specific requirements for simulating wireless sensor ... sensor networks is the military application [3]. ... technology that has changed the way of how people.
Simulating Wireless Sensor Networks Ittipong Khemapech, Alan Miller and Ishbel Duncan School of Computer Science University of St Andrews North Haugh, St Andrews Fife, KY16 9SX, Scotland Email: {ik,alan,ishbel}@dcs.st-and.ac.uk Abstract Wireless Sensor Networks were first used in military missions. They are currently deployed in a wide range of civil applications as sensors are becoming smaller and cheaper. The main limitation is the energy constraint as it seems impractical to change or recharge the battery. Several applications require an end-to-end reliable data transport with congestion control to achieve an intended performance, especially during heavy traffic. To study sensor networks behaviour and performance by means of deployment or setting up a testbed may require much effort and financial resources. Various network simulators have been newly developed or extended to simulate sensor networks. This paper examines several research works on sensor network simulators development and simulating wireless sensor networks. Both are summarised and compared in different sections. Further, both general and specific requirements for simulating wireless sensor networks are also provided. Key Words: Wireless Sensor Networks, Reliable Transport Protocol, Congestion Control, Network Simulator
1. Introduction Wireless sensor networks are one of the most interesting research areas with a profound effect on technological developments [1]. With the significant breakthrough in technology called “Microelectromechanical Systems (MEMS)” technology [2], sensors are becoming smaller. It is possible to fit them into a smaller volume with more power and with less production costs. Many sensors can be deployed in harsh environments to sense and periodically transmit data to the sink or base station. The main driving force behind research in wireless sensor networks is the military application [3]. However, there is a diversification towards the
development of civilian applications such as environmental monitoring [4,5], habitat monitoring [6,7], classroom/home application [8,9], structural monitoring [10,11] and health monitoring [12,13]. According to their application-specific characteristic, each application has its own design concept and implementation to suit specific requirements. For several applications such as re-tasking or reprogramming of sensors, reliability of data transmission is required. Various reliable transport protocols [14-16] and congestion control approaches [17-20] have been reviewed. However, providing reliable data transmission and congestion control seem insufficient to serve such requirements of current applications. Supporting a wide range of applications, lower layer independence and energy preservation are also essential. A new generic, lightweight reliable transport protocol with congestion control development is the focus of our current research. Sensor networks research has been evolving and their characteristics are quite different from the traditional wired network. Hence, such requirements on simulating their behaviour need to be differently addressed. Newly developed work possibly has profound effects on both performance of the existing infrastructure and user which have to be tested and evaluated. Testing and evaluating sensor networks by analytical study, small-scale deployment or setting up a testbed is complex, costly and time-consuming. Existing widely-used network simulators such as ns2 [24] have been extended to simulate sensor networks. Newly specific simulators for sensor networks have been continually developed [30-43]. An open-source network simulator such as ns-2 allows any users or researchers to develop their own modules and then link to the core system. This paper aims to provide a survey of network simulator researches in wireless sensor networks. Both general and specific simulating requirements are determined in order to consider the most suitable
network simulator for the new protocol development. Each approach will be briefly described separately and then evaluated with respect to each other. Simulating sensor networks, existing network simulators, simulating TinyOS, discussion, evaluating existing protocols, and related works are outlined in Section 2, 3, 4, 5, 6 and 7 respectively. Finally, some conclusions are stated in Section 8.
2. Simulating Sensor Networks Sensor networks have been developed and deployed in various civilian applications. Research fields in this area include increasing the potential of hardware components in terms of smaller size and less energy consumption, operating system, protocol and application. Traditionally, each work has to be tested and evaluated to ensure achievement of the predefined objectives. There are several ways to determine results such as small-scale experiment in a lab, wide-area testbed and custom simulation [25]. Unlike traditional networks, sensor networks are application-specific and may compose thousands of resource-constrained sensors. Therefore, conducting a simulation seems to be the most efficient way to obtain a preliminary result. Several simulators are described in this section.
2.1 Network Simulating Motivation Networking plays a major role in today’s communication worldwide. Several underlying technologies become involved and are continually evolving. The Internet is one of the obvious phenomena reflecting the profound networking technology that has changed the way of how people communicate. The number of the Internet users is exponentially increasing and the trends in application use are becoming hard to predict. Newly developed application software and protocols may be widelyused even in couple months. Further, heterogeneity in topology, link properties, and protocols [22] are obstacles to fully understanding how the Internet works. The Virtual InterNetwork Testbed (VINT) project was initiated and performed by USC/ISI, XeroxPARC, LBNL and UCB. The objectives of this project are to develop methods and tools to study protocol operations in both high level including interaction and scaling, and low levels such as congestion control, reliable multicast, multicast routing and dynamic topologies. Finally, an increase in the quality of analysis and rate of progress in protocol development can be achieved by providing a common simulation infrastructure [23].
2.2 Requirements for Simulators Simulating wireless sensor networks requires more specific properties to reflect the real operational environment. This section provides both general and specific requirements to effectively simulate wireless sensor networks. Non-functional and functional requirements are the general requirements which each simulator should address. Specific requirements are such characteristics needed to test and evaluate the new generic, lightweight and reliable transport protocol for wireless sensor networks. 2.2.1 General Requirements The general requirements are some of the basic characteristics which are essential to simulate ordinary sensor networks. Two groups of this requirement are described including non-functional and functional requirements. Non-functional Requirements The non-functional requirements are: • Open Source – Publicly available simulators allow various users to freely develop their own contributed modules. This supports rapid enhancement, but bug reports and contributing report forms are required to keep the developing information updated. • Platform Independence – A simulator should support all sensor platforms. An ability to simulate different platforms would support a wide range of sensors developed by various communities. • Visualisation Module – A friendly visualised and/or animated environment displaying results should be provided. For example, node mobility, data packet and energy level should be displayed at different timeline. This could promote better understanding and interpretation of the result. Functional Requirements Several functional requirements are provided: • Hardware Simulation Coverage – A network simulator should be capable of simulating all the hardware of a sensor such as CPU, transceiver and sensing unit. This could reflect performance of each component and also interaction amongst them. • Battery and Power Models – Resource constraint is still a major drawback of sensor networks. All of the energy comes from a tiny battery and each operation needs different energy levels. With both models, a picture of energy consumption and energy depletion can be seen and forecasted.
•
•
•
•
Propagation Modelling – All reviewed simulators support only radio frequency (RF) modelling. This may be because this medium is currently the most widely used by deployed sensor networks. However, other models such as optical communication (laser) and infrared should be also determined to support various requirements. Protocols Modelling – A large number of protocols at different network layers have been developed. It is impossible to include all protocols but it should include all such common Internet protocols such as TCP and UDP. Current routing protocols for ad hoc mobile wireless networks such as Destination Sequenced Distance Vector (DSDV), Ad-hoc On Demand Vector (AODV), Dynamic Source Routing (DSR) and Temporally Ordered Routing Algorithm (TORA) should also be included. However, providing a convenient API for defining new protocol in a simulator would be another effective approach. Physical Environment Modelling – Sensors will be scattered in/on various placing areas such as soil, water, cement surface or human body. Different physical areas have different signal propagation characteristics. Radio signal changes when travelling through different physical media. Emulation – There are two approaches to address such deficiencies of simulation through real-world interaction including network and environment emulations [28]. In the network emulation approach, simulated entities are facilitated to communicate with the real-world entities such as protocol implementation. Another approach, the environment emulation, an implementation of the real-world entities is built in order to be directly executed within the simulator. This requirement will then promote better understanding of the network behaviour. More accurate results will also be obtained.
2.2.2 Specific Requirements A new transport protocol for wireless sensor networks is needed to provide an end-to-end reliable data transport with congestion control. This protocol should be generic or at least multipurpose to support a wide range of applications with minimal energy consumption. This section provides several specific requirements in order to analyse the new protocol performance. To test and evaluate performance of the new protocol, a network simulator with several capabilities will be used to obtain a preliminary result. Various modules will be implemented and
then linked to the existing system. Hence, the simulator has to be open-source with some guidelines provided to contribute a new feature. Energy preservation is one of the major characteristics of the new protocol. Each process requires different level of energy. Battery and power models could reflect energy status of a sensor at any time. Since the most widely used propagation model in sensor networks is RF, the new protocol will only focus on RF technology. Several routing protocols for sensor networks have been developed and currently widely. To achieve the multipurpose properties, the new transport protocol will be tested by running over DSDV, AODV, DSR and TORA. Another famous routing protocol, the Directed Diffusion may also be involved in the experiment. The standard MAC protocol will be used as a link layer. In some situations, a user may access sensor networks via the Internet to see sensory data or use such services provided by an application. An interaction with the Internet protocols should be observed. Finally, visualised and animated results are required in order to nicely understand protocol behaviours.
3 Network Simulators 3.1 The Network Simulator – ns-2 Amongst the existing network simulators, ns-2 [22] is an open-source, discrete-event simulator and is one of the most widely used tools in the networking research community. It was developed within the VINT project in 1995 which attempted to provide an efficient simulation tool to facilitate new protocol design [27]. Current ns-2 users come from several universities and research communities. It also provides much useful information on its website [24] such as downloading and installation guides, examples, tutorials and on-line manuals, development help, and mailing lists. The ns-2 includes several common protocols for the Internet such as TCP and UDP. Moreover, network emulation is included in ns-2 to simulate real-world interaction [28]. In the case of mobile networks, ns-2 provides several ad hoc routing protocols including DSDV, AODV, DSR, and TORA. Further, ns-2 includes an application module named Network Animator (nam) to provide a visualisation result. Energy consumption and depletion are essential data for operational lifetime analysis; battery and power model have been included to reflect the current energy level of mobile node. Several previous works on reliable transport protocol [14-16] and congestion control [17] used the ns-2 to evaluate their performance.
Figure 1 and Figure 2 illustrate a full trace file generated by ns-2 and a visualised result, respectively. Node movement could be addressed in ns-2 to reflect the real behaviour of a sensor. In this example, there are three mobile nodes (node 0, 1 and 2) transmitting ftp packets. The thick and thin strips demonstrate a ftp packet and an acknowledgement, respectively. Although ns-2 is a powerful network simulator, some researchers claimed it has some drawbacks for simulating wireless sensor networks [31,41]. An object-oriented design in ns-2 may introduce some unnecessary interdependence between modules which may lead to difficulties in a new protocol addition [31]. Finally, ns-2 has been developing for about 10 years; it has quite a large and expanding infrastructure which leads to a steep learning curve for a novice user.
Figure 1. Full Trace File Generated by ns-2
components, for example, IEEE 802.11, AODV, DSR, Battery and Power Models. The developers claim it is memory-efficient, fast, extensible and reusable. Several experiments were operated in order to compare its performances to those of ns-2 such as event processing rate and frequency of packet allocation. Unlike ns-2, a packet is shared by all receivers. Hence, the number of packet allocations will be the number of sent packets which is less than that of received packets as in ns-2. However, SENSE provides less routing protocols comparing to ns-2. No visualisation tools appear in SENSE.
3.3 GloMoSim GloMoSim (Global Mobile System Simulator) [31] is a simulation environment for large-scale wireless networks up to million nodes based on parallel execution. It consists of a set of library modules which were developed using PARSEC (PARallel Simulation Environment for Complex Systems), a C-based parallel simulation language for evaluating a variety of wireless network protocols. GloMoSim implements a network gridding concept to overcome such memory requirement problems in case of a network consisting of thousands of nodes [32]. Several models of various layers are included in the library such as DSR, TCP, UDP, Telnet and FTP. An actual operation code could be integrated into GloMoSim, for example, by extracting a code for TCP model implemented in the FreeBSD operating system. Information about battery and power models, emulation and visualisation do not appear in [31]. A simulating experiment using GloMoSim has been found in [19]. Some experiments to evaluate real-time packet scheduling and prioritisation protocols on sensor networks for biometric sensing application were conducted. Two routing protocols including DSR and GF were implemented. Several metrics such as deadline miss ratios and distance fairness were measured and analysed.
3.4 SENS
Figure 2. Visualised Result by nam
3.2 SENSE SENSE (SEnsor Network Simulator and Emulator) [30] design is based on a componentoriented simulation methodology. It provides several
One of major basic requirements for a network simulator is to provide a customisable feature. SENS (Sensor, Environment and Network Simulator) [33], is a platform-independent and has a modular, layered architecture which is capable of modelling the application, networking and physical environment. The ability to model physical environments by defining them as a grid of interchangeable tiles is a core strength of SENS. Three modelling implementations with different signal propagation characteristics including concrete, grass and walls are currently available. A source code for a simulated sensor which is executed in SENS could be deployed
on an actual node. The authors claim this capability would enable application portability. However, the existing power model needs an improvement to include a battery model. No details of routing protocols, emulation and visualisation module are mentioned in [33]. Further, no manual has been published.
3.5 SensorSim Taking some advantages of ns-2, SensorSim [34] was to provide new power and designed communication protocol models, and to support hybrid simulations and provide a new graphical user interface. The power model consists of a battery, radio, CPU and sensor device models to reflect the energy consumptions in a real environment. Further, there are several modes of operation in each model. For example, there are five different modes for radio models including Transmit, Receive, Idle, Sleep and Off. SensorSim also supports hybrid simulation which was claimed to be less complicated to implement comparing to network. Unfortunately, this project has terminated with incomplete tasks [36].
3.6 ATEMU One of the most widely-used sensors in market is the MICA2 platform developed by UC Berkeley. ATEMU (ATmel EMUlator) [35], an open source tool, was built as a software emulator for AVR processor based systems such as MICA2 and its peripheral devices to simulate any operations of various applications on the MICA2 platform and could be extended to different platforms. ATEMU is able to emulate the operation of various components such as processor, timer and radio interface. Further, it could also be used to develop alternate operating systems for various sensor platforms. The two main packages are developed. The first is an emulator tool for CPU and hardware emulation which can be attached to the CPU to specify a particular sensor node platform. The second provides a graphical front-end debugger, XATDB which supports users’ learning. Either assembly level or C level code could be stepped through to better understand the process. Further, XML is used to develop a common sensor network definition specification framework. However, there is no information about the battery and power models in [35].
3.7 OMNeT++ OMNeT++ (Objective Modular Network Test-bed in C++) [36] is an open-source, discrete-event network simulator which has initially developed to simulate 802.11 MAC and Directed Diffusion protocols. It consists of hierarchically nested modules and provides several hardware models including
battery, CPU and radio model. OMNeT++ has some similarities to ns-2. First, it was designed by an object-oriented approach. It also provides a community site [37] providing several information such as related publication, on-line detailed manual, models forum and mailing lists. Finally, graphical results could then be presented. Like all of the reviewed simulators in this paper, OMNeT++ supports only the RF propagation model. However, the paper does not provide any information about emulating capability.
3.8 Prowler Prowler [38] is an event-driven simulator which was designed to simulate a mote-based sensor networks. A Berkeley field-node (mote) includes microcontroller, program memory and radio chip. It also accommodates a set of sensors such as sound and temperature to support various applications. Prowler provides two modes of operation including a deterministic mode and a probabilistic mode. Some replicable results could be produced in a deterministic mode. On the other hand, a nondeterministic nature of the communication channel and low-level communication protocol could be simulated in a probabilistic mode. Prowler includes several models including radio propagation, signal reception and collisions, MAC layer and application level models. The nondeterministic nature of a radio propagation and noise variance parameter could then be simulated. This simulator runs under MATLAB and has visualisation capabilities.
3.9 J-Sim J-Sim [39] is an open source and componentbased compositional network simulation. It was developed in Java, therefore it has several characteristics such as platform-independence, extensibility and reusability. It is implemented on top of an autonomous component architecture (ACA) which is a component-based architecture facilitating components communication. Another underlying layer of J-Sim, a generalised packet-switched internetworking framework (INET) provides several common features extracted from protocol stack. Like ns-2, J-Sim is a dual-language simulator. Its classes were written in Java and J-Sim can integrate with several languages such as Perl, Tcl or Python. Nodes and wireless communication channels can be modelled by the object-oriented definitions provided by J-Sim framework. Moreover, the framework is also developed to simulate physical media, mobility and power models. Finally, a required applicationspecific model can be customised by sub-classing appropriate classes including in the framework and
then customising their behaviours to simulate the requirements.
3.10 Shawn A newly developed event simulator, Shawn [40] is open source and designed to support large-scale network simulation. Instead of simulating a phenomenon, Shawn is designed to simulate the effect of the phenomenon. It is claimed to provide the highest abstract level and support larger network comparing to other simulators such as ns-2, SENSE, OmNeT++, GloMoSim, and TOSSIM. However, details on simulating wireless sensor networks cannot be found. Table 1 provides a summary of various network simulators.
4.1 TOSSIM TOSSIM [41], a discrete event simulator, was designed and developed to simulate TinyOS wireless sensor networks. The TOSSIM architecture is composed of five parts such as support for compiling TinyOS component graphs into the simulation structure and a small number of re-implemented TinyOS hardware abstraction components. TOSSIM takes advantages of TinyOS to directly generate simulations. It can run the same code on a real sensor. Along with capability to simulate an application, operating system and network stack, TOSSIM is likely to provide more realistic results. With a detailed visualisation module, results could then be easily understandable.
4.2 OPNET
4. Simulating TinyOS TinyOS, developed at UC Berkeley, is an operating system specifically developed for sensor networks. Its system, libraries and applications are written in nesC language which is intended for embedded system. This section describes two tools which enable simulating TinyOS applications. The driving force behind this development was to have a tool enabling to simulate an actual implementation which includes a TinyOS interaction.
OPNET [42] is capable of simulating TinyOS applications. It enables scenario and statistics management which could not be found in TOSSIM. OPNET also allows instantiations of different applications to be simulated in the same memory space and provides link models. New OPNET models for each TinyOS application will be created. The models are the combination of OPNET specific code implementing TinyOS functionality and application specific code. This characteristic will reflect the interaction between the application and TinyOS.
Table 1. Summary of Network Simulators Properties Simulator
Open Source
Battery Model
Power Model
Visualisation Module
Emulation
Propagation Model
Yes -
Yes Yes
Yes Yes
Yes No
Yes -
RF RF
-
-
-
-
-
RF
Yes
-
Yes
-
-
RF
-
Yes
Yes
-
RF
6. ATEMU
Yes
-
-
Yes
Hybrid Simulation Yes
7. OMNeT++
Yes
Yes
Yes
Yes
-
RF
8. Prowler
Yes
-
-
Yes
-
RF
9. J-Sim
Yes
Yes
Yes
-
-
RF
10. Shawn
Yes
-
-
-
-
-
1. ns-2 2. SENSE 3. GloMoSim
4. SENS
5. SensorSim
RF
Comments - Steep curve of learning - Less frequency of packet allocation - Specially designed for largescale network and based on Parsec - Provide physical environment Modelling - Provides power utilisation analysis - Incomplete - Mainly supports MICA2 platform - Provide graphical debugger - XML based - First developed to support Directed Diffusion and 802.11 MAC - Supports MICA platform - Supports deterministic and probabilistic modes of operation - Runs under MATLAB - Written in Java - Provides node mobility model - Newly developed - Supports high-scale network - No wireless sensor networks simulation mentioned
4.3 TOSSF The design of the TinyOS Scalable Simulation Framework (TOSSF) [43] was driven to support simulation of Smart Dust project initiated by UC Berkeley. Very tiny motes are being developed to remain suspended in the air. TOSSF provides an environment to simulate TinyOS applications by allowing direct execution at the source code level. There are several limitations of TOSSIM stated in [42] including inability to mix different applications in the same simulation run, only two approaches provides to model radio signals (perfect or totally broken), and no performance stability to run largescale systems. TOSSF was built up on two existing projects including DaSSF (Darthmouth Scalable Simulation Framework) and SWAN (Simulator for Wireless Ad-Hoc Networks). DaSSF provides a streamlined and optimised simulation kernel whilst SWAN offers a range of models for simulating wireless ad hoc networks. TOSSF provides some set of scripts which could adapt the source code for execution in the simulator. However, TOSSF is currently in the developing stage and has not been ready for a public release.
5. Discussion There are several schemes to study wireless sensor networks. Analytical study seems to be difficult because of the high varying characteristics of a network. A small-scale testbed in a lab and full-scale deployment may be costly because the price of a sensor is quite high. Further, a large number of sensors will be in an experiment because a sensor is prone to failure and easily runs out of energy. Network simulation is considered to be cost-effective way to gain a preliminary result with some limitations. Several network simulators have been reviewed in this section. The ns-2 is the most widely used simulator and more modules for simulating sensor networks are continually added. Several new simulators have been built, especially for sensor networks. However, they are quite new and lack some necessary features and documents. To simulate an entire application environment including TinyOS and the network stack seems to be an efficient way to obtain more accurate results compared to the traditional approach. However, more study on how TinyOS works is needed and may result in more learning efforts. To develop a new reliable transport protocol, ns-2 will be used to preliminarily study its performance by developing C++ modules and then linking to the core system. A deploying experiment in a real environment should then be conducted to validate the
simulation. Replicating other papers’ works could help to ensure how to use ns-2 in a correct manner.
6. Evaluating Existing Protocols From the previous section, ns-2 is evaluated to be the most suitable network simulator to test and evaluate the new research works for wireless sensor networks. This section provides a preliminary plan for evaluating the reviewed transport protocols.
6.1 Area of Interest As mentioned earlier, several reliable protocols for wireless sensor networks including Pump Slowly, Fetch Quickly (PSFQ) [14], Event-to-Sink Reliable Transport Protocol (ESRT) [15] and Reliable MultiSegment Transport (RMST) [16] have been developed. All of them were evaluated by using the ns-2. However, each protocol has several strengths and weaknesses which have to be studied in details. The ns-2 will be used to study several performances in terms of various metrics as follows: • Efficiency – Throughput, power utilisation and number of retransmissions will be investigated. Energy limitation is one of the major drawbacks in wireless sensor networks. Transport protocol should operate consuming minimise energy as possible. • Fairness – How well sensors at the far side of the target area can transmit data to a sink will be measured. • Robustness/Stability – During an attack or disaster, a lot of data will be transmitted over the network. Various sensor networks composing of different amount of sensors will be simulated. Simulating a large-scale sensor networks under heavy traffic could reflect robustness/stability characteristic of each approach. • Flexibility – Each protocol will be run over various routing protocols. Difference and similarities in results could be observed.
6.2 Experimental Plan All the reviewed reliable transport protocols for wireless sensor networks were simulated using the ns-2. Several simulating experiments appeared in reliable transport protocol [14-16] and congestion control researches [17-20]. Regarding the simulators used, ns-2 was used in [14-17] whilst GloMoSim appeared in [19]. No simulator is mentioned in [18] and [20]. In our experiments, the existing protocols will be evaluated by using ns-2. The various strengths and weaknesses of each protocol will be concluded. The obtained results will help in designing the new protocol.
Apart from studying transport protocol for wireless sensor networks, a multipurpose reliable transport protocol for the Internet will also be studied. Applying the Transmission Control Protocol (TCP) will bring about several interesting issues such as energy consumption which should be investigated. The experimental study will be based on comparison amongst existing protocols. Four protocols including PSFQ, ESRT, RMST and TCP will be run over routing protocols (DSDV, AODV, DSR and TORA) included in the ns-2. Developed as a filter and run over the Directed Diffusion, it would also be tested to evaluate RMST. By stating the required routing protocol in a command script, differences in output could then be observed. 6.2.1 Number of Nodes Various number of sensor nodes will be varied to measure robustness or stability of each protocol. The number of maximum nodes was limited to 2,000 in [40] as it took more than 25 hours for running times. Some preliminary experiments detecting the maximum nodes affordable by the ns-2 with fixed area size will be conducted. 6.2.2 Nodes Density In some circumstances, a large amount of sensors may be needed in a small area. Further, sensors communicate with each other by transmitting radio signals. Radio signal naturally varies itself and interference may occur. Transport protocol performance due to these effects should be investigated. 6.2.3 Nodes Mobility In some applications such as habitat monitoring, sensors are implanted into animal’s body to track the travelling routes. Nodes mobility is supported in ns2. Although a routing protocol plays major roles in this issue, some interactions with transport protocol should be studied. 6.2.4 Traffic Rates During an attack or disaster, sensors will generate a lot of data and then transmit to sinks. Various traffic rates will be generated to measure the robustness of each protocol. 6.2.5 Energy Consumption A full trace file with energy level is provided in ns-2. All processes in a sensor need power from a tiny battery. An efficient transport protocol should optimally consume less energy as possible whilst providing reliable data transport. Energy consumption is one factor reflecting protocol efficiency in our study. Various power consumption
levels will be varied in the command script to investigate this property.
7. Related Works Several filtering criteria such as open source, ability to simulate a network with at least one thousand nodes and free of charge were realised to consider the suitable sensor network simulators in [44]. TOSSIM and SENSE were selected to conduct experiments which focus on CPU and memory uses. Number of sensors and packets sent per node were varied. A flexible toolset for studying power consumption in wireless sensor network is described in [45] using ns-2. New nodes architecture with various battery models were developed and tested for power consumption. By specifying node placement and traffic generation which are suitable for each type of application, several results can be used as an indication of code size and memory requirements for the target protocol to be implemented for each new node architecture. As a result, low cost design can then be achieved.
8. Conclusion Recent advanced hardware technologies result in more powerful sensors as small as a few millimetres volume. The main drawback is still energy constraints. More protocols and various relevant technologies have been developed to suit specific types of application and optimally meet energy requirements. In some applications, reliable data transport with congestion control is needed to meet the required system performance. Packet loss would bring about useless energy consumption. It is unlikely to be cost-effective to initially study and evaluate effects of the new research work by setting up a real deployment or small-scale testbed in a lab. Hence, simulation seems to be an interesting way to study network behaviours. There are several efforts to simulate wireless sensor networks, either by extending widely used tools such as ns-2 or developing a new one. Conducting a deploying experiment to validate any results from a simulation could identify differences between a theoretical and a real result. A simulator could then be improved to include more realistic networking and environmental characteristics. Various simulating requirements, both general and specific have been provided to study and evaluate the performance of a new reliable protocol. We argue that hardware resource consumption such as CPU and memory usages are considered as only one of the key features to present the most suitable simulators for wireless sensor networks. Other capabilities such as including a wide range of routing and MAC
protocols, providing energy and power models, and emulation should also be considered. After determining several capabilities of each simulator, ns-2 seems to be the most suitable tool to be used in the upcoming research. However, a real deployed experiment or small-scale testbed is an essential scheme to validate the results from the simulation.
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