wireless and Wired networks,
New York, USA, October 2008, pp. 23-26.
[93] P. Andreou, D. Zeinalipour-Yazti, A. Pamboris, P. K.
Chrysanthis and G. Samaras, “Optimized Query Routing
Trees for Wireless Sensor Networks,” Information Sys-
tems, 2010.
[94] Crossbow Technology Inc. http://www.xbow.com
[95] G. Werner-Allen, P. Swieskowski and M. Welsh, “Mo-
teLab: A Wireless Sensor Network Testbed,” Proceed-
ings of the 4th International Symposium on Information
Processing in Sensor Networks, Article 68, April 2005.
[96] Tutornet
Testbed. http://enl.usc.edu/projects/tutornet/index.html
[97] WUSTL Wireless Sensor Network
Testbed. http://www.cs.wustl.edu/wsn/index.php?title=Te
[98] R. Murty, G. Mainland, I. Rose, A. R.Chowdhury, A.
Gosain, J. Bers and M. Welsh, CitySense: A Vision for
an Urban-Scale Wireless Networking Testbed,” Pro-
ceedings of the IEEE International Conference on Tech-
nologies for Homeland Security, Waltham, May
2008. doi:10.1109/THS.2008.4534518
[99] E. Ertin, A. Arora, R. Ramnath, et al.,Kansei: A
Testbed for Sensing at Scale,” Proceedings of the 4th
Symposium on Information Processing in Sensor Net-
works, USA, April 2006.
[100] M istLab Testbed. http://mistlab.csail.mit.edu/
[101] M. Ott, I. Seskar, R. Siracusa and M. Singh, “ORBIT
Testbed Software Architecture: Supporting Experiments
as a Service,” Proceedings of the IEEE Tridentcom, Italy,
February 2005.
[102] D. Johnson, D. Flickinger, T. Stack, R. Ricci, L. Stoller,
R. Fish, K. Webb, M. Minor and J. Lepreau, Emulabs
Wireless Sensor Net Testbed: True Mobility, Location
Precision, and Remote Access,” Proceedings of the 3rd
International Conference on Embedded Networked Sen-
sor Systems, 2005.
[103] WISEBEDWire l e ss Sensor Network
Testbed. http://www.wisebed.eu/
[104] J. Albesa, R. Casas, M. T. Penella and M. Gasulla,
REALnet: An Environmental WSN Testbed,” Pro-
ceedings of the International Conference on Sensor
Technologies and Applications, 2007, pp. 502-507.
[105] K. Iwanicki, A. Gaba and M. V. Steen, KonTest: A
Wireless Sensor Network Testbed at Vrije Universiteit
Amsterda m,” Technica l Report IR-CS-045, Vrije Univer-
siteit Amsterdam, August 2008.
[106] A. Hergenr¨oder, J. Horneber, D. Meier, et al., Demo
Abstract: Distributed Energy Measurements in Wireless
Sensor Networks,” Proceedings of the SenSys’09, USA,
November 2009.
[107] H. Alzaid and S. Abanmi, A Wireless Sensor Networks
Test-bed for the Wormhole Attack,” International Jour-
nal of Digital Content Technology and its Applications,
Vol. 3, No. 3, 2009, pp. 19-27.
[108] U. Kumar, A. Ranjan, V. Jalan, et al., CENSE: A Mod-
ular Sensor Network Testbed,” Proceedings of the Na-
tional Conference on Embedded System s, February 2006.
[109] M. J. Murillo and J. A. Slipp, “Application of WINTeR
Industrial Testbed to the Analysis of Closed-Loop Con-
trol Systems in Wireless Sensor Networks,” Demo Ac-
cepted in the 8th ACM/IEEE International Conference on
Information Processing in Sensor Networks, April 2009.
[110] C. Kejie, G. Xiang, H. Xianying, C. Jiming and S. Youx-
ian, Design and Develop of the WSN Testbed:
NESC-Testbed,” China Science Paper Online, December
[111] Y. L. Huang, J. D. Tygar, H. Y. Lin, et al., SWOON: A
Testbed for Secure Wireless Overlay Networks,” Pro-
ceedings of the Workshop on Cyber Security Experi-
mentation and Test (CSET '08), 2008.
[112] M. Doddavenkatappa, M. C. Chan and A. L. Ananda,
Indriya: A Low Cost, 3DWireless Sensor Network
Testbed,” Developed at School of Computing, National
University of Singapore, Singapore, 2009.
[113] Clarity-Testbed. http://ercim-news.ercim.eu/en76/special/
ty-Testbed. http://ercim-news.ercim.eu/en76/special/the-c
[114] Imote2 Sensor Network Testbed.
[115] J. P. Sheu, C. J. Chang, C. Y. Sun and W. K. Hu,
WSNTB: A Testbed for Heterogeneous Wireless Sensor
Networks,” Proceedings of the 1st IEEE International
Copyright © 2011 SciRes. WSN
Conference on Ubi-Media Computing, 2008.
[116] V. Handziski, A. K¨opke, A. Willig, et al., TWIST: A
Scalable and Reconfigurable Testbed for Wireless Indoor
Experiments with Sensor Networks,” Proceedings of the
International Symposium on Mobile Ad Hoc Networking
and Computing, 2006, pp. 63-70.
[117] ENL Sensor Network
Testbed. http://enl.usc.edu/projects/testbed/index.html
[118] A. Kanzaki, T. Hara, Y. Ishi, et al., “X-Sensor: A Sensor
Network Testbed Integrating Multiple Networks,” Pro-
ceedings of the International Conference on Complex,
Intelligent and Software Intensive Systems, 2009, pp.
[119] E. Welsh, W. Fish and P. Frantz, GNOMES: A Testbed
for Low-Power Heterogeneous Wireless Sensor Net-
works,” Proceedings of the IEEE International Sympo-
sium on Circuits and Systems, Thailand, 2003.
[120] M. Ramakrishnan and P. V. Ranjan, PICSENSE–A
Wireless Sensor Network Testbed,” International Journal
of Recent Trends in Engineering, Vol. 1, No. 4, May
2009, pp. 59-63.
[121] SOWNet WSN Testbed.
[122] D. Sakamuri, NetEye: A Wireless sensor Network
Testbed,” Thesis for Mater of Science Degree, Wayne
State University, Detroit, Michigan, 2008.
[123] T. Dimitriou, J. Kolokouris and N. Zarokostas, Sense-
net: A Wireless Sensor Network Testbed,” Proceedings
of the 10th ACM Symposium on Modeling Analysis and
Simulation of Wireless and Mobile Systems, 2007, pp.
143-150. doi:10.1145/1298126.1298153
[124] Omega a nd Motescope
[125] Sharesense
[126] P. Dutta, J. Hui, J. Jeong, et al., Trio: E nabli ng Sustain-
able and Scalable Outdoor Wireless Sensor Network
Deployments,” Proceedings of the fifth International
Conference on Information Processing in Sensor Net-
works, 2006, pp. 407-415.
[127] sMote Testbed. University of California, Berkeley.
bed-federation/testbed-dire ctory/
[128] G. Mylonas, “Practical Aspects of CTI WSN Testbed,”
2nd PROSENSE Meeting, Santorini, September
[129] A. Mateska, V. Atanasovski and L. Gavrilovska, “FEEIT
WSN Testbed: Effective System for Providing Emer-
gency Situations Prevention and Surveillance,” In: A.
Mateska, V. Atanasovski and L. Gavrilovska, Eds., Ap-
plication and Multidisciplinary Aspects of Wireless Sen-
sor Networks, Springer, London, 2011, pp. 245-256.
www.prosense -project.eu/files/prosense.pdf
[130] Roulette. https://www.millennium.berkeley.edu/wiki/sens
lette. https://www.millennium.berkeley.edu/wiki/sensorne
[131] BigNet Sensor Network Te stbed.
[132] UCR Wirele ss Networking Research Testbed. University
of California. http://networks.cs.ucr.edu/testbed/
[133] J. Sa Silva, R. Ruivo, T. Camilo, et al., IP in wireless
sensor networks Issues and lessons learnt,Proceedings
of the 3rd International Conference on Communication
Systems Software and Middleware and Workshops, 2008,
pp. 496-502. doi:10.1109/COMSWA.2008.4554464
[134] Z. Ji, M. Marina, M. Varshney, et al., WHYNET: A
Hybrid Testbed for Large-Scale, Heterogeneous and
Adaptive Wireless Networks,” Technical Report CSD-
TR060002, UCLA Computer Science Department, Ja-
nuary 2006.
[135] CENS-Testbed. http://www.tinyos.net/ttx-02-2005/testbe
Testbed. http://www.isi.edu/scadds/publications.html
[137] J. Hernstrom, “A Test-Bed Application for Analog Sen-
sors Used in Wireless Sensor Networks: Student Paper,”
Journal of Computing Sciences in Colleges, Vol. 21, No.
4, 2006, pp. 184-189.
[138] N. Brent, P. Buonadonna, A. AuYoung, et al., “Mirage:
A Microeconomic Resource Allocation System for Sen-
sornet Testbeds,” Proceedings of the 2nd IEEE Workshop
on Embedded Networked Sensors, Australia, May 2005.
[139] A. Tavakoli, Wringer: A Debugging and Monitoring
Framework for Wireless Sensor Networks,” Proceedings
of the ACM SenSys Doctoral Colloquium, 2007.
[140] J. Yang, M. L. Soffa, L. Selavo and K. Whitehouse,
“Clairvoyant: A Comprehensive Source-Level Debugger
for Wireless Sensor Networks,” Proceedings of the 5th
International Conference on Embedded Networked Sen-
sor Systems, Sydney, 2007, pp. 189-203.
[141] M. M. H. Khan, H. K. Le, H. Ahmadi, T. F. Abdelzaher
and J. Han, “Dustminer: Troubleshooting Interactive
Complexity Bugs in Sensor Networks,” Proceedings of
the 6th ACM Conference on Embedded Network Sensor
Systems, Raleigh, 2008, pp. 99-112.
[142] N. Ramanathan, K. Chang, R. Kapur, L. Girod, E. Kohler
and D. Estrin, Sympathy for the Sensor Network De-
bugger,” Proceedings of the 3rd International Conference
on Embedded Networked Sensor Systems, San Diego,
2005, pp. 255-267. doi:10.1145/1098918.1098946
[143] S. Guo, Z. Zhong and T. He, FIND: Faulty Node Detec-
tion for Wireless Sensor Networks,” Proceedings of the
7th ACM Conference on Embedded Networked Sensor
Systems, California, 2009, pp. 253-266.
[144] I. Urteaga, K. Barnhart and Q. Han, “REDFLAG: A
Run-timE, Distributed, Flexible, Lightweight, and Ge-
neric Fault Detection Service for Data-Driven Wireless
Sensor Applications,” Pervasive and Mobile Computing,
Vol. 5, No. 5, 2009, pp. 432-446.
Copyright © 2011 SciRes. WSN
[145] S. Bapat, W. Leal, T. Kwon, et al. , “Chowkidar: Reliable
and Scalable Health Monitoring for Wireless Sensor
Network Testbeds,” ACM Transactions on Autonomous
and Adaptive Systems (TAAS), Vol. 4, No. 1, Article 3,
January 2009.
[146] Y. Kwon, S. Sundresh, K. Mechitov and G. Agha, “Ac-
torNet: An Actor Platform for Wireless Sensor Net-
works,” Technical Report UIUCDCS-R-2005-2595,
University of Illinois, Urbana-Champaign, 2005.
[147] R. K. Karmani and G. Agha, Debugging Wireless Sen-
sor Networks Using Mobile Actors,” Proceedings of the
RTAS Poster Session, 2008.
[148] M. Lodder, Debugging Wireless Sensor Networks using
a Global-State Perspective,” Master’s Thesis in Computer
Science, Delft University of Technology, 2008.
[149] M. Lodder, G. P. Halkes and K. G. Langendoen, A
Global-State Perspective on Sensor Network Debug-
ging,” Proceedings of the HotEmNets’08, Charlottesville,
Jun. 2008.
[150] Q. Cao, T. Abdelzaher, J. Stankovic, et al., “Declarative
Tracepoints: A Programmable and Application Indepen-
dent Debugging System for Wireless Sensor Networks,”
Proceedings of the 6th ACM Conference on Embedded
Network Sensor Systems, Raleigh, 2008, pp. 85-
98. doi:10.1145/1460412.1460422
[151] L. Luo, T. He, G. Zhou, et al., Achieving Repeatability
of Asynchronous Events in Wireless Sensor Networks
with EnviroLog,” Proceedings of the IEEE INFOCOM,
23-29 April 2006. doi:10.1109/INFOCOM.2006.114
[152] V. Krunic, E. Trumpler and R. Han, NodeMD: Diag-
nosing Node-Level Faults in Remote Wireless Sensor
Systems,” Proceedings of the 5th International Confe-
rence on Mobile Systems, Applications and Services, San
Juan, June 2007.
[153] P. D. K. Varma and V. Radha, “Prevention of Buffer
Overflow Attacks Using Advanced Stackguard,” Pro-
ceedings of the International Conference on Advances in
Communication, Network, and Computing (CNC), Cali-
cut, 4-5 October 2010.
[154] R. Sasnauskas, O. Landsiedel, M. H. Alizai, et al.,
KleeNet: Discovering Insidious Interaction Bugs in
Wireless Sensor Networks before Deployment,” Pro-
ceedings of the IPSN’10, Stockholm, 12 April 2010.
[155] K. Whitehouse, G. Tolle, J. Taneja, et al. , “Marionette:
Using RPC for Interactive Development and Debugging
of Wireless Embedded Networks,” Proceedings of the
ACM/IEEE International Conference on Information
Processing in Sensor Networks (IPSN), Nashville, 19-21
April 2006. doi:10.1109/IPSN.2006.243881
[156] K. Römer and J. Ma, PDA: Passive Distributed Asser-
tions for Sensor Networks,” Proceedings of the IPSN’09,
San Francisco, April 2009, pp. 337-348.
[157] G. Tolle and U. C. Berkeley, “Nucleus Network Man-
[158] T. Sookoor, T. Hnat, P. Hooimeijer, et al., “Macrode bug-
ging: Global Views of Distributed Program Execution,”
Proceedings of the SenSys-09, Berkeley, 4-6 November
[159] M. M. H. Khan, L. Luo, C. Huang and T. F. Abdelzaher,
Snts: Sensor Network Troubleshooting Suite,” Pro-
ceedings of the 3rd IEEE International Conference
DCOSS, Springer LNCS, Vol. 4549, 2007, pp. 142-157.
[160] S. Gupta, R. Zheng and A. M. K. Cheng, ANDES: an
Anomaly Detection System for Wireless Sensor Net-
works,” Proceedings of the IEEE International Confe-
rence on Mobile Adhoc and Sensor Systems, 2007, pp.
1-9. doi:10.1109/MOBHOC.2007.4428636
[161] M. Woehrle, C. Plessl, R. Lim, et al., “EvAnT: Analysis
and Checking of Event Traces for Wireless Sensor Net-
works,” Proceeding of the IEEE International Confe-
rence on Sensor Networks, Ubiquitous, and Trustworthy
Computing, Taichung, 11-13 June 2008, pp. 201-208.
[162] T. O’Donovan, C. J. Sreenan, N. Tsiftes, et al., Poster
Abstract: Storage-Centric Debugging of Performance
Problems in Sensor Networks,” Proceedings of the 7th
European Conference on Wireless Sensor Networks,
Coimbra, February 2010.
[163] A. D. Jong, Evaluating Model-Based Diagnosis for
Wireless Sensor Networks,” Master’s Thesis in Computer
Science, Delft University of Technology, Delft, 2009.
[164] Z. Chen and K. G. Shin, Post-Deployment Performance
Debugging in Wireless Sensor Networks,” Proceedings
of the 30th IEEE Real-Time Systems Symposium, Wash-
ington DC, 2009, pp. 313-322.
[165] Y. Wen, R. Wolski and S. Gurun, “S2DB: A Novel Si-
mulation Based Debugger for Sensor Network Appli- ca-
tions,” Proceedings of the EMSOFT’06, Seoul, October
[166] R. K. Panta, S. Bagchi and S. P. Midkiff, “Zephyr: Effi-
cient Incremental Reprogramming of Sensor Nodes using
Function Call Indirections and Difference Computation,”
Proceedings of the USENIX, San Diego,
[167] P. Levis, N. Patel, D. Culler and S. Shenker, Trickle: A
Self Regulating Algorithm for Code Propagation and
Maintenance in Wireless Sensor Networks,” Proceedings
of the 1st conference on Symposium on Networked Sys-
tems Design and Implementation, San Francisco, 2004.
[168] T. Stat hopoulos, J. Heidemann and D. Estrin, A Remote
Code Update Mec hanism for Wireless Sensor Networks,”
Technical Report CENS-TR-30, University of California,
Los Angeles, November 2003.
[169] P. J. Marr´on, M. Gauger, A. Lachenmann, et al., Flex-
Cup: A Flexible and Efficient Code Update Mechanism
for Sensor Networks,” Proceedings of the Third Euro-
pean Workshop on Wireless Sensor Networks, Zurich,
February 2006, pp. 212-227.
[170] J. W. Hui and D. Culler, The Dynamic Behavior of a
Data Dissemination Protocol for Network Programming
at Scale,” Proceedings of the ACM SenSys’04, Ma ryland,
November 2004, pp. 81-94.
Copyright © 2011 SciRes. WSN
[171] R. Panta, I. Khalil and S. Bagchi, Stream: Low Over-
head Wireless Reprogramming for Sensor Networks,”
Proceedings of the IEEE Infocom, Anchorage, 2007, pp.
[172] R. Panta and S. Bagchi, Hermes: Fast and Energy Effi-
cient Incremental Code Updates for Wireless Sensor
Networks,” Proceedings of the IEEE Infocom, 2009.
[173] L. Mottola, G. P. Picco and A. A. Sheikh, Figaro: Fine-
grained Software Reconfiguration for Wireless Sensor
Networks,” Proceedings of the EWSN, Bologna, 2008.
[174] S. S. Kulkarni and L. Wang, Mnp: Multihop Network
Reprogramming Service for Sensor Networks,” Pro-
ceedings of the 25th International Conference on Distri-
buted Computing Systems (ICDCS), Columbus, 2005.
[175] S. Rost and H. Balakrishnan, Memento: A Health Mon-
itoring System for Wireless Sensor Networks,” Proceed-
ings of the 3rd IEEE Communications Society Conference
on Sensor, Mesh and Ad Hoc Communications and Net-
works (IEEE SECON), Reston, 2006.
[176] A. Meier, M. Motani, H. Siquan, et al., DiMo: Distri-
buted Node Monitoring in Wireless Sensor Networks,”
Proceedings of the MSWiM’08, Vancouver, October
[177] B. Chen, G. Peterson, G. Mainland, et al., LiveNet: Us-
ing Passive Monitoring to Reconstruct Sensor Network
Dynamics,” Proceedings of the 4th International Confe-
rence on IEEE/ACM Distributed Computing in Sensor
Systems, Santorini Island, June 2008.