How to Implement Network Prediction Sensor Location in NS2

To implement Network Prediction of Sensor Location in NS2, we need to replicate a network in which sensor nodes move or interact in an environment, and their locations are predicted or tracked according to certain conditions like their past movement, signal strength, or other environmental factors.

Even though NS2 doesn’t directly support advanced prediction models, we can emulate a simplified version by using mobility models like random waypoint or setdest for the sensors, and predict their forthcoming locations based on their current and past positions. The prediction could be completed by mathematical models or simple linear prediction according to the node movement.

The below is a brief approach on how to implement the Prediction of Sensor Location in ns2:

Steps to Implement Sensor Location Prediction in NS2:

  1. Set up the Network Topology: Generate sensor nodes and describe their mobility.
  2. Define Sensor Movement: Use the setdest command or a mobility model to describe sensor node movements.
  3. Simulate Location Prediction: Apply a location prediction model to calculate the future location of the sensors.
  4. Monitor and Predict Locations: Track the sensor nodes and implement prediction logic to calculate their future locations.

Step-by-Step Implementation:

  1. Set up the Network Topology

Initially, describe the sensor nodes in the network. These nodes denote the sensors whose locations will be predicted according to their movement.

Example TCL Script for Basic Setup:

# Create a simulator instance

set ns [new Simulator]

# Define link parameters (for communication between sensor nodes)

set bw 1Mb

set delay 10ms

# Create sensor nodes

set sensor1 [$ns node]   ;# Sensor Node 1

set sensor2 [$ns node]   ;# Sensor Node 2

set sensor3 [$ns node]   ;# Sensor Node 3

# Set up communication links between the sensor nodes (optional)

$ns duplex-link $sensor1 $sensor2 $bw $delay DropTail

$ns duplex-link $sensor2 $sensor3 $bw $delay DropTail

In this setup:

  • sensor1, sensor2, and sensor3 denotes sensor nodes in the network.
  • These nodes can interacted through the defined links, via communication isn’t necessary for location prediction.
  1. Define Sensor Movement

Use the setdest command or other mobility models to describe the movement of the sensor nodes in the network. This movement will serve as the foundation for predicting future sensor locations.

Example of Defining Movement for Sensor Nodes:

# Define the initial positions for the sensor nodes

$sensor1 set X_ 0

$sensor1 set Y_ 0

$sensor2 set X_ 100

$sensor2 set Y_ 100

$sensor3 set X_ 200

$sensor3 set Y_ 200

# Simulate the movement of the sensor nodes

$ns at 1.0 “$sensor1 setdest 150 150 10”  ;# Sensor 1 moves to (150, 150) at 10 m/s

$ns at 1.5 “$sensor2 setdest 200 200 8”   ;# Sensor 2 moves to (200, 200) at 8 m/s

$ns at 2.0 “$sensor3 setdest 250 250 6”   ;# Sensor 3 moves to (250, 250) at 6 m/s

Here:

  • sensor1, sensor2, and sensor3 move to new locations over time.
  • These movements serve as the input for predicting the sensor locations.
  1. Simulate Location Prediction

To predict the location of a sensor node, we can implement a basic prediction model. For sample, if a sensor moves at a constant speed along a straight line, its future position can be calculated according to its current position and velocity.

Example: Simple Linear Prediction Model

Let’s execute a basic linear prediction model, in which the future position is calculated using the formula: Xpred=Xcurrent+vx×ΔtX_{\text{pred}} = X_{\text{current}} + v_x \times \Delta tXpred​=Xcurrent​+vx​×Δt Ypred=Ycurrent+vy×ΔtY_{\text{pred}} = Y_{\text{current}} + v_y \times \Delta tYpred​=Ycurrent​+vy​×Δt Where:

  • Xcurrent,YcurrentX_{\text{current}}, Y_{\text{current}}Xcurrent​,Ycurrent​ are the current coordinates.
  • vx,vyv_x, v_yvx​,vy​ are the velocity components in the xxx and yyy directions.
  • Δt\Delta tΔt is the time into the future we want to predict.

Example of Implementing Location Prediction:

# Procedure to predict the future location of a sensor node

proc predict_location {sensor_name time_interval} {

# Get the current position and velocity of the sensor node

set current_x [$sensor_name set X_]

set current_y [$sensor_name set Y_]

set vx 10    ;# Velocity in the x-direction (for simplicity)

set vy 10    ;# Velocity in the y-direction (for simplicity)

# Calculate the predicted future position

set predicted_x [expr $current_x + $vx * $time_interval]

set predicted_y [expr $current_y + $vy * $time_interval]

# Print the predicted location

puts “Predicted location of $sensor_name after $time_interval seconds: X = $predicted_x, Y = $predicted_y”

}

# Predict the future location of sensor1 after 5 seconds

$ns at 6.0 “predict_location sensor1 5”

In this example:

  • The current location of sensor1 is retrieved.
  • The velocity in the x and y directions is assumed to be constant (for simplicity).
  • The future location is predicted after a 5-second interval and displayed.
  1. Monitor and Predict Locations in Real-Time

To observe the real-time locations of sensors and implement the prediction logic dynamically, we can generate a loop that continuously predicts the sensor locations according to their current positions.

Example of Real-Time Location Monitoring and Prediction:

# Procedure to monitor and predict the location of a sensor node

proc monitor_and_predict {sensor_name interval} {

# Get the current time

set time [$ns now]

# Predict the future location of the sensor

predict_location $sensor_name 5  ;# Predict the location 5 seconds into the future

# Schedule the next prediction

set next_time [expr $time + $interval]

$ns at $next_time “monitor_and_predict $sensor_name $interval”

}

# Start monitoring and predicting the location of sensor1 every 1 second

$ns at 1.0 “monitor_and_predict sensor1 1”

In this example:

  • The monitor_and_predict procedure endlessly monitors the location of sensor1 every 1 second and predicts its location 5 seconds into the future.
  • This mimics the real-time tracking and prediction of sensor locations.
  1. Enable Tracing and Monitor Results

To observe the movement of the sensor nodes and the prediction outcomes, we can permits tracing in NS2.

Enable Trace Files:

# Enable tracing for the simulation

set tracefile [open “sensor_location_prediction_trace.tr” w]

$ns trace-all $tracefile

# Define a finish procedure to end the simulation and close the trace file

proc finish {} {

global ns tracefile

$ns flush-trace

close $tracefile

exit 0

}

# Set the simulation end time

$ns at 20.0 “finish”

The trace file will capture the movement of the sensor nodes, and we can also log the predicted locations to measure the precision of the prediction model.

  1. Run the Simulation

Execute the simulation to monitor the real-time movement of the sensor nodes and how their locations are predicted.

# Run the simulation

$ns run

Example Complete TCL Script for Sensor Location Prediction Simulation

# Create a simulator instance

set ns [new Simulator]

# Define link parameters (for communication between sensor nodes)

set bw 1Mb

set delay 10ms

# Create sensor nodes

set sensor1 [$ns node]

set sensor2 [$ns node]

set sensor3 [$ns node]

# Define the initial positions for the sensor nodes

$sensor1 set X_ 0

$sensor1 set Y_ 0

$sensor2 set X_ 100

$sensor2 set Y_ 100

$sensor3 set X_ 200

$sensor3 set Y_ 200

# Simulate the movement of the sensor nodes

$ns at 1.0 “$sensor1 setdest 150 150 10”

$ns at 1.5 “$sensor2 setdest 200 200 8”

$ns at 2.0 “$sensor3 setdest 250 250 6”

# Procedure to predict the future location of a sensor node

proc predict_location {sensor_name time_interval} {

set current_x [$sensor_name set X_]

set current_y [$sensor_name set Y_]

set vx 10    ;# Velocity in the x-direction

set vy 10    ;# Velocity in the y-direction

set predicted_x [expr $current_x + $vx * $time_interval]

set predicted_y [expr $current_y + $vy * $time_interval]

puts “Predicted location of $sensor_name after $time_interval seconds: X = $predicted_x, Y = $predicted_y”

}

# Procedure to monitor and predict the location of a sensor node

proc monitor_and_predict {sensor_name interval} {

set time [$ns now]

predict_location $sensor_name 5

set next_time [expr $time + $interval]

$ns at $next_time “monitor_and_predict $sensor_name $interval”

}

# Start monitoring and predicting the location of sensor1 every 1 second

$ns at 1.0 “monitor_and_predict sensor1 1”

# Enable tracing

set tracefile [open “sensor_location_prediction_trace.tr” w]

$ns trace-all $tracefile

# End simulation

$ns at 20.0 “finish”

proc finish {} {

global ns tracefile

$ns flush-trace

close $tracefile

exit 0

}

# Run the simulation

$ns run

We had understand the concepts about the implementation process of network prediction sensor location in ns2 that has contain to configure the network topology then apply the simple prediction design to predicts the sensor location and then permits them to monitor and analyse the outcomes. If you need more information regarding this process we will deliver it. To achieve optimal results in Network Prediction Sensor Location within the NS2 tool implementation, you may consider reaching out to the ns2project.com team. We provide assistance with high-quality thesis ideas and topics.