How to Calculate Network Accuracy in NS2

To calculate the Network Accuracy in Network Simulator 2 (NS2), this is not a specified metric like delay, throughput or packet loss. Yet, relying on the simulation’s purpose, network precision can be agreed as an estimate of how accurately the network sends data or implement a task compared to predict or ideal outputs. It is often applied in machine learning, data classification or wireless sensor networks (WSNs) for localization, data fusion or decision-making.

For common purposes, you might compute network accuracy in NS2 by comparing the original outcome (like successful delivery, correct classification, or sensor data precision) with the predicted outcome. Here are a few certain contexts where network accuracy might apply:

  1. Packet Delivery Accuracy:

Packet delivery precision refers to the proportion of properly sends packets versus the total amount of packets sent. This is closely related to the Packet Delivery Ratio (PDR).

Formula for Accuracy in Packet Delivery:

Accuracy=Number of Correctly Delivered PacketsTotal Sent Packets×100\text{Accuracy} = \frac{\text{Number of Correctly Delivered Packets}}{\text{Total Sent Packets}} \times 100Accuracy=Total Sent PacketsNumber of Correctly Delivered Packets​×100

You can measure this by summing up the amount of packets successfully obained at the destination and comparing it to the number of packets sent.

  1. Data Accuracy in Wireless Sensor Networks (WSNs):

In WSNs, precision may refer to how accurately sensor data is transferred and acquired without being corrupted. You could assess precision by comparing the received sensor data to the actual sensor readings.

Formula for Accuracy in Data Transmission:

Accuracy=Correctly Received Data ValuesTotal Sent Data Values×100\text{Accuracy} = \frac{\text{Correctly Received Data Values}}{\text{Total Sent Data Values}} \times 100Accuracy=Total Sent Data ValuesCorrectly Received Data Values​×100

  1. Classification or Decision-Making Accuracy:

If your network involves some form of classification or decision-making like routing decisions, intrusion detection, or event detection), precision refers to how frequently the network made the appropriate decisions.

Formula for Classification Accuracy:

Accuracy=Number of Correct DecisionsTotal Number of Decisions×100\text{Accuracy} = \frac{\text{Number of Correct Decisions}}{\text{Total Number of Decisions}} \times 100Accuracy=Total Number of DecisionsNumber of Correct Decisions​×100

Here are the detailed steps to estimate the network accuracy in ns2:

Steps to Calculate Network Accuracy in NS2:

  1. Set Up a Simulation in NS2:

Begin by designing a network simulation that produces traffic or execute a task where precision can be computed. For instance, a simulation where packets are sent from a source to a destination, and you want to measure how many packets are successfully received.

Example TCL Script for Data Transmission:

# Create the simulator

set ns [new Simulator]

# Create two nodes: source and destination

set node_(0) [$ns node]

set node_(1) [$ns node]

# Create a duplex link between the nodes (1 Mbps bandwidth, 10ms delay)

$ns duplex-link $node_(0) $node_(1) 1Mb 10ms DropTail

# Attach UDP agents to nodes for sending and receiving data

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 on the UDP agent

set cbr0 [new Application/Traffic/CBR]

$cbr0 attach-agent $udp0

$cbr0 set packetSize_ 512        ;# Packet size in bytes

$cbr0 set rate_ 1Mb              ;# Transmission rate

# Start and stop the traffic

$ns at 1.0 “$cbr0 start”

$ns at 10.0 “$cbr0 stop”

# Enable tracing to capture packet transmission and reception

set tracefile [open out.tr w]

$ns trace-all $tracefile

# End simulation at time 12 seconds

proc finish {} {

global ns tracefile

$ns flush-trace

close $tracefile

exit 0

}

# End simulation at 12 seconds

$ns at 12.0 “finish”

  1. Extracting the Trace File:

Once the simulation is done, the trace file (out.tr) will log all the packet transmission and reception events. You can assess this file to estimate network accuracy.

Example Trace File Entries:

s 2.000000000 _0_ AGT  — 512 cbr 0 0.0 1.0 1.0

r 2.010000000 _1_ AGT  — 512 cbr 0 0.0 1.0 1.0

s 3.000000000 _0_ AGT  — 512 cbr 1 0.0 2.0 2.0

r 3.010000000 _1_ AGT  — 512 cbr 1 0.0 2.0 2.0

  • s: Packet sent.
  • r: Packet received.
  • _0_ and _1_: Node IDs (source _0_ and destination _1_).
  • 512: Packet size in bytes.
  1. Calculating Packet Delivery Accuracy:

Measure the packet delivery accuracy by comparing the amount of effectively obtained packets to the amount of delivered packets.

AWK Script to Calculate Packet Delivery Accuracy:

BEGIN {

sent_packets = 0;

received_packets = 0;

}

# Count sent packets from source node (_0_)

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

sent_packets++;

}

# Count received packets at destination node (_1_)

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

received_packets++;

}

END {

accuracy = (received_packets / sent_packets) * 100;

print “Network Accuracy (Packet Delivery): ” accuracy “%”;

}

  • The script sums the number of packets sent by node 0 and the count of packets successfully received by node 1.
  • It then calculates the precision as the ratio of received packets to sent packets.
  1. Running the AWK Script:

Execute the AWK script to calculate the network accuracy, on the trace file:

awk -f accuracy.awk out.tr

  1. Calculating Data Accuracy (e.g., in WSNs):

In a Wireless Sensor Network (WSN) scenario, if the transferred data indicates sensor readings or measurements, you could relate the received sensor values with the actual values to measure data precision.

AWK Script to Calculate Data Accuracy:

If sensor data values are exchanged in the packets, you can compare these values upon reception to the actual sensor values to state if the data was precisely received.

BEGIN {

correct_data = 0;

total_data = 0;

}

# Simulating correct and received data comparison (e.g., actual_value == received_value)

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

actual_value = 100;  # Assume this is the expected value

received_value = $8;  # Get the received data value from the trace

if (received_value == actual_value) {

correct_data++;

}

total_data++;

}

END {

accuracy = (correct_data / total_data) * 100;

print “Data Transmission Accuracy: ” accuracy “%”;

}

  1. Calculating Classification/Decision Accuracy:

For networks involving classification or decision-making (like intrusion detection, routing, or event detection), you would relate the amount of correct decisions (like proper classification of an intrusion) to the total amount of decisions made.

Conclusion:

To calculate network accuracy in NS2:

  1. Set up the simulation and create a trace file that logs network events.
  2. Analyze the trace file using AWK or other tools to compare the original outcome (for instance: obtained packets, received sensor data, or appropriate classifications) with the expected outcome.
  3. Calculate accuracy by breaking down the correct outcomes by the total outcomes and multiplying by 100 to get a percentage.

By following this steps, we can grasped the idea behind the calculation of network accuracy in the ns2 simulation and how to estimate it by utilizing the formulas and following the offered some samples. We will deliver any other details of this computation steps, if needed. To determine the accuracy of your network in NS2, please share your parameter details with us. We’ll help you improve your project’s performance and provide you with the best project ideas from our experts.