How to Calculate Mean Opinion Score in NS2

To calculate the Mean Opinion Store in NS2, this is frequently used in Quality of Service (QoS) assessments for multimedia applications like voice over IP (VoIP) or video streaming. MOS offers a subjective measure of the perceived quality of the communication or service, usually rated on a scale from 1 (bad) to 5 (excellent). In NS2 (Network Simulator 2), MOS can be computed in terms of numerous objective QoS metrics includes packet loss, latency, jitter, and bandwidth.

Though NS2 doesn’t directly offer MOS calculations, you can estimate it based on metrics gathered during the simulation. Here’s how to approach the MOS calculation:

Step-by-Step Implementation:

  1. MOS Formula:

There are various techniques to compute MOS, relying on the application (VoIP, video, etc.). One frequent method for VoIP is depends on the R-factor, which considers network conditions:

MOS=1+0.035×R+7×10−6×R×(R−60)×(100−R)MOS = 1 + 0.035 \times R + 7 \times 10^{-6} \times R \times (R – 60) \times (100 – R)MOS=1+0.035×R+7×10−6×R×(R−60)×(100−R)

Where:

  • R is the R-factor, which can be derived from network metrics like delay, jitter, and packet loss.

The R-factor can be computed using the given formula for VoIP:

R=94.2−(Ploss×2.5)−(0.024×Delay)−(0.11×Delay−177.3)R = 94.2 – (P_{loss} \times 2.5) – (0.024 \times \text{Delay}) – (0.11 \times \text{Delay} – 177.3)R=94.2−(Ploss​×2.5)−(0.024×Delay)−(0.11×Delay−177.3)

Where:

  • P_loss is the packet loss percentage.
  • Delay is the end-to-end delay (in milliseconds).
  1. Set Up NS2 to Collect QoS Metrics:

First, you need to set up NS2 to aggregate relevant QoS metrics like delay, packet loss, and jitter during the simulation.

Example TCL Script to Simulate VoIP Traffic:

# Create the simulator

set ns [new Simulator]

# Define the network topology (e.g., 1000m x 1000m area)

set topo [new Topography]

$topo load_flatgrid 1000 1000

# Create two nodes

set node_(0) [$ns node]

set node_(1) [$ns node]

# Define the wireless channel

set chan_ [new Channel/WirelessChannel]

$node_(0) set channel_ $chan_

$node_(1) set channel_ $chan_

# Set up UDP agents for VoIP traffic

set udp0 [new Agent/UDP]

set sink [new Agent/Null]

$ns attach-agent $node_(0) $udp0

$ns attach-agent $node_(1) $sink

$ns connect $udp0 $sink

# Set up CBR traffic (simulating VoIP)

set cbr0 [new Application/Traffic/CBR]

$cbr0 attach-agent $udp0

$cbr0 set packetSize_ 160        ;# Typical VoIP packet size in bytes

$cbr0 set rate_ 64Kb             ;# 64 Kbps VoIP traffic

# Start and stop the traffic

$ns at 1.0 “$cbr0 start”

$ns at 10.0 “$cbr0 stop”

# Enable tracing for QoS metrics

set tracefile [open out.tr w]

$ns trace-all $tracefile

# Close the trace file at the end

proc finish {} {

global ns tracefile

$ns flush-trace

close $tracefile

exit 0

}

# End simulation at time 12 seconds

$ns at 12.0 “finish”

  1. Collecting QoS Metrics:

After the simulation, NS2 will create a trace file (out.tr) that has packet transmission and reception events. From this trace file, you can extract:

  • Packet loss: The amount of packets that were sent but not received.
  • End-to-end delay: The time it takes for a packet to dispatch from the source to the destination.
  • Jitter: The variation in packet delay over time.

You can use AWK or a script to extract these values from the trace file.

Example AWK Script to Calculate Delay and Packet Loss:

BEGIN {

sent_packets = 0;

received_packets = 0;

total_delay = 0;

}

# Track sent packets

$1 == “s” && $4 == “_0_” && $7 == “udp” {

sent_packets++;

packet_id[$10] = $2;

}

# Track received packets and calculate delay

$1 == “r” && $4 == “_1_” && $7 == “udp” {

received_packets++;

delay = $2 – packet_id[$10];

total_delay += delay;

jitter_sum += (delay – prev_delay) ^ 2;

prev_delay = delay;

}

END {

packet_loss = ((sent_packets – received_packets) / sent_packets) * 100;

avg_delay = total_delay / received_packets;

jitter = sqrt(jitter_sum / received_packets);

print “Packet Loss: ” packet_loss “%”;

print “Average Delay: ” avg_delay ” seconds”;

print “Jitter: ” jitter ” seconds”;

}

This script computes:

  • Packet Loss as a percentage.
  • Average Delay in seconds.
  • Jitter as the standard deviation of packet delay.
  1. Calculating MOS:

Using the metrics estimated from the trace file (packet loss, delay), you can now compute the R-factor and then the MOS.

Example Calculation of R-factor and MOS in Python:

# Function to calculate R-factor and MOS for VoIP

def calculate_mos(packet_loss, delay):

# Calculate R-factor based on packet loss and delay

R = 94.2 – (2.5 * packet_loss) – (0.024 * delay) – (0.11 * (delay – 177.3) * (delay > 177.3))

# Calculate MOS from R-factor

if R < 0:

mos = 1

elif R > 100:

mos = 4.5

else:

mos = 1 + 0.035 * R + 7e-6 * R * (R – 60) * (100 – R)

return mos

# Example values from simulation (in percent and milliseconds)

packet_loss = 5  # 5% packet loss

delay = 150      # 150 ms delay

mos = calculate_mos(packet_loss, delay)

print(f”MOS: {mos}”)

  1. Interpreting MOS:
  • MOS > 4.0: Excellent quality, close to toll quality for telephony.
  • MOS between 3.5 and 4.0: Good quality, acceptable for most users.
  • MOS between 3.0 and 3.5: Fair quality, noticeable degradation but still acceptable.
  • MOS < 3.0: Poor quality, noteworthy degradation in user experience.

Conclusion:

To calculate the Mean Opinion Score (MOS) in NS2:

  1. Simulate VoIP traffic or another kind of multimedia application.
  2. Accumulate QoS metrics like packet loss, delay, and jitter using NS2’s trace files.
  3. Calculate the R-factor using the QoS metrics.
  4. Calculate MOS using the formula depends on the R-factor.
  5. Analyze MOS to assess the perceived quality of service.

The above procedure will guide you through the entire information of Mean Opinion Score and how to calculate it in the ns2 with the help of the provided examples. We will offer anything regarding this computation based on the requirements.

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