How to Implement Network Path Prediction in NS2

To implement the network path prediction using the simulation tool NS2 (Network Simulator 2) that encompasses designing the mechanisms to expect the future paths or routes, which data packets will acquire via the network depending on present and past network conditions. Network Path prediction can be useful in the dynamic networks, such as Mobile Ad-Hoc Networks (MANETs) or Vehicular Ad-Hoc Networks (VANETs) and mobile that nodes are often alter their positions, and the network topology constantly shifts.

Give below is a step-by-step approaches on how to execute the network path prediction in NS2:

Step-by-Step Implementation:

  1. Understand the Path Prediction Concept

Path prediction contains predicting the future state of the network and preemptively modifying routing decisions depends on these predictions. It can be particularly helpful in dynamic networks in which node mobility or network traffic changes often.

Some general use cases for network path prediction are:

  • Mobility Prediction: Forecasting the movement of mobile nodes and choosing routes, which remain stable longer.
  • Traffic Prediction: Expecting alters in the traffic conditions (e.g., congestion) and proactively modifying the route to prevent delays.
  1. Select or Modify a Routing Protocol

To execute the path prediction, we will required to change or expand an existing routing protocol such as AODV, DSR, or any other protocol within NS2. The idea is to incorporate the path prediction mechanisms in the route discovery or route maintenance process.

For instance, in AODV that path prediction can be appended while the Route Request (RREQ) or Route Reply (RREP) procedure to expect future changes in the route.

  1. Integrate Prediction Logic in the Routing Protocol

We can create the prediction model on:

  • Mobility patterns: We can use the past and current mobility information (e.g., speed, direction) to forecast the future position of nodes.
  • Traffic conditions: Observe the queue lengths, packet loss, or congestion levels to forecast future traffic on links.
  • Historical data: We can be used historical data or machine learning algorithms to predict impending route stability.

Given below is an sample of how to incorporate mobility-based path prediction into AODV:

Modify the recvRequest Function in AODV (C++):

void AODV::recvRequest(Packet *p) {

struct hdr_ip *ih = HDR_IP(p);

struct hdr_aodv_request *rq = HDR_AODV_REQUEST(p);

// Calculate the future position of the node based on mobility

Position predictedPosition = predictNodePosition(ih->saddr());

// Check if the predicted position is favorable for maintaining the route

if (isPredictedPathStable(predictedPosition)) {

// Process the route request as usual

forwardRequest(p);

} else {

// Ignore or drop the route request due to predicted instability

Packet::free(p);

}

}

Add Path Prediction Logic:

In the sample above, predictNodePosition() and isPredictedPathStable() are custom functions, which we will require to execute and this functions will forecast the future position of the node according to the mobility models and verify if the predicted path is stable.

Example of Mobility Prediction Function:

Position AODV::predictNodePosition(nsaddr_t nodeAddr) {

// Get the current mobility information of the node

double speed = getNodeSpeed(nodeAddr);

double direction = getNodeDirection(nodeAddr);

// Predict the future position based on current speed and direction

Position currentPosition = getNodePosition(nodeAddr);

Position predictedPosition;

predictedPosition.x = currentPosition.x + speed * cos(direction);

predictedPosition.y = currentPosition.y + speed * sin(direction);

return predictedPosition;

}

Example of Path Stability Check:

bool AODV::isPredictedPathStable(Position predictedPosition) {

// Check if the predicted position will lead to a stable route

// For example, ensure the predicted position is within communication range

if (distance(predictedPosition, myPosition()) < MAX_COMM_RANGE) {

return true;  // Path is stable

}

return false;  // Path is not stable

}

  1. Handle Path Prediction in Route Maintenance

Once the network topology alters (due to node mobility or changing traffic), we require to proactively adjust the route before the link breaks. It can be executed by periodically forecasting the future state of the route and updating the path before the present one becomes invalid.

In AODV, for instance, we can change the Route Maintenance mechanism to activate the route readjustment rely on the predicted path failures.

void AODV::routeMaintenance() {

// Periodically predict future path stability

if (!isPredictedPathStable(predictNodePosition(destAddr))) {

// If the predicted path is unstable, initiate route rediscovery

sendRequest(destAddr);

}

}

  1. Use Machine Learning for Path Prediction (Optional)

For more furthered path prediction, we can be executed the machine learning (ML) algorithms to forecast future network conditions depends on the historical data. It can be done by:

  • Training an ML model: Train a supervised learning model such as regression or decision trees on data like node mobility, packet loss, and delays.
  • Integrating the ML model: We can use the trained model to predict whether a route will remain stable in the nearby future.

If we can decide to use machine learning for path prediction, we may require to run the ML algorithms externally in Python or MATLAB and incorporate the outcomes with NS2 by interchanging the data via files or sockets.

Example:

void AODV::runMLPrediction(nsaddr_t destAddr) {

// Run an external ML script and get the prediction result

double prediction = runExternalScript(“predict_path_stability.py”, destAddr);

if (prediction < STABILITY_THRESHOLD) {

// If the prediction indicates instability, readjust the route

triggerRouteReadjustment(destAddr);

}

}

  1. Simulate Path Prediction in a Tcl Script

When we have changed the routing protocol to encompass the path prediction, we can replicate it in NS2 by writing a Tcl script, which describes the network topology, node mobility, and traffic.

Example Tcl Script for Path Prediction Simulation:

# Create a simulator

set ns [new Simulator]

# Create nodes

set node1 [$ns node]

set node2 [$ns node]

set node3 [$ns node]

# Define routing protocol (AODV with path prediction)

$ns rtproto AODV

# Set up mobility models for nodes

$ns at 0.0 “$node1 setdest 10.0 20.0 1.0”  ;# Set speed and direction

$ns at 0.0 “$node2 setdest 30.0 40.0 1.5”

$ns at 0.0 “$node3 setdest 50.0 60.0 2.0”

# Set up traffic sources

…

# Start the simulation

$ns run

  1. Evaluate the Simulation

When we have executed the path prediction then run the simulation, we can use trace files to examine the following metrics:

  • Route stability: Compute how frequently the predicted path remains the stable likened to the standard protocol.
  • Throughput: Examine how path prediction effects overall network throughput.
  • Packet delivery ratio: Calculate whether packet delivery increases with path prediction.
  • End-to-end delay: Verify if predictive routing minimizes latency by preventing broken links.

In this module, we followed a stepwise process that executing the Network Path Predication and assess the simulation via the simulation platform NS2. Further precise data on this topic will be presented as well.

Our team works on modeling dynamic networks, such as Mobile Ad-Hoc Networks (MANETs) and Vehicular Ad-Hoc Networks (VANETs), for our projects. Get in contact with us for the finest project direction and implementation outcomes.