How to Start Swarm Networking Projects Using NS2

To create a Swarm Networking project using Network Simulator 2 (NS2) has involves the replicate of network that several devices such as often autonomous coordinate their activities we successfully a general goal. Swarm networking typically denotes the systems in which the decentralized nodes such as drones, robots, or other autonomous agents cooperate the perform tasks such as data collection, environmental tracking or communication relay.

In the context of NS2, swarm networks can be replicated through modeling the several devices as nodes which communicate via wireless protocols such as Wi-Fi, Zigbee, or 5G and use swarm intelligence principles such as distributed control, cooperation, and self-organization.

Here’s a guide on how to start a Swarm Networking project using NS2.

Steps to Start Swarm Networking projects using NS2

Key Concepts in Swarm Networking

  1. Swarm Intelligence: The behavior of a large number of basic agents which exhibit collective behavior. Every agent observes the simple rules, and system collectively achieves complex goals for sample self-organizing networks, collaborative task-solving.
  2. Autonomous Devices: Devices such as drones, robots, or sensors which perform autonomously according to local information or swarm algorithms.
  3. Distributed Control: Every device are works on independently, nevertheless their move are coordinated in the swarm deprived of central control.
  4. Wireless Communication: Swarm networks are typically use the wireless technologies such as Wi-Fi, Zigbee, Bluetooth, or 5G for communication between swarm members.
  5. Routing Protocols: Swarm networks can be tailed use for routing algorithms such as AODV, DSR, or level of self-organizing protocols for communication between devices.
  6. Collaborative Tasks: The swarm network can be replicating the tasks such as data collection, movement coordination, or collective communication.

Step 1: Install NS2

Assure which we have NS2 installed on the machine. We can download and install it from the official NS2 website.

To check if NS2 is installed:

ns

If the NS2 prompt opens, the installation is successful.

Step 2: Define the Swarm Network’s Key Components

In a swarm network, core components include:

  1. Nodes (Agents): The swarm’s members such as robots, drones, or devices which communicate and coordinate.
  2. Communication Medium: The connection mediums have wireless communication connections for sample Wi-Fi, Zigbee, etc.
  3. Routing Protocol: Distributed routing protocols can be used the AODV, OLSR, or DSR.
  4. Swarm Intelligence Algorithms: This procedure assures the coordination between swarm members. In NS2, this can be modeled indirectly with communication protocols or network behavior patterns.

Step 3: Design the Network Topology

For swarm networks, a common topology is:

  • Ad-hoc Network: Nodes are communicated by every other directly, often using multi-hop communication.
  • Mesh or Grid: In similar swarm networks, nodes can form a grid or mesh topology in which they relay data to every other.
  • Dynamic Movement: Nodes such as drones can have a mobility model which assigns the action in the condition for sample based on a Random Waypoint model or Constant Speed).

Step 4: Create the Simulation Script

Let’s assume the basic scenario in which several multiple devices like as drone-like nodes form a swarm and communicate with every other. We will use a wireless communication model for instance Wi-Fi or a similar protocol and a routing protocol for sample AODV for communication.

Here’s an example NS2 script for simulating a basic Swarm Network with mobile nodes:

Example: Swarm Networking Simulation Using NS2

# Create the simulator object

set ns [new Simulator]

# Set up trace files to collect data

set tracefile “swarm_network.tr”

$ns trace-all $tracefile

# Create nodes (representing swarm members)

set drone1 [$ns node]

set drone2 [$ns node]

set drone3 [$ns node]

set drone4 [$ns node]

# Set node positions and movement model (e.g., random waypoint)

$ns at 0.0 “$drone1 set X_ 100; $drone1 set Y_ 100”

$ns at 0.0 “$drone2 set X_ 200; $drone2 set Y_ 200”

$ns at 0.0 “$drone3 set X_ 300; $drone3 set Y_ 300”

$ns at 0.0 “$drone4 set X_ 400; $drone4 set Y_ 400”

 

# Set mobility model (Random Waypoint)

$ns node-config -adhocRouting AODV -llType LL -macType Mac/802_11 -ifqType Queue/DropTail \

-ifqLen 50 -antType Antenna/OmniAntenna -phyType Phy/WirelessPhy -topoInstance $topo

# Set mobility: nodes will move with random waypoint model

$ns at 1.0 “$drone1 random-motion”

$ns at 1.0 “$drone2 random-motion”

$ns at 1.0 “$drone3 random-motion”

$ns at 1.0 “$drone4 random-motion”

# Create agents (UDP for communication between swarm members)

set udp_drone1 [new Agent/UDP]

set udp_drone2 [new Agent/UDP]

set udp_drone3 [new Agent/UDP]

set udp_drone4 [new Agent/UDP]

# Attach UDP agents to nodes

$ns attach-agent $drone1 $udp_drone1

$ns attach-agent $drone2 $udp_drone2

$ns attach-agent $drone3 $udp_drone3

$ns attach-agent $drone4 $udp_drone4

# Create traffic generators (CBR traffic for data transmission)

set cbr_drone1 [new Application/Traffic/CBR]

set cbr_drone2 [new Application/Traffic/CBR]

set cbr_drone3 [new Application/Traffic/CBR]

set cbr_drone4 [new Application/Traffic/CBR]

# Attach CBR traffic to UDP agents

$cbr_drone1 attach-agent $udp_drone1

$cbr_drone2 attach-agent $udp_drone2

$cbr_drone3 attach-agent $udp_drone3

$cbr_drone4 attach-agent $udp_drone4

# Set packet size and interval for the CBR traffic

$cbr_drone1 set packetSize_ 512

$cbr_drone1 set interval_ 0.5

 

$cbr_drone2 set packetSize_ 512

$cbr_drone2 set interval_ 0.5

$cbr_drone3 set packetSize_ 512

$cbr_drone3 set interval_ 0.5

$cbr_drone4 set packetSize_ 512

$cbr_drone4 set interval_ 0.5

# Start traffic generation at time 1.0 seconds

$ns at 1.0 “$cbr_drone1 start”

$ns at 1.0 “$cbr_drone2 start”

$ns at 1.0 “$cbr_drone3 start”

$ns at 1.0 “$cbr_drone4 start”

# Stop traffic generation at 4.0 seconds

$ns at 4.0 “$cbr_drone1 stop”

$ns at 4.0 “$cbr_drone2 stop”

$ns at 4.0 “$cbr_drone3 stop”

$ns at 4.0 “$cbr_drone4 stop”

# Finish the simulation after 5 seconds

$ns at 5.0 “finish”

# Define finish procedure

proc finish {} {

global ns

$ns flush-trace

exit 0

}

# Run the simulation

$ns run

Key Aspects of the Script:

  1. Node Creation: Four drones are generated such as drone1, drone2, drone3, and drone4 that classify the swarm members.
  2. Node Positioning and Mobility:
    • The Random Waypoint design is used for node action, that replicates the drones moving randomly in the network.
    • The nodes are primarily located at the specific coordinates.
  3. Routing Protocol: AODV is selected as the routing protocol for the swarm network and we assign the decentralized routing.
  4. Traffic Generation:
    • The CBR (Constant Bit Rate) congestion is used to replicate the data modification among the swarm members.
    • Every drone forwards the data for other drones.
  5. Simulation Timings:
    • The traffic begins at 1.0 seconds and drop at 4.0 seconds.

Step 5: Run the Simulation

Store the script as swarm_network.tcl and run it in NS2:

ns swarm_network.tcl

Step 6: Analyze the Results

The replication will create a trace file for instance swarm_network.tr that we can analyze to calculate the performance of the swarm network. General performance metrics include:

  • Packet delivery ratio (PDR)
  • End-to-end delay
  • Throughput
  • Routing efficiency

We can use tools like AWK to filter and process the trace file:

awk ‘{ if ($1 == “r”) print $0 }’ swarm_network.tr > received_packets.txt

Step 7: Extend the Simulation

We can encompass the replication through the following:

  1. Dynamic Swarm Behavior: execute the procedures for swarm intelligence to permits the nodes change behavior according to network conditions for sample leader election, task division.
  2. Energy Consumption: Enhance the energy design to replicate on how energy consumption impacts the performance of swarm nodes.
  3. Advanced Routing Protocols: Discover further self-organizing routing protocols such as DSR or OLSR for improved swarm coordination.
  4. Security: Incorporate the security protocols for safe communication in the swarm.
  5. Scalability: Improve the number of nodes and validate the scalability for the swarm network.

This guide gives you the basic framework for simulating a swarm network using NS2. The key challenge is to model the swarm’s self-organizing behavior, and while NS2 does not directly model swarm intelligence, you can simulate it using proper network protocols and mobility models.

At the last, we clearly explained to execute and analyse the results for the Swarm Networking in ns2 implementation framework. If you need any support regarding Swarm Networking we will help and provide the related information.