to a certain level and not to consider the physical and hardware levels of network nodes, which will reduce the time spent on the study of network parameters. The architecture and graphical interface of the developed real-time simulator "SNOW" for research of models and methods of local networks wireless access construction are considered in the work. The performance indicators of the simulator for modeling the topology control, construction of the communication graph, its visualization and determination of the power consumption parameters of the K-NEIGH type sensor network for variants with sequential and parallel execution of simulation steps are given.
• creation and subsequent simulation of a model containing stochastic elements. It is assumed to use random variables to model the consequences of the negative phenomenon, rather than the negative phenomenon itself (path losses, packet delays, etc).
The first option allows you to simulate the behavior of each of the network nodes in detail over time, as well as to simulate the movement of packets in detail and their routing.
The second option is optimal for obtaining some general characteristics of the network, such as the
connectivity of the communication graph, the average number of neighbors for each node. This
approach is often used to model topology control methods. To determine the general characteristics of
a network consisting of a large number of nodes (typical for wireless sensor networks), the second
method of modeling is used. Although both the first and the second methods will give the same result
for a large number of elements. The fact is that while using the full model, the modeling time
increases exponentially with increasing number of elements. It is hundreds of times higher than the
time cost of statistical modeling, which has little dependence on changes in the number of network
elements by a thousand elements or more [
In order to select tools for modeling methods for constructing sensor networks, an assessment of existing software products was conducted. Figure 1 shows a diagram that allows to compare the means for simulation on the implemented level abstraction (y-axis) and the maximum possible size of the simulated network (x-axis).
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SNOW
Atarraya
Shawn
GloMoSim
OmNeT++ SENSE
NS-2 TOSSIM Кіnлuькmісbтeьr oвfузnлoіdвes 103 104 105 106
In [
The NS development began in 1989 is constantly being improved. Purpose of NS is education and research in network technologies.
The simulator NS-2 has a basic model that implements the IEEE 802.15.4 standard. For ad-hoc hemming in NS-2, routing protocols AODV, DSDV, DSR and TORA are adopted. They provide an additional support for securing the flexibility of robots with mobile universities. At the same time, only the routing protocols can be used in the NS-2, as it is not up to the point to break the special features of mouthless sensor fences (Figure 2).
Simulation programma: • Otcl (NS-2) script • Python or C++ (NS-3) script
NS Octl (NS-2) interpretator Library C++: • Event scheduling
objects • Network component
objects • Additional objects
Simulation results: • NAM (NS-2)
visualization • Pcap (NS-3) trace files
The NS-3 simulator, which is described in [10-13] is much better for stimulating wireless sensor
networks. NS-3 (version 3) is a completely excellent simulator based on NS-2 [
NS uses two programming languages because it has two types of operations to perform. On the one hand, detailed data exchange protocol simulation requires a programming language that can efficiently manipulate bytes, packet headers, and at the same time must enable the implementation of algorithms that work with large data sets. This task requires a high execution speed. Ensuring low time costs of the development cycle (running the simulation, finding an error in the code, error correction, recompilation, re-simulation) are less important.
On the other hand, a large amount of research in the field of network technology requires minor changes in parameters, changes in network configuration or a quick review of possible scenarios. In this case, the above-mentioned iterative development process is more important, and the speed of execution does not play a role. Note that in case of both static and mobile sensor network nodes, it is necessary to investigate not only the network configuration, but also the ways of data transmission, which is quite difficult in this simulator due to the limited graphical interface.
Shawn simulator described in [14] is a program-simulator of discrete events for large wireless sensor networks modeling algorithms. Shawn does not provide the same level of modeling detail as, for example, NS-2, but with the correct construction of the model it gives a convergent result. The Shawn simulator has a very wide range of possibilities for statistical modeling, however does not allow modeling of a phenomenon, but simulates the impact of such. For example, you can simulate the interference of individual packets using a signal propagation model and abstractly set the channel losses proportional to the number of nodes in the transmitter area.
Also, Shawn simulator requires writing your own processors for wireless network nodes, different models for messaging, and more. The modular architecture of the simulator allows additions that are standard for simulators of such types. An alternative approach to the simulation process itself provides high performance.
TOSSIM (TinyOS) – a system specifically designed for sensor networks [14, 15]. It has a software model component described in nesC. TinyOS is not an operating system in the traditional sense. It is a software environment for embedded systems and has a set of components that allow you to create simulation models for a specific application, such as TOSSIM. The TOSSIM simulator can simulate networks of up to several thousand nodes, and by analyzing them, predict the behavior of the network with high accuracy. By modeling networks with possible interferences and errors, the simulator creates a simple but at the same time effective model of various interactions of nodes in the network. Describing a low-power model of TinyOS devices, it simulates the behavior of the sensor node with a high probability, describing its characteristics and conducting a large number of experiments. For the convenience of developers, TOSSIM supports a graphical user interface, providing detailed visualization and reproduction of the actions of the running simulation model, but does not reproduce the communication graph tied to the environment.
There are also other publicly available network simulators, such as JavaSim, SSFNet, Glomosim and Qualnet, in which the developers tried to solve the shortcomings of these systems. JavaSim developers realized the disadvantage of using object-oriented system design and tried to build a component-oriented architecture. However, the effectiveness of the simulation was limited by the choice of the Java simulation language [15, 17].
The SENSE simulator is designed as an efficient and powerful sensor network simulator [15, 16]. It uses a component port model, which frees simulations from the interdependence that is common in object-oriented architecture. The component port model makes simulation models extensible – a new component can replace an old one if they have compatible interfaces, and advanced users have the ability to develop new simulation mechanisms. Removing the interdependence between models also promotes reusability. A component designed for one simulation can be used in another if it meets the requirements of the latter in terms of interface and semantics. In SENSE, there is a level of reusability that has been made possible by the widespread use of the C ++ template: a component is usually declared as a template class so that it can process other types of data. However, SENSE can only use the parallel simulation mechanism for compatible components. Therefore, only in the case of sequential simulation can each component in the model repository be reused. 3. Architecture and of the Simulator of the Personal Local Wireless Networks
The analysis of existing software products for simulation of construction methods and control of sensor topology and actuator networks of wireless access allowed to reveal advantages, lacks and means of improvement. This led to the development of the "SNOW" simulator (Sensor Network Over Wireless). The following requirements have been identified as the main ones that will provide the necessary environment for conducting experiments on the construction and study of sensor networks: • support of inhomogeneous network structure; • the possibility of independent description and simultaneous use in experiments of different components of the network model; • the maximum approximation of the node behavior description and the protocol to the description of them in the node software; • the ability to describe arbitrary methods of construction and data exchange protocols in a wireless network; • simplicity of describing the behavior of network nodes; • ability to expand the simulator by adding new models; • the ability to change the level of detail for each of the models; • the ability to remove arbitrary characteristics of the network or individual nodes in real time; • powerful tools for visualization of results: communication graphs, graphs; • the ability to save the initial parameters and results of experiments; • the ability to conduct a series of experiments with different settings; • no restrictions on the size of the studied network; • high speed.
The "SNOW" program is a simulator of discrete events in time, designed to study: • network formation and reconfiguration processes; • topology control; • routing methods in the IS; • distributed algorithms for the operation of nodes and wireless communication protocols related to the channel, network, transport and session layers of OSI; • algorithms and methods of building IP, as part of a local area network with wireless access. The simulator program provides: • graphical shell to adjust the parameters of the experiment; • save configuration files and host locations to play the experiment; • experiment results display in text and graphical formats (graphs of characteristics in real time, map of nodes, coverage areas, communication graph).
The simulator's ability to scale experiments is limited only by the hardware characteristics of the PC on which the simulation is performed and the simulation time itself. As for the functional extension, thanks to the modular architecture it is possible to add any new model for a particular component. 3.1
In the article [18] a general description of the simulator architecture is given. There is briefly described and explained the relationships between the components of the network model that are implemented in the developed simulator and the functionality of the simulator kernel.
[18] provides a general description of the simulator architecture, briefly describes the relationships between the components of the network model implemented in the developed simulator, explains the functionality of the simulator core. This article describes the structure of classes in the simulator in more detail and explains the chosen decomposition. In the diagrams mentioned in Figure3 and Figure 4 the main program classes and subclasses of the developed simulator are shown in accordance with: 1. implementation of different types of IP devices for the wireless sensor networks ("Specification of Control Types") 2. implementation of their behavior, ie the exchange of messages in the wireless sensor networks ("Specification of Stages of Work").
The architecture constructed in this way allows to obtain the necessary flexibility in the description of all components of the model. It is also possible to study arbitrary methods of topology control for the network (see “Model of Construction Method” in Figure 4) with simultaneous support of inhomogeneous devices (see “Model of Network Node” in Figure 3).
Specification of control element types (1.4.1) 1. Simple 2. Complex 3. Super
Network node model (1) 1. Radio device 2. Network device 3. Power model 4. Control element Radio device specification (1.1) 1. Send a message environtment 2. Process the message environtment 3. Transmitter power 4. Massage buffer 5. Current and next reciver states Network device specification (1.2) 1. Identifier 2. Properties (2.4) 3. Stages of work (2.5) Power model (1.3) 1. Battery charge 2. Costs
Control model (1.4) 1. Type of control 2. Current and next state Transmitter power specification (1.1.3) 1. Current 2. Minimal 3. Maximum Specification of the states of the control element (1.4.2) 1. Not rannsng 2. Sleeping 3. Working 4. Stopped
“Network Node Model”, in particular, can be described arbitrary un combinations of the following components: • Radio Device – sending and receiving data from the air, allows to adjust the power of the transmitter and the sensitivity of the receiver and to control its activation; • Network Device – network identification; storing, updating and accumulating information about neighbors, routes, etc; • Power model – control of battery charge, data transmission and reception costs; • • • • • properties and allows to make changes to existing ones [19, 20].
For the “Topology Control Model” we can describe arbitrary: types and structure of messages; protocol states and functionality of each of the states; incoming message handlers; communication radius assignment function.
Control Element – implements the machine states of the node operation and the transition to them in accordance with the current method of the nod work; the basic set includes four states of operation of the node; Each of the components described above can be supplemented with new Specification of work stages (2.5) 1. Initialization 2. Functioning 3. State switching
Model of construction method (2) 1. Message types 2. Message structure 3. Protocol conditions 4. Properties 5. Stage of work 6. Incoming message processing 7. Communication radius assignment function Specification of protocol stages (2.1)
Specification of protocol states (2.3) 1. Internal condition 2. Simulator protocol status 1. Internal condition 2. Simulator protocol status Specification of the message structure (2.2) 1. Sender 2. Transmitted power 3. Received power 4. Simulator package type 5. (Internal variables)
Specification of the construction method properties (2.4) 1. Message counters (by simulator message types) 2. List of neighbors 3. List of routes 4. Current and next internal protocol states show the graphics subsystem.
{} SIM4 {} SIM4.NetworkComponents Network
{} SIM4.NetworkComponents.Nodes Environment
{} SIM4.NetworkComponents.Interfases {} SIM4.NetworkComponents.TopologyControls part of the simulator.
The Table 1 shows an example of setting parameters for sensor networks simulation. The simulator interface consists of the following windows: 1. Main window (Figure 9) 2. The window for generating the number and type of nodes (Figure 10) 3. Node settings change window (Figure 11) 4. Communication graph view window (Figure 12)
coverage area parameters for the node; type of start of knots; retransmission parameters; change the distribution of nodes by type; choice of construction method.
The main results of the development and application of the SNOW simulator will be demonstrated by examples of the study of real sensor networks.
Simulation time for the K-NEIGH network topology method of construction and control Table 2 shows the duration of 100 simulation steps for experiments with different numbers of nodes. The size of the side of the nodes square area was changed to maintain the same density of the location. The side of the square region is calculated by the formula: r = | N | q (1) where N is the set of nodes, r is the side of the nodes square area, q is the density of the nodes; the density is equal to 0.01; r is rounded to the nearest larger number that is a multiple of 10.
We believe that devices in the network can be of two types: "Simple Node" (limited autonomous power supply) and "Super Node" (unlimited power).
Instead of assigning the same communication radius to all nodes, we use the function of assigning the communication radius: a gradual increase in the communication radius from the minimum value until we achieve the desired connectivity of the node. For the first method of construction, it is physical, and for the second – logical connectivity of knot.
A number of experiments were performed for networks of different sizes, which aimed to determine the simulator performance on the example of the K-NEIGH topology control method.
The K-NEIGH (K-Neighbors) method involves building a network based on a certain minimum required number for each node neighbors, which ensures the connectivity of the communication graph.
Experiments were performed with off (sequential simulation), partially on and full on parallelization of processes in the simulator core. Table 2 shows the obtained data.
Sequential simulation here means the use of sequential cycles when performing both state machines of all nodes and when processing (redirecting) messages by the environment. With a partial parallelization, message processing is carried out by the medium and the parallel cycle.
Improving the speed of the simulator – work in parallel mode
One way to increase the productivity is more sparse performance. In the conducted experiments, the removal of all characteristics occurred every 10 steps of the simulation with a total number of steps equal to 200. More frequent removal of characteristics provides more accurate intermediate results and, accordingly, smoothed graphs of the obtained characteristics over time.
Also note, that the speed experiments were performed in the program debugging mode; eliminating the collection of debugging information saves up to 30% of the time. To test this assumption, experiments were repeated for the cases listed in Table 2 color. It was confirmed that when you run the program in normal operation, the gain ranges from 26.6% to 33.8%.
Time costs for experiments in different modes are shown in Figure14.
Scaling capabilities and graphical interface of simulation results
As you can see from the Figure14, the use of parallel calculations in the simulator allows you to significantly reduce the time spent on the simulation, while achieving the same results.
This effect increases as the size of the network increases. N the Fig. 15, there is given an example of a communication graph for a 3000 elements network by the K-NEIGH construction method, which is obtained by means of the "SNOW" simulator.
The wireless network simulator building method adapted for the research on the process of building, forming and reconfiguring the network, topology control, routing methods and maintaining wireless network connectivity, is considered. The simulator allows to explore the distributed algorithms of the node operation and wireless sensor network protocols related to the channel, network, transport and session levels of OSI.
The simulator implements all the requirements (which are determined from the analysis of the advantages and disadvantages of existing simulators and described in section 3), which provides the necessary environment for experiments to build and study sensor networks.
The structure of the simulator program in which the network model is implemented is described in details. Components, classes, interface, environment model and simulator implementation are described. Thanks to the modular architecture, it is possible to use any new model for a particular component. There is a graphical shell for the study parameters adjustment and text displaying in the textual and graphical form (graphs of characteristics in real time, location map of nodes, coverage areas, communication graph).
Examples of work with the simulator are given. One of the simplest ways to build a wireless network is taken as an example. Methods of optimization of the described method of network construction are proposed.
The authors are appreciative to colleagues for their support and appropriate suggestions, which allowed them to improve the materials of the article.
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