How to Implement Network Emergency Data Prediction in NS2

To implement the Network Emergency Data Prediction using NS2 that has encompasses replicating a system in which an emergency data is made, predicted, and transferred in a network. This prediction is normally used in healthcare applications (such as Body Area Networks), Internet of Things (IoT) systems, or Wireless Sensor Networks (WSNs), to identify the rare or critical situations such as heart attacks, fire, or accidents. The goal is to replicate the prediction of emergency data and prioritize its transmission across the regular data. Given below is a guide on how to execute it in NS2:

Step-by-Step Implementation:

  1. Understanding Emergency Data Prediction
  • Emergency Data: It refers to crucial data generated in response to identify the emergency or an anomalous event. This related data wants to high-priority managing to make certain that instant transmission and processing.
  • Prediction Model: The system predicts while an emergency data could be generated rely on the sensor readings and other reasons. For example, a healthcare sensor may guess a heart attack depends on the abnormal heart rate readings.
  • Priority Transmission: When emergency data is identified or predicted, it is prioritized across the regular data and transmitted rapidly including minimal delay.
  1. Steps to Implement Emergency Data Prediction in NS2
  1. Define Nodes with Regular and Emergency Traffic: Make a nodes, which generate both regular data and emergency data.
  2. Implement Prediction Logic: Append the logic to replicate the prediction of emergency events depends on the data patterns.
  3. Prioritize Emergency Data: Execution a mechanism, which prioritizes emergency data across the regular data in the MAC or network layer.
  4. Simulate Traffic and Analyze Performance: Now, run the simulation and estimate how emergency data is transferred and managed liken to the usual data.

Step 1: Define Nodes and Traffic in TCL

Initially, we make a simple simulation with nodes, which generate both regular data (normal traffic) and emergency data (high-priority traffic). We will be mimicked both kinds of data traffic in the similar network.

# Create a simulator instance

set ns [new Simulator]

# Create trace and nam files for output

set tracefile [open emergency_data_prediction.tr w]

set namfile [open emergency_data_prediction.nam w]

$ns trace-all $tracefile

$ns namtrace-all $namfile

# Define network topology

set topo [new Topography]

$topo load_flatgrid 1000 1000  ;# 1000×1000 meters simulation area

# Create the General Operations Director (GOD)

set god_ [create-god 10]  ;# 10 nodes in the network

# Define wireless channel and propagation model

set chan [new Channel/WirelessChannel]

Phy/WirelessPhy set CSThresh_ 1.0e-10           ;# Default carrier sensing threshold

# Create nodes for regular and emergency communication

set node_0 [$ns node]

set node_1 [$ns node]

set node_2 [$ns node]

# Set initial positions for the nodes

$node_0 set X_ 100

$node_0 set Y_ 100

$node_1 set X_ 300

$node_1 set Y_ 300

$node_2 set X_ 500

$node_2 set Y_ 500

Step 2: Implement Emergency Data Prediction Logic

In this configuration, we replicate a situation in which emergency data is generated depends on the abnormal sensor readings (e.g., temperature exceeding a threshold, irregular heart rate). We launch a logic to make high-priority emergency packets while an anomaly is identified.

Emergency Data Prediction Function

# Function to simulate emergency data prediction based on sensor readings

proc emergency_data_prediction {node} {

set threshold 80   ;# Define an emergency threshold (e.g., temperature > 80 degrees)

set sensor_reading [expr rand() * 100]  ;# Simulate a random sensor reading between 0 and 100

if {$sensor_reading > $threshold} {

# Emergency detected, generate high-priority emergency data

puts “Emergency detected at Node [$node id]: Sensor reading: $sensor_reading”

generate_emergency_data $node

} else {

# No emergency, generate regular data

generate_regular_data $node

}

}

# Function to generate high-priority emergency data

proc generate_emergency_data {node} {

global ns

set udp_emer [new Agent/UDP]

set null_emer [new Agent/Null]

$ns attach-agent $node $udp_emer

$ns attach-agent $node $null_emer

set cbr_emer [new Application/Traffic/CBR]

$cbr_emer set packetSize_ 1024

$cbr_emer set interval_ 0.05   ;# Short interval for high-priority data

$cbr_emer attach-agent $udp_emer

$ns connect $udp_emer $null_emer

# Start the emergency data transmission immediately

$ns at 0.0 “$cbr_emer start”

}

# Function to generate regular data

proc generate_regular_data {node} {

global ns

set udp_reg [new Agent/UDP]

set null_reg [new Agent/Null]

$ns attach-agent $node $udp_reg

$ns attach-agent $node $null_reg

set cbr_reg [new Application/Traffic/CBR]

$cbr_reg set packetSize_ 512

$cbr_reg set interval_ 1.0   ;# Longer interval for regular data

$cbr_reg attach-agent $udp_reg

$ns connect $udp_reg $null_reg

# Start the regular data transmission

$ns at 1.0 “$cbr_reg start”

}

Step 3: Prioritize Emergency Data Transmission

In the simulation NS2, we can alter the MAC layer or queue discipline to highlight emergency traffic across the regular traffic. One path to prioritize emergency data is by using Priority Queuing (PQ) that emergency packets are processed before regular packets.

Define Priority Queue

# Use a Priority Queue for prioritizing emergency data

Queue/DropTail/PriQueue set limit_ 50  ;# Limit the queue size

# Attach Priority Queue to nodes handling emergency and regular traffic

$node_0 set ifq [new Queue/DropTail/PriQueue]

$node_1 set ifq [new Queue/DropTail/PriQueue]

$node_2 set ifq [new Queue/DropTail/PriQueue]

With this configuration, packets in the priority queue will manage according to their priority. Emergency packets will process initially.

Step 4: Simulate Traffic and Emergency Data

To mimic the network, periodically verify the sensor readings and also activate emergency data if the readings exceed the threshold.

# Function to simulate periodic checking of sensor data and triggering emergency data

proc periodic_sensor_check {node} {

global ns

emergency_data_prediction $node

# Check again after 1 second

$ns at [expr $ns now + 1.0] “periodic_sensor_check $node”

}

# Start the periodic sensor checking for each node

$ns at 1.0 “periodic_sensor_check $node_0”

$ns at 1.5 “periodic_sensor_check $node_1”

$ns at 2.0 “periodic_sensor_check $node_2”

Step 5: End the Simulation

At the end, describe the destination of the simulation then run the TCL script.

# End simulation after 100 seconds

$ns at 100.0 “finish”

# End simulation procedure

proc finish {} {

global ns tracefile namfile

close $tracefile

close $namfile

$ns halt

}

# Run the simulation

$ns run

  1. Explanation of the Script
  • Emergency Data Prediction: We replicate the sensor readings and verify if they exceed a predefined threshold (e.g., temperature > 80°C). If the threshold is exceeded, then the emergency data is generated, and these broadcast is prioritized.
  • Priority Queuing: Emergency data is transferred including higher priority using a priority queue. This make sure that emergency packets are transferred before regular packets, minimising latency for difficult data.
  • Regular Data: Regular traffic is made at longer intervals to mimic non-urgent data transmissions.
  1. Running the Simulation
  1. We can save the TCL script as emergency_data_prediction.tcl.
  2. Run the simulation using the below command:

ns emergency_data_prediction.tcl

  1. Analyzing Results
  • We calculate the trace file (emergency_data_prediction.tr) and the NAM file (emergency_data_prediction.nam), after running the simulation that to assess the behaviour of the emergency data handling.
  • Metrics to observe:
    • Packet Delivery Time: Estimate how rapidly emergency packets are delivered liken to regular packets.
    • Packet Drop Rate: Verify if any emergency packets are dropped because of the congestion or other network issues.
    • Latency: Assess the delay among the generating emergency data and these delivery to the end.
  1. Further Enhancements
  • Dynamic Priority Adjustments: Execute the dynamic priority levels that the priority of packets can alter rely on the severity of the emergency.
  • Prediction Algorithms: Integrate more sophisticated emergency prediction algorithms according to the machine learning or statistical models to predict emergencies before they occur.
  • Energy-Efficient Routing: Append the energy-efficient routing protocols to make certain that nodes are transmitting emergency data conserve energy when maintaining reliability.
  • Real-Time Analytics: Expand the simulation to contain the real-time analytics and decision-making depends on the emergency data.

As illustrated, the execution approach with basic examples that helps you to acquire and implement the Network Emergency Data Prediction using the virtual environment NS2. Also, we offer more detailed data like this in various materials rely on your needs.

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