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Class No 03 & 09




Review:
The validation of large-scale network models and simulation of wireless and mobile networks in
the real world is not easily or accurately repeatable, due to the complications and subtleties of
physical movement and wireless propagation, making the system highly variable and extensively
increasing the complex interactions between the system parts and the surrounding environment,
thus reducing the use of such experiments for validation.
In particular, the position and movement of nodes in the network and the position and possible
movement of other objects in the environment around the nodes themselves, such as buildings,
mountains, and trees, or vehicles, people, and rain, can significantly affect the behavior and
performance of the system being modeled. Furthermore, to accurately control an entire
experiment in the real world, all of these positions and movements would need to be controlled.
Models and real experiments, to some degree, can only be approximations as having complete
control over all of the factors is simply not fully achievable in any real system.
In this paper, few validation approaches are suggested that can be used to compare different runs
using simulation, emulation, or measurements from the real world. These approaches seem to be
appropriate at least for small- or medium-scale networks, and should be able to be applied to
large-scale networks given suitable choices by the modeler.
In simulation work, a system is created for emulating an ad hoc network on a stationary, wired
network. Network emulation allows a stationary, wired network to be made to behave in the
same way as a wireless network by altering the one network’s behavior, as seen by packets on
the network, to perform like the other’s. Ad hoc network emulation for simple wireless networks
is demonstrated by creating two alternate approaches in the trace modulation.
In the “trace emulation” approach, a trace of the desired network’s behavior is generated using
simulation, and then uses this trace to drive the standard trace modulation system in the operating
system kernel of the real machines on the real network; each packet is then delayed or dropped
under control of trace modulation. In the “direct emulation” approach instead, each packet from a
real machine is sent to a centralized machine on which a simulation of the desired network is
running, if the packet is not dropped within the simulation, it is then resent on the real network at
the appropriate time to its real destination. In either approach, the behavior of the emulated
Class No 03 & 09


network is thus correct if the underlying simulation is correct, and if no unwanted artifacts are
introduced in the emulation process.
Initial approaches to the validation of simulation work, were to check the operation of the system
according to a number of logical consistency checks. Although initial validation checks give
considerable confidence in results, they do not actually fully validate the simulator results
matching to the real world. By substantial logging information provided by ad hoc network
testbed implementation, the identical movement and communication scenarios can be simulated
that are experienced in these real implementation experiments and only then will be able to
assess the degree to which the results from the simulator match those from the real world.
One approach to comparing the results from simulations and real measurements (or emulations)
that was considered is a comparison based on the progression of some performance metric as a
function of time. For example, rather than simply comparing results at the end of the simulation
run and at the end of the measurements from the real world, it become possible to compare the
simulated and measured systems using graphs over each instant of time. Such graphs, known as
time-sequence number plots, show not only the total results, but also show the changes over
time. Similar graphs could be produced for other performance metrics of interest.
An example presented shows three time-sequence number plots for the TCP connection between
the two nodes in a 16-node ad hoc network. The top curve shows the connection’s behavior in a
completely simulated ad hoc network using simulator, while the other two curves show the same
connection’s behavior when run on an emulated ad hoc network, using the two different
techniques for emulation that was developed.
 Due to the experimental setup, the network emulation curve are shifted from simulator results,
The shape of the direct emulation curve tracks the simulation curve more tightly than the trace
emulation curve, although the absolute RMS error is significantly greater.
An alternative approach to validation could be to record a trace of all significant events (e.g., all
packets sent, received, or forwarded) during the experiment in the real network, and to create a
similar trace during a corresponding simulation run. The two trace files could then be compared.
However, such a comparison seems to be extremely difficult to perform, since each trace
represents the intertwined events caused by many different nodes and traffic streams in the
network. Each of these events possibly influences other events in the trace and may also be
independent of yet other events. Many events in the trace may even be able to be ignored, but it
seems hard to identify which without risking too much abstraction and introducing inaccuracy.
So currently research is running to find the possible methods for turning this general trace
comparison approach into a more concrete algorithm.
Hence we can say that the validation approaches suggested in this paper can be used to compare
different runs using simulation, emulation, or measurements from the real world. They seem to
be appropriate at least for small- or medium-scale networks, and should be able to be applied to
large-scale networks provided that suitable choices given by the modeler.

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Validation of Wireless and Mobile Network Models and Simulation Paper Review

  • 1. Class No 03 & 09 Review: The validation of large-scale network models and simulation of wireless and mobile networks in the real world is not easily or accurately repeatable, due to the complications and subtleties of physical movement and wireless propagation, making the system highly variable and extensively increasing the complex interactions between the system parts and the surrounding environment, thus reducing the use of such experiments for validation. In particular, the position and movement of nodes in the network and the position and possible movement of other objects in the environment around the nodes themselves, such as buildings, mountains, and trees, or vehicles, people, and rain, can significantly affect the behavior and performance of the system being modeled. Furthermore, to accurately control an entire experiment in the real world, all of these positions and movements would need to be controlled. Models and real experiments, to some degree, can only be approximations as having complete control over all of the factors is simply not fully achievable in any real system. In this paper, few validation approaches are suggested that can be used to compare different runs using simulation, emulation, or measurements from the real world. These approaches seem to be appropriate at least for small- or medium-scale networks, and should be able to be applied to large-scale networks given suitable choices by the modeler. In simulation work, a system is created for emulating an ad hoc network on a stationary, wired network. Network emulation allows a stationary, wired network to be made to behave in the same way as a wireless network by altering the one network’s behavior, as seen by packets on the network, to perform like the other’s. Ad hoc network emulation for simple wireless networks is demonstrated by creating two alternate approaches in the trace modulation. In the “trace emulation” approach, a trace of the desired network’s behavior is generated using simulation, and then uses this trace to drive the standard trace modulation system in the operating system kernel of the real machines on the real network; each packet is then delayed or dropped under control of trace modulation. In the “direct emulation” approach instead, each packet from a real machine is sent to a centralized machine on which a simulation of the desired network is running, if the packet is not dropped within the simulation, it is then resent on the real network at the appropriate time to its real destination. In either approach, the behavior of the emulated
  • 2. Class No 03 & 09 network is thus correct if the underlying simulation is correct, and if no unwanted artifacts are introduced in the emulation process. Initial approaches to the validation of simulation work, were to check the operation of the system according to a number of logical consistency checks. Although initial validation checks give considerable confidence in results, they do not actually fully validate the simulator results matching to the real world. By substantial logging information provided by ad hoc network testbed implementation, the identical movement and communication scenarios can be simulated that are experienced in these real implementation experiments and only then will be able to assess the degree to which the results from the simulator match those from the real world. One approach to comparing the results from simulations and real measurements (or emulations) that was considered is a comparison based on the progression of some performance metric as a function of time. For example, rather than simply comparing results at the end of the simulation run and at the end of the measurements from the real world, it become possible to compare the simulated and measured systems using graphs over each instant of time. Such graphs, known as time-sequence number plots, show not only the total results, but also show the changes over time. Similar graphs could be produced for other performance metrics of interest. An example presented shows three time-sequence number plots for the TCP connection between the two nodes in a 16-node ad hoc network. The top curve shows the connection’s behavior in a completely simulated ad hoc network using simulator, while the other two curves show the same connection’s behavior when run on an emulated ad hoc network, using the two different techniques for emulation that was developed. Due to the experimental setup, the network emulation curve are shifted from simulator results, The shape of the direct emulation curve tracks the simulation curve more tightly than the trace emulation curve, although the absolute RMS error is significantly greater. An alternative approach to validation could be to record a trace of all significant events (e.g., all packets sent, received, or forwarded) during the experiment in the real network, and to create a similar trace during a corresponding simulation run. The two trace files could then be compared. However, such a comparison seems to be extremely difficult to perform, since each trace represents the intertwined events caused by many different nodes and traffic streams in the network. Each of these events possibly influences other events in the trace and may also be independent of yet other events. Many events in the trace may even be able to be ignored, but it seems hard to identify which without risking too much abstraction and introducing inaccuracy. So currently research is running to find the possible methods for turning this general trace comparison approach into a more concrete algorithm. Hence we can say that the validation approaches suggested in this paper can be used to compare different runs using simulation, emulation, or measurements from the real world. They seem to be appropriate at least for small- or medium-scale networks, and should be able to be applied to large-scale networks provided that suitable choices given by the modeler.