How to Implement Network Edge Intelligence in NS2

To implement the Network Edge Intelligence within NS2, which refers to the integration of the Machine Learning (ML) or Artificial Intelligence (AI) at the edge of the network, and closer to the users, and devices. It permits the real-time processing, decision-making, and analytics at the edge, minimizing latency as well as enhancing the network efficiency. While NS2 is mainly a network simulator concentrated on the network protocols and traffic, we can replicate the edge intelligence by integrating some basic AI/ML-like decision-making logic at the edge nodes.

In NS2, we can replicate the Network Edge Intelligence by:

  1. Configure an edge node (such as a base station, gateway, or router) in which the decisions are created rely on the data received from several devices or users.
  2. Executing logic at the edge node to actively route, prioritize, or control traffic according to the predefined conditions (simulating the AI/ML aspect).
  3. Replicating the network performance enhancements like reducing latency or load balancing that outcomes from intelligent decision-making at the edge.

Steps to Implement Network Edge Intelligence in NS2:

  1. Define Network Topology: Configure a nodes are signifying devices (clients), edge nodes (with intelligence), and the core network or cloud.
  2. Implement Intelligent Decision-Making: Append the logic at the edge node to replicate AI/ML-based decisions such as traffic prioritization, load balancing, or congestion control.
  3. Simulate Different Traffic Flows: Made a traffic from devices and it use the intelligent edge node to process or manage the flow.
  4. Monitor and Analyze Performance: It use trace files to evaluate the impact of edge intelligence on network performance like latency reduction, bandwidth optimization.

Example: Simulating Network Edge Intelligence in NS2

We will be mimicked a situation in which an edge node performs as a smart gateway among the devices and the core network, creating an intelligent decisions concerning the traffic depends on the data it receives.

Example TCL Script for Network Edge Intelligence in NS2:

# Create a new simulator instance

set ns [new Simulator]

# Define output trace file for logging events

set tracefile [open edge_intelligence.tr w]

$ns trace-all $tracefile

# Define the animation file for NAM (optional)

set namfile [open edge_intelligence.nam w]

$ns namtrace-all $namfile

# Create network nodes: Clients (IoT devices), Edge Node (with intelligence), and Server

set client1 [$ns node]   # IoT Device 1

set client2 [$ns node]   # IoT Device 2

set edge_node [$ns node] # Edge node (intelligent gateway)

set server [$ns node]    # Core server

# Create duplex links between clients, edge node, and server

$ns duplex-link $client1 $edge_node 10Mb 10ms DropTail

$ns duplex-link $client2 $edge_node 10Mb 10ms DropTail

$ns duplex-link $edge_node $server 50Mb 20ms DropTail

# —————- Edge Intelligence Logic —————-

# This is a simplified example of edge intelligence. The edge node will prioritize

# traffic based on data type or traffic conditions, such as reducing delay-sensitive

# traffic (e.g., video streaming) or load balancing between devices.

proc edge_decision_making {} {

global ns client1 client2 edge_node server

# Intelligent decision at the edge node

puts “Edge node making intelligent decisions…”

# Example: Prioritize traffic from Client 1 (e.g., video streaming) based on some conditions

# This can be expanded into more sophisticated logic based on traffic patterns, load, etc.

if {[rand] < 0.5} {

puts “Prioritizing traffic from Client 1”

$ns at 1.0 “$ns link $client1 $edge_node queue-limit 50”

$ns at 1.0 “$ns link $client2 $edge_node queue-limit 10”

} else {

puts “Balancing traffic from both clients”

$ns at 1.0 “$ns link $client1 $edge_node queue-limit 30”

$ns at 1.0 “$ns link $client2 $edge_node queue-limit 30”

}

}

# —————- Traffic Simulation —————-

# Define traffic for Client 1 (Video Streaming using UDP)

set udp_client1 [new Agent/UDP]

$ns attach-agent $client1 $udp_client1

set udp_sink [new Agent/Null]

$ns attach-agent $server $udp_sink

$ns connect $udp_client1 $udp_sink

set cbr_client1 [new Application/Traffic/CBR]

$cbr_client1 attach-agent $udp_client1

$cbr_client1 set packetSize_ 1000

$cbr_client1 set rate_ 2Mb

$cbr_client1 set interval_ 0.01

# Define traffic for Client 2 (Sensor Data using TCP)

set tcp_client2 [new Agent/TCP]

$ns attach-agent $client2 $tcp_client2

set tcp_sink [new Agent/TCPSink]

$ns attach-agent $server $tcp_sink

$ns connect $tcp_client2 $tcp_sink

set ftp_client2 [new Application/FTP]

$ftp_client2 attach-agent $tcp_client2

# —————- Simulation Control —————-

# Schedule traffic generation

$ns at 0.5 “$cbr_client1 start”

$ns at 0.5 “$ftp_client2 start”

# Schedule edge intelligence decision-making process at 1 second

$ns at 1.0 “edge_decision_making”

# Schedule the stop time for traffic

$ns at 4.0 “$cbr_client1 stop”

$ns at 4.0 “$ftp_client2 stop”

# End the simulation at 5.0 seconds

$ns at 5.0 “finish”

# Define finish procedure to close trace files and execute NAM for visualization

proc finish {} {

global ns tracefile namfile

$ns flush-trace

close $tracefile

close $namfile

exec nam edge_intelligence.nam &

exit 0

}

# Run the simulation

$ns run

Explanation of the Script:

  1. Node Setup:
    • We make a nodes for two clients (representing IoT devices), an edge node (acting as a smart gateway), and a server (representing the core cloud).
  2. Link Setup:
    • Duplex links are made among the edge node and the clients and among the server and edge node.
    • The edge node replicates an intelligent device capable of creating a decisions concerning the traffic it manages.
  3. Edge Intelligence Logic:
    • The process edge_decision_making mimics the intelligent traffic management at the edge node. For instance, it prioritizes the traffic from Client 1 (e.g., video streaming) if it identifies, which this traffic wants to low latency or higher bandwidth.
    • The logic would be depends on the traffic conditions, load balancing, or specific application requirements, simulating the real-world edge intelligence use cases such as AI/ML-based decision-making.
  4. Traffic Simulation:
    • Client 1 sends UDP traffic (representing a video stream), when Client 2 sends TCP traffic (representing sensor data).
    • The traffic generation begins at 0.5 seconds, then the edge node starts creating intelligent decisions at 1.0 second.
  5. Traffic Prioritization:
    • The edge_decision_making function prioritizes traffic by altering the queue limits on the links among the clients and the edge node. If Client 1’s traffic is prioritized, it acquires a larger queue buffer likened to the Client 2.
  6. Simulation Control:
    • The simulation stops at 5.0 seconds, and trace files are made for analysis.
  1. Analyzing Network Edge Intelligence in the Simulation

When the simulation is finish, we can estimate the trace file (edge_intelligence.tr) to:

  • Monitor Traffic Prioritization: Observe how the edge node creates a decisions to prioritize one kind of traffic over another.
  • Measure Latency and Throughput: Liken the latency and throughput of traffic from the various clients to monitor the impact of the edge intelligence.
  • Packet Loss: Verify, if packets were dropped because of the congestion or queue overflow.
  1. Advanced Edge Intelligence Features

To create the simulation more realistic and advanced, we can:

  • Use ML-based Decision-Making: Replicate more difficult AI/ML models at the edge, which create a decisions according to the patterns monitored in the traffic. For instance, predicting congestion and changing the traffic flows dynamically.
  • Simulate Distributed Edge Intelligence: Execute the numerous edge nodes with intelligence and replicate their collaboration for distributed decision-making (e.g., load balancing over multiple edge nodes).
  • Introduce Congestion or Failures: Replicate the network congestion or link failures and monitor how the intelligent edge node alters the traffic routing and resource allocation in response.

Example: Adding Dynamic Load Balancing Based on Traffic

# Add logic for dynamic load balancing between clients

proc dynamic_load_balancing {} {

global ns client1 client2 edge_node

puts “Edge node performing dynamic load balancing…”

# Check traffic load and adjust queue limits dynamically

if {[rand] < 0.7} {

puts “Balancing traffic load equally between clients”

$ns link $client1 $edge_node queue-limit 25

$ns link $client2 $edge_node queue-limit 25

} else {

puts “Prioritizing traffic from Client 2 (sensor data)”

$ns link $client1 $edge_node queue-limit 10

$ns link $client2 $edge_node queue-limit 40

}

}

This extension permits the edge node to actively balance traffic load rely on the present conditions, mimicking more furthered intelligence capabilities.

We applied a sequential process to the Network Edge Intelligence, which was then executed and simulated using the simulation tool NS2. Further precise details on this topic will share as well.

If you need support with implementing Network Edge Intelligence in NS2, reach out to the ns2project.com team. We have great project ideas tailored to your research interests.