Limitations of Simulation Tools for Large-Scale Wireless Sensor Networks Muhammad Zahid Khan, Bob Askwith, Faycal Bouhafs, Muhammad Asim School of Computing and Mathematical Sciences, Liverpool John Moores University, UK
[email protected], {R.J.Askwith, F.Bouhafs}@ljmu.ac.uk,
[email protected] Abstract—Wireless Sensor Networks (WSNs) consist of a large number of wireless sensor nodes networked together. It is a complex set of applications, link technologies, communication protocols, traffic flows, and routing algorithms. Simulation is a predominant technique used to study and analyse the performance and potency of a senor network design. Since there are a huge variety of simulation tools available for WSNs, which vary in their characteristics and capabilities, it is often very difficult to decide which simulation tool to choose and which one is more appropriate for large-scale WSNs. To address this issue, in this paper, we review some of the most widely-used and stateof-the-art simulation tools for WSNs. This distinguishing feature of this paper is that we identify the key limitations of the reviewed simulation tools and inspect their suitability for largescale WSNs. We review and investigate simulation tools based on a new set of preferred criterion, i.e. popularity, accessibility (open-source), complexity, accuracy, scalability, extensibility and availability of various models and protocols. Keywords- WSNs; simulators; scalability
I.
INTRODUCTION
WSNs are different from traditional wireless networks. It is a complex set of applications, link technologies, communication protocols, traffic flows and routing algorithms. The use of wireless technology for communication and its nondeterministic deployment further increases the complexity of a network design process. Furthermore, sensor networks might consist of several hundred thousands of nodes, therefore, deploying sensor networks of actual size in case of developing and testing new protocols, designs, architectures or experimenting functionalities may consume too much time and cost. Despite the steady increase in mathematical analysis and experimental modeling, research community widely uses simulation for their study [1]. Simulation is an alternative technique used for testing and analysing realistic scenarios of large-scale WSNs [2]. In WSNs, simulation is one of the most predominant evaluation methodologies for the development of new communication architectures, and network protocols as well as to test and validate the existing one in various scenarios [3]. Simulation helps researchers to get significant information on feasibility and practicability crucial to the implementation of the system prior to investing significant time and money. In WSNs, simulation based testing and validation has many advantages, such as: ease of implementation, lower cost, flexibility and possibility of testing large-scale networks [4]. The availability of a large number of simulation tools and specific requirement (e.g. energy-constraints, large-scale deployment) of WSNs makes it difficult for a user to choose a nearly perfect tool for his evaluation. To address this issue, we survey some of the most widely-used and state-of-the-art simulation tools for WSNs. The aim is to help researchers in
the selection of an appropriate simulation tool to evaluate their work, and to acquire reliable results for large-scale WSNs. Different researchers categorized and reviewed simulation tools for WSNs in different ways [3, 5-9], however, the distinguishing feature of our work is that we define a new set of criterion for our investigation, which rigorously keep into account the specific requirement and characteristics of WSNs. We review and investigate simulation tools based on their popularity, accessibility (open-source), complexity, accuracy, extensibility, availability of various models and protocols and scalability. The preferred set of criterion helps us to identify the key limitations of different simulation tools and their suitability for large-scale WSNs. The rest of the paper is organized as follows. In Section II, we discuss the importance of simulation in WSNs and our motivation behind this paper. In section III, we overview stateof-the art simulation tools for WSNs and identify their key limitations. In Section IV, we discuss the overall suitability of simulation tools for large-scale WSNs along with a comparison table of the presented simulation tools, and finally the paper is concluded in Section V. II.
BACKGROUND
According to S. Asmussen et al. [10] Network Simulation is a technique where a computer program (Simulation tool or Simulator) model the behaviour of a network by manipulating the interaction between different network entities ( e.g. nodes, sinks, data links, packets, etc.) using mathematical formulas. Simulation tools model and imitate the working of the real networking scenario in a virtual environment. The behaviour of the network and various applications and services it supports can then be observed in that virtual environment. In addition, various attributes of the environment can also be modified in a controlled manner to assess how the network would behave under different conditions. The motivation behind this paper is to identify the key limitations of existing simulation tools for WSNs and their suitability to simulate large-scale WSNs. A broad range of WSNs simulators are available, which have been developed over time, each specializing in certain features and applications. The simulation tools presented in this paper are NS-2, OMNeT++, GloMoSim, OPNET, SENSE, TOSSIM and GTSNetS. Other simulation tools include TOSSF, Prowler, Sidh, VisualSense, Monarch, EmStar, and WSNSim, just to name a few. Some of the differentiating capabilities among these include: usability, extensibility, customization, scalability, fidelity ranges, and level of support. Various problems found in different simulators, including oversimplified models, extensibility issues, lack of customizations, difficulty in obtaining already existing relevant protocols, availability, scalability and financial cost [3].
III.
WIDELY-USED SIMULATION TOOLS FOR WSNS
The availability of a large number of simulators raises the question of which tool to use, especially if one is interested in achieving a high simulation performance, accuracy and scalability. We review and analyse state-of-the art simulation used by the research community. Our investigation is based upon a set of selected criterion such as popularity, accessibility, extensibility, availability of sensor models and features, accuracy, and scalability. The preferred set of criterion helps us to identify the key limitations of different simulation tools and their suitability for large-scale WSNs. A comparison table of all the reviewed simulation tools is also given. A. NS-2 NS-2 (Network Simulator) is the most popular and widelyused general purpose network simulator [5, 11]. t is an objectoriented discrete-event simulator, which provides substantial support for simulation of TCP, routing and multicast protocols. The simulator is using a combination of C++ and Tcl/OTcl ((Tool Command Language/Object Oriented Tcl). In general, C++ is used for implementing protocols and extending the NS2 library, while Tcl/OTcl is used to create and control the simulation environment itself. Initially, NS-2 was used as a Mobile ad-hoc network's simulator, but later with few modifications and small number of add-ons, it has been using for WSNs as well. Now supports for different unique requirements (energy and resource constraints) of WSN are included in the new version of NS-2 (version 2.34). NS-2 is available free of cost under GNU open-source agreement, and currently it is actively maintained and updated. Its C++ object-oriented and modular approach has effectively made it extensible. User can extend the existing code and models as well as can implement new protocols and architectures. The combination of easy in protocol development and popularity has ensured that a large number of different protocols are publicly available [6]. The open-source nature and the extensibility feature make NS-2 so popular among the researchers. Although NS-2 is a powerful network simulator, some researchers claimed that it has some drawbacks for simulating WSNs [3, 6, 12]. S-2 does not scale well for large-scale sensor network ( not more than 100 nodes) [3, 5]. e object-oriented design introduces unnecessary interdependency between modules. Such interdependency sometimes makes the addition of new protocol models extremely difficult. Another drawback of NS-2, which limits its use for WSNs, is the lack of customization. Energy models, packet formats, MAC protocols, accuracy models, and the sensing hardware models are differed from those found in most sensors. In addition, mixing wired and wireless nodes in NS-2 simulation is a very also very difficult. Overall, WSN simulation is not easily supported by NS-2 although many researchers are currently attempting to modify NS-2 towards better WSN simulation. Furthermore, due to the combination of using many scripting languages (e.g., AWK, Pearl, etc.) make it complicated for a user, it takes a long time to get used to it, and finally it has a poor documented source code.
B. OMNeT++ OMNeT++ (Objective Modular Network Test-bed) is an open-source, component-based, discrete-event, modular simulation framework for WSNs [13, 14]. OMNeT++ uses C++ language for simulation models. The OMNeT++ model is a collection of hierarchically nested modules to implement their simulator. The top-level module is also called the System Module or Network. This module contains one or more submodules each of which could contain other sub-modules. In OMNeT++ modules can be defined as being either simple or compound. Simple modules are used to define algorithms, and make up the bottom of the hierarchy. Compound modules are a collection of simple modules that interact with one another, using messages. Like the aforementioned NS-2, OMNeT++ rests upon C++ for the implementation of simple modules. However, to form compound modules and set-up of network simulation takes place in Network Description Language (NED) of OMNeT++. NED is transparently rendered into C++ code when the simulation is compiled as a whole. Like NS-2, OMNeT++ is popular, extensible and actively maintained by its user community in the academia who has also produced extensions for WSN simulation. The WSN version of the OMNeT++ simulator is called a SensorSim[6]. SensorSim provides a component based efficient implementation, which has proven to be much faster than ns-2. It also provides accurate models of sensor hardware and physical phenomena for WSNs. In addition, due to the C++ object-oriented nature it is fairly extensible and all layers of the protocol stack can easily be modified. In spite, of its obvious advantages, SensorSim has remained relatively obscure and very little published work is available using SensorSim. Castalia [7] is the most recent general purpose simulation environment for WSNs built on top of the OMNeT++ platform. Castalia is modular and extensible. Its strongest features are the provision of accurate wireless channel and radio modelling, including MAC. C. GloMoSim GloMoSim (Global Mobile System Simulator) [15] is a scalable simulation environment developed for mobile wireless networks. It is designed using the parallel discreteevent simulation capability provided by PARSEC, which is an extension of C for parallel programming. The ability to use GloMoSim in a parallel environment distinguished it from most other simulators. There is a wide variety of protocols and models available in GloMoSim by default including TCP, IEEE 802.11 CSMA/CA, MAC, UDP, HTTP, FTP, CBR, ODMRP, WRP, DSR, MACA, Telnet, AODV, etc. It uses a VT visualization tool to view and debug these protocols. Like NS-2, GloMoSim is also designed to be extensible, with all protocols implemented as modules in the GloMoSim library. It also uses an object-oriented approach; however, the designers realized that using a purely object-oriented approach would not be scalable. Instead, GloMoSim partition the nodes, and each object is responsible for running one layer in the protocol stack of every node for its given partition. This approach helps to minimize the overhead of a large scale sensor network [6].
GloMoSim is easily configurable and provides fast simulation due to the parallel processing nature. A wider range of protocols are implemented and available in GloMoSim. It has an open source and very well documented source code. GloMoSim performs effective simulation for IP Networks; however, it is not capable of simulating any other type of network. Since WSNs are data centric network; hence many sensor network applications cannot be simulated accurately in GloMoSim. Finally, GloMoSim stopped releasing updates since 2000. It is now updated as a commercial product called QualNet [3], which is not freely available. D. OPNET OPNET [16] (Optimized Network Engineering Tool) was launched in 1987 as a first commercial simulator for communication networks by OPNET Technologies, Inc. OPNET is an object-oriented, discrete-event, general purpose simulator. It uses C and Java languages. It is very popular because it provides a comprehensive development environment for configuration, specification, simulation and performance analysis of the network [5]. OPNET was originally built for the simulation of fixed network; therefore, it contains extensive libraries of accurate models from commercially available fixed network hardware and protocols [7]. OPNET allows users in developing the various models via a graphical interface and contains various tools (such as Probe Editor, Filter Tool and Animation Viewer) for data collection to model graph, and animate the resulting output. Unlike NS-2 and GloMoSim, OPNET can support to model different sensor-specific hardware, such as physical-link transceivers and antennas. OPNET also contains sensorspecific models such as mobility of nodes, ad-hoc connectivity, node failure models, modeling of power-consumption, etc. OPENT is easily extensible, having large customer base and provides professional support. OPNET code is very well documented and ships with a large number of built-in protocols. However, OPNET suffers from the same objectoriented scalability problems as ns-2. It has a complex architecture and takes time to learn. It also has portability issues, which need to be addressed [5]. The main drawback of OPENT is that it is only commercially available and acquiring a license is very expensive. E. SENSE The SENSE [17] (Sensor Network Simulator and Emulator) is designed to be an efficient and powerful network simulator that is also easy to use. The primary design goal is to address factors such as extensibility, reusability, and scalability and to take into account the needs of different users. SENSE attempts to implement the same functionality as NS-2. However, it deviates by using the object-oriented approach and use J-Sim's simulator component based architecture. Similar to GloMoSim, SENSE also includes support for parallelization. Through its component-based model and support for parallelization, the developers attempt to address what they consider to be the three most critical factors in simulation: extensibility, reusability, and scalability. SENSE was developed in C++, on top of COST, a generalpurpose discrete event simulator. SENSE introduced a
component-port model that frees simulation models from interdependency usually found in an object-oriented architecture. This allows independence between components and enables straightforward extensibility and reusability. There is another level of reusability made possible by the extensive use of C++ template: a component is usually declared as a template class so that it can handle different types of data. The designers tried to improve scalability by having all sensors use the same packet in memory, assuming that the packet should not have to be modified. SENSE's packet sharing model is an improvement on NS-2 and other object-oriented models; however, SENSE is still in its active development phase. Although the core of the simulator has been gradually stabilized, it still lacks a comprehensive set of models, routing protocols and a wide variety of configuration templates for WSNs. Besides, a visualization tool is desirable, which can quickly track down what goes wrong during the simulation [17]. F. TOSSIM TOSSIM is mainly regarded as an emulator rather than a simulator. An emulator has the ability to simulate both the hardware and software aspects of a model [18]. They are also different from simulators in that they run actual application code. TOSSIM is especially designed for TinyOS applications to be run on Berkeley MICA Motes. Motes are tiny sensing and computational devices that have very limited communication, computational and energy resources. TinyOS sensor networks are often composed of large numbers of these little hardware devices. TinyOS [18] is an open-source operating system specifically developed for embedded WSNs. There are few hardware platforms available for TinyOS, some of them are commercial and some non-commercial. TinyOS release includes a simulation tool called TOSSIM [19]. TOSSIM was developed having four key requirements: scalability, completeness, fidelity and bridging. To be scalable, a simulator should manage networks of thousands of nodes in a wide variety of configurations. For completeness, a simulator must capture behaviour and interactions of a system at a wide variety of levels. For fidelity, a simulator must capture behaviour of a network with a subtle timing of interactions on a mote and between motes. Requirement for bridging is met as the simulated code runs directly in a real mote [7]. TOSSIM supports two programming interfaces: Python and C++. Python allows interacting with a running simulation dynamically, like a powerful debugger. However, since the interpretation can be a performance bottleneck when obtaining results, TOSSIM also has a C++ interface. Usually, transforming code from one to the other is very simple. Currently, TOSSIM provides a scalable, high fidelity simulation of a complete TinyOS sensor network. It also has a GUI tool, TinyViz, which can visualize and interact with running simulations. Using a simple plug-in model, users can develop new visualizations and interfaces for TinyViz. However, TOSSIM's probabilistic bit error model leads to inaccuracies, and reduces the simulator's effectiveness in
analyzing low level protocols [3]. Furthermore, TOSSIM is only supported by MICA motes platform. G. GTSNetS Georgia Tech Sensor Network Simulator (GTSNetS) [2, 12] is a simulation tool that enables the development and evaluation of algorithms for large-scale WSNs. GTSNetS is a C++, fee open-source, event-driven simulator. It allows users to evaluate the effects of different architectural choices and strategies on the lifetime and performance of a particular sensor network. It can also be used to evaluate new approaches such as new sensor network algorithms and network protocols. GTSNetS is built on top of the Georgia Tech Network Simulator (GTNetS), inherits and extends all the design decisions of the existing GTNetS simulator. The authors testified that GTSNetS is the only sensor network simulator capable of handling networks of several thousand nodes (up to 200, 000 nodes). GTSNetS is entirely based on the C++ language using an object-oriented methodology. It has been structured in a modular and efficient manner; leading to the capability to simulate large-scale WSNs. It is designed in such a way that it would not impose any architectural or design decisions on the user who wants to simulate a particular sensor network. GTSNetS provides an opportunity for a researcher to choose from various implemented alternatives: different energy models, network protocols, applications, and tracing options. Several different methods for each of these choices are included in the baseline implementation. Furthermore, the existing models can easily be extended or replaced for a specific need. GTSNetS provides the capability to track the overall lifetime of a simulated network. It also measures the energy consumption of each one of the functional units and provides detailed statistics for each, allowing the researchers to study the effect of different architectural choices on lifetime and energy consumption. GTSNetS inherits mobility support from the existing GTNetS and thereby, allows specification of mobile sensor nodes, moving sensed objects, as well as a mobile base station. A distinctive feature of GTSNetS is that it can be used to simulate networks of several hundred thousand nodes (up to 200, 000 nodes). The graphical user interface provided with GTSNetS supports the graphical representation of the simulation topology. GTSNetS provides extensive packet tracing by default. User is given a wide set of tracing options: the different types of energies (example sensing) at a node level as well as network-wide, node and network lifetime, the sensed data, location and if the node is dead or still alive. Finally, we can conclude GTSNetS is the most suitable simulator for large-scale WSNs with a wide variety of available sensor models. However, one of the potential drawbacks is that they have been halted updating and maintaining the project since Oct, 2008.
IV.
DISCUSSION
This paper provides an insight to the most popular, wellestablished and widely-used simulators for WSNs. We emphasize to on the key aspect of their suitability for simulating large-scale WSNs. The simulation tools presented in this paper are NS-2, OMNeT++, GloMoSim, OPNET, SENSE, TOSSIM and GTSNetS. Concerning simulation tools for WSNs, there are a number of surveys and comparisons [3, 5, 6, 8, 9, 20] available. A detail survey of various simulation tools is presented in [3, 6, 21]; however, they only gave a general overview of different tools along with their strengths and weakness. The survey presented in [9] differs with respect to its selection criteria of simulation tools as they review various common simulators based on different features such as their popularity, the available modules, and GUI supports, etc. However, they do not consider the availability of sensor networks' specific modules and the suitability of a simulation tool for large-scale WSNs. The distinguishing feature of our work is that we review and investigate simulation tools based on their popularity, accessibility (open-source), complexity, accuracy, extensibility, availability of various models and protocols and its suitability for large-scale WSNs. These factors form the basis of the criterion for the suitability of a simulation tool for large-scale WSNs. In this survey, we found that the famous simulation tools, e.g. ns-2, OMNeT++ and GloMoSim, need to be extended and further modified for more accurate WSNs simulation. Defaultsupplied protocols, energy models, MAC layer protocols and environmental models are too simplified or unsuitable for WSNs. Many of the simulators (e.g., TOSSIM) are developed for a specific modeling task in which they are accurate and appropriate, e.g., only a certain type of routing protocol, MAC, energy and physical model has been implemented. Ns-2 having the attraction of being open-source software, but suffers from poor presentation of results and ways to analyse them. Furthermore, Ns-2, GloMoSim, and OPNET can be used to simulate certain aspects of a sensor network. However, these simulators do not model many important features of sensor networks such as sensed object, realistic energy models, accuracy models, and sensor network specific communication protocols. By reviewing the related work, we can determine that different simulation tools present different results within the same simulation model. The accuracy of results [4, 7-9, 20, 22] mainly depend on protocol stack, nature of application, traffic generation parameters, incorrect parameters settings and unrealistic simulation assumptions. Most specifically the simulation assumptions made by the tools have a huge effect on research outcomes. Thus, for the effective development, design and testing of any architecture and protocol for largescale WSNs by using simulation. It is imperative to gain appropriate knowledge about different simulation tools along with their strengths and weaknesses.
TABLE I.
COMPARISON TABLE OF THE REVIEWED SIMULATION TOOLS
Tools Interface
Accessibility & User Support
Availability of WSNs Modules
Extensibility
Scalability
Energy Model, battery model, Mobility
Excellent
Limited
Excellent
Large-scale
Good
Large-scale
Features NS -2
C++/OTcl with limited visual support
Open source with Good user support
OMNeT++
C++/NED with good GUI and debugging support
Free for academic use, licence for commercial use with Good user support
Energy Model, battery model, accurate wireless channel and radio modelling
GloMoSim
Parsec (C-Based) with limited visual support
Open source with Poor user support
Sensor network specific MAC and network protocols, mobility model
OPNET
C or C++/Java with Excellent GUI and debugging support
Free for academic use, licence for commercial use with Excellent user support
Energy model, battery model, Routing protocols (directed diffusion), Mobility, node failure model
Excellent
Moderate
SENSE
C++ with good GUI support
Open source with Poor user support
Energy models, battery models, Mobility, modelling of physical environment
Excellent
Large-scale
Open source (BSD) with Excellent user support
Energy models with power TOSSIM ads-on, Bit-level radio model
Good
Large-scale
Open source with good user support
Energy model, battery model, accuracy model, model applications, Mobility
Excellent
Very large-scale
TOSSIM
C++/Python with good GUI support
GTSNetS
C++ with good user interface & visual support
Sundani et al. [23] carried out a case study to demonstrate a performance comparison of one specific application for various sensor network simulators such as NS-2 and TOSSIM. In this case, a simple application is simulated to broadcast a message every 250ms, with the size of the simulated area is 500x500 length units. A number of simulations with increasing node count were performed. The obtained results present a measurement to show the required CPU time and memory for each simulator Fig. 1. The experiment results show that the performance of NS-2 is good for 100 nodes, which decreases significantly as the number of nodes increases. TOSSIM follows a linear curve.
performance and lifetime of a given sensor network. In addition, the designed experiments demonstrate the scalability of GTSNetS and its capability of simulating very large sensor networks. The experiment starts by simulating a network of thousands of nodes deployed in a small region and gradually increases the number of nodes while increasing the size of the region of deployment until reaching the limit that can be handled by the simulator. Experiments (Fig. 2) confirm that GTSNetS has the least memory consumption and excels in its specialty: the simulation of large-scale sensor networks and extensibility.
Figure 2. Network Size VS Network lifetime
Figure 1. Number of Nodes Vs CPU Time
Authors in [2] perform a series of experiments to prove that GTSNetS is an affective simulation tool to study the
Almost all of the reviewed simulation tools allow extensibility; customized extensions can be written or downloaded from the Internet for better WSNs simulation. Simulation of Mobile WSNs with dynamic topology changes is also possible in all of the presented simulators except TOSSIM. NS-2 provides extensibility and reusability, but when considering scalability to large-scale WSNs, NS-2 lags behind
the other reviewed simulators. In WSNs the level of scalability is of great importance as recent years have seen a tremendous increase in the size of deployed senor networks. The size of these networks grew from few tens of nodes to few hundred and over a thousand for the ExScale [12] project. The ExScale border monitoring project has a final target deployment of 10, 000 nodes. GTSNetS is currently, to the best of our knowledge, the most scalable simulator, specifically designed for sensor networks, which can simulate networks of up to several hundred thousand nodes (scale to network up to 200,000 nodes on a single core workstation). It also supports the simulation of network control systems having sensing, control and actuation capabilities, which have been lacking in other sensor network simulators. Moreover, GTSNetS is distributed under the GNU General Public License and is freely available. In-fact there is no ―all–in-one‖ simulator available for WSNs. Each simulator exhibit different features and models; each has advantages and disadvantages. In choosing a simulation tools from available choices, user should be looking into the requirements and specific needs of the experiment. Different simulators are appropriate and most effective in different situations based on the nature of designed application. Therefore, it is very important for a researcher to choose a simulator that is best suited for his project and targeted application. It is also recommended that he should consider the pros and cons of different simulators, the level of complexity of the simulator, availability, extensibility and scalability. Usually, WSNs applications consist of a large number of sensor nodes, therefore, based on our analysis it is recommended to use the simulation tool which is capable of simulating large-scale WSNs. We also conclude that GTSNetS is currently the most scalable simulator specifically designed for WSNs having built-in energy model, battery model and accuracy models to give reliable results for large-scale WSNs. V. CONCLUSION In this paper, we reviewed and investigated current simulation tools for WSNs. Based on our study of the related papers, we can determine that, most of the survey and review papers only gave a general overview of different simulation tools along with their strengths and weakness. However, they overlook to consider the limitations and suitability (i.e. scalability factor and availability of WSNs specific modules) of a simulation tool for large-scale WSNs. Our overall conclusion from this survey is that scalable and easily extensible simulation tools are needed for WSNs. The tools should provide an easy-to-use interface and easy-tounderstand architectural design for a researcher, so that the user can efficiently use it to model and validate their own designs and architectures. Besides, it is also important to decide a simulation tool by the availability of sensor network specific modules and the reliability of results. Based on our investigation we conclude that GTSNetS is currently the most scalable simulator specifically designed for WSNs having built-in energy model, battery model and accuracy models to give reliable results for large-scale WSNs. We hope that this paper will help the researchers to choose an appropriate simulator for their validation while taking into account their limitations and strengths.
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