How to Implement Swarm networking in ns2

To implement Swarm Networking in NS2 has needs to contain a network of autonomous agents like drones, robots, or sensors that collaborate to complete the tasks via the decentralized communication and cooperation. Swarm networking is stimulated by the behaviour of social insects such as ants or bees, in which the simple agents follow local rules to attain complex collective behaviour. The given below is the procedure to implement the simple Swarm Networking scenario in NS2:

Step-by-Step Implementation:

  1. Understand Swarm Networking Components:
  • Swarm Agents: Autonomous units like drones, robots that interact with each other and make decentralized decisions to perform tasks.
  • Communication Nodes: Nodes that enable communication among swarm agents.
  • Tasks: Missions or objectives that the swarm desires to achieve, like exploration, search and rescue, or mapping.
  1. Set Up the NS2 Environment:
  • Make sure NS2 is installed on system.
  • Understand with writing TCL scripts, as NS2 simulations are controlled through TCL.
  1. Define the Network Topology:
  • Generate nodes that signify swarm agents. These nodes will communicate to simulate the swarm networking environment.

# Define the simulator

set ns [new Simulator]

# Create a trace file for analysis

set tracefile [open out.tr w]

$ns trace-all $tracefile

# Create a NAM file for animation

set namfile [open out.nam w]

$ns namtrace-all-wireless $namfile 10

# Set up the network parameters

set opt(chan)   Channel/WirelessChannel      ;# Channel type

set opt(prop)   Propagation/TwoRayGround     ;# Radio-propagation model

set opt(netif)  Phy/WirelessPhy              ;# Network interface type

set opt(mac)    Mac/802_11                   ;# MAC type

set opt(ifq)    Queue/DropTail/PriQueue      ;# Interface queue type

set opt(ll)     LL                           ;# Link layer type

set opt(ant)    Antenna/OmniAntenna          ;# Antenna model

set opt(ifqlen) 50                           ;# Max packet in ifq

set opt(x)      1000                         ;# X dimension of the topography

set opt(y)      1000                         ;# Y dimension of the topography

set opt(adhocRouting) AODV                   ;# Ad hoc routing protocol

# Create a topography object

create-god 50

# Configure the nodes (e.g., swarm agents)

$ns node-config -adhocRouting $opt(adhocRouting) \

-llType $opt(ll) \

-macType $opt(mac) \

-ifqType $opt(ifq) \

-ifqLen $opt(ifqlen) \

-antType $opt(ant) \

-propType $opt(prop) \

-phyType $opt(netif) \

-channelType $opt(chan) \

-topoInstance $topo \

-agentTrace ON \

-routerTrace ON \

-macTrace OFF \

-movementTrace ON

# Create swarm agents

set agent1 [$ns node]  ;# Swarm Agent 1

set agent2 [$ns node]  ;# Swarm Agent 2

set agent3 [$ns node]  ;# Swarm Agent 3

set agent4 [$ns node]  ;# Swarm Agent 4

# Set initial positions for the nodes

$agent1 set X_ 100.0

$agent1 set Y_ 500.0

$agent1 set Z_ 0.0

$agent2 set X_ 200.0

$agent2 set Y_ 500.0

$agent2 set Z_ 0.0

$agent3 set X_ 300.0

$agent3 set Y_ 500.0

$agent3 set Z_ 0.0

$agent4 set X_ 400.0

$agent4 set Y_ 500.0

$agent4 set Z_ 0.0

  1. Simulate Communication Between Swarm Agents:
  • Configure communication links among the swarm agents to permits them to deliver information and make decentralized decisions.

# Create duplex links between swarm agents

$ns duplex-link $agent1 $agent2 10Mb 10ms DropTail

$ns duplex-link $agent2 $agent3 10Mb 10ms DropTail

$ns duplex-link $agent3 $agent4 10Mb 10ms DropTail

  1. Simulate Decentralized Decision-Making:
  • Execute decentralized decision-making, in which each agent follows local rules to complete the overall swarm objective.

# Example procedure for decentralized task assignment

proc assign_task {agent task} {

global ns

puts “$agent is assigned to $task”

$ns at [expr $ns now + 0.1] “$agent perform_task $task”

}

# Assign tasks to swarm agents

$ns at 1.0 “assign_task $agent1 {explore_area}”

$ns at 1.5 “assign_task $agent2 {collect_data}”

$ns at 2.0 “assign_task $agent3 {relay_communication}”

$ns at 2.5 “assign_task $agent4 {monitor_status}”

  1. Implement Swarm Behavior:
  • Execute behaviours like flocking, formation, or dispersion, in which agents coordinate their movements based on local rules.

# Example procedure for flocking behavior

proc flocking_behavior {agent1 agent2 agent3 agent4} {

global ns

puts “Flocking behavior: Agents moving together”

$ns at [expr $ns now + 1.0] “$agent1 setdest 500.0 600.0 10.0”

$ns at [expr $ns now + 1.0] “$agent2 setdest 600.0 600.0 10.0”

$ns at [expr $ns now + 1.0] “$agent3 setdest 700.0 600.0 10.0”

$ns at [expr $ns now + 1.0] “$agent4 setdest 800.0 600.0 10.0”

}

# Schedule flocking behavior

$ns at 3.0 “flocking_behavior $agent1 $agent2 $agent3 $agent4”

  1. Simulate Swarm Task Execution:
  • Execute task execution in which agents collaborate to finish a mission, like discovering an area, collecting data, or relaying messages.

# Example procedure for task execution

proc execute_task {agent task} {

global ns

puts “$agent is executing task: $task”

$ns at [expr $ns now + 0.1] “$agent perform_task $task”

}

# Schedule task execution for the swarm

$ns at 4.0 “execute_task $agent1 {explore}”

$ns at 4.5 “execute_task $agent2 {collect_data}”

$ns at 5.0 “execute_task $agent3 {relay_communication}”

$ns at 5.5 “execute_task $agent4 {monitor_status}”

  1. Run the Simulation:
  • Describe as they the simulation should terminate and run it. The finish procedure will close the trace files and launch NAM for visualization.

# Define the finish procedure

proc finish {} {

global ns tracefile namfile

$ns flush-trace

close $tracefile

close $namfile

exec nam out.nam &

exit 0

}

# Schedule the finish procedure at 10 seconds

$ns at 10.0 “finish”

# Run the simulation

$ns run

  1. Analyse the Results:
  • Use the trace file (out.tr) to evaluate data transmission, network performance, and communication between swarm agents.
  • Open the NAM file (out.nam) to visualize the network operations and monitor the communication among swarm agents.
  1. Customize and Extend:
  • We can customize the simulation by:
    • Attach more swarm agents and executing more complex swarm behaviours.
    • Executing advanced swarm scenarios, like dynamic environment exploration, search and rescue missions, or autonomous data collection.
    • Replicate various scenarios, like agent failures, changing communication ranges, or real-time decision-making challenges.

Example Summary:

This sample configures a basic Swarm Networking simulation in NS2 that concentrates on communication among the swarm agents and decentralized decision-making. The simulation will show on how swarm agents can collaborate to complete tasks via simple local interactions.

Advanced Considerations:

  • For more complex scenarios, deliberate to incorporating NS2 with specialized swarm robotics simulation tools or evolving custom modules to emulate an advanced swarm behaviours, like an adaptive learning, obstacle avoidance, or cooperative manipulation.
  • Expand the simulation to contains an advanced features such as Quality of Service (QoS) management, security (e.g., encryption, authentication), or fault tolerance in swarm networks.

Debugging and Optimization:

  • Use the trace-all command to debug the simulation and evaluate packet flows.
  • Enhance the simulation by refining communication protocols, modifying swarm behaviours, and tuning network metrics for better performance and effectiveness.

In the end, we had implemented the Swarm Networking that generates the nodes and it handles to complete the tasks via the decentralized communication in ns2 simulation. If you have any doubts regarding the Swarm Networking we will provide that too. We focus on decentralized communication and collaboration tailored to your projects, providing comprehensive explanations. Reach out to ns2project.com for top-notch support and to receive innovative thesis ideas and topics from us.