How to Start Intelligent Agent WSN Projects Using NS2

To start an Intelligent Agent-based Wireless Sensor Network (WSN) project using NS2 (Network Simulator 2), we’ll want to incorporate an intelligent decision-making procedure to WSN nodes for energy-efficient routing, anomaly detection, or dynamic task allocation. Below is a stepwise approach to get started:

Steps to Start Intelligent Agent WSN Projects in NS2

  1. Understand Intelligent Agents in WSN
  • What is an Intelligent Agent in WSN?
    • In a WSN context, intelligent agent states to a software object that are inserted within sensor nodes, which make decisions autonomously according to the network conditions like modifying energy levels, choosing paths, or detecting anomalies.
  • Why use Intelligent Agents in WSN?
    • Enhance the energy efficiency.
    • To improve fault tolerance.
    • Allow adaptive routing according to the dynamic environmental modifications.
  1. Define the Goals of Your Project
  • Common Objectives:
    • Execute the intelligent routing protocols for energy-efficient interaction.
    • Mimic dynamic load balancing to utilise intelligent agents.
    • To improve network lifetime by enhancing the energy efficiency.
    • Identify and moderate the network anomalies with agents.
  1. Install NS2
  • Install NS2 on Linux:

sudo apt-get update

sudo apt-get install ns2

  • Confirm installation by running:

ns

If % emerges then the installation is effectively set up.

  1. Design Your Network Topology
  • Components of an Intelligent WSN:
    • Sensor Nodes: Nodes including restricted power and computational capacity.
    • Sink Node: The central node to gather information from sensor nodes.
    • Intelligent Agents: In these agents, software inserted within sensor nodes for decision-making.
  • Example Topology:
    • One sink node for data collection.
    • Several sensor nodes like grid or random distribution.
  1. Create a Basic WSN Simulation

Below is a simple WSN simulation script including agent-based functionality:

Example Script:

# Create a Simulator

set ns [new Simulator]

# Trace and NAM Files

set tracefile [open wsn_agent.tr w]

$ns trace-all $tracefile

set namfile [open wsn_agent.nam w]

$ns namtrace-all $namfile

# Define Nodes

set sink [$ns node]

set sensor1 [$ns node]

set sensor2 [$ns node]

set sensor3 [$ns node]

# Create Links

$ns duplex-link $sensor1 $sink 1Mb 10ms DropTail

$ns duplex-link $sensor2 $sink 1Mb 10ms DropTail

$ns duplex-link $sensor3 $sink 1Mb 10ms DropTail

# Energy Configuration (Simulating Power Constraints)

$sensor1 set energy_ 100  ;# Set energy for sensor1

$sensor2 set energy_ 120  ;# Set energy for sensor2

$sensor3 set energy_ 90   ;# Set energy for sensor3

# Intelligent Agent: Energy-Aware Routing

proc energy_aware_routing {src dest energy_threshold} {

global ns

set energy [$src set energy_]

if {$energy > $energy_threshold} {

$ns connect $src $dest

} else {

puts “Node [$src] does not have enough energy to transmit”

}

}

# Apply Intelligent Routing Logic

$ns at 1.0 “energy_aware_routing $sensor1 $sink 50”

$ns at 2.0 “energy_aware_routing $sensor2 $sink 50”

$ns at 3.0 “energy_aware_routing $sensor3 $sink 50”

# End Procedure

proc finish {} {

global ns tracefile namfile

$ns flush-trace

close $tracefile

close $namfile

exec nam wsn_agent.nam &

exit 0

}

# Schedule End of Simulation

$ns at 10.0 “finish”

# Run Simulation

$ns run

  1. Enhance the Simulation with Intelligent Agents
  • Energy-Aware Agent:
    • Make an intelligent agent, which dynamically modifies the transmission energy or chooses the paths according to the power levels.
  • Fault-Tolerant Agent:
    • In the network, execute agents which identify and avoid faulty nodes.
  • Data Aggregation Agent:
    • Set up agents, which gather information from numerous nodes for minimizing redundancy and storing energy.
  1. Simulate Advanced Scenarios
  • Dynamic Task Allocation:
    • Make use of agents for allocating sensing or transmitting tasks depends on the node capabilities and network conditions.

proc task_allocation_agent {node tasks} {

foreach task $tasks {

if {[$node set energy_] > 50} {

puts “Node [$node] allocated task: $task”

} else {

puts “Node [$node] does not have enough energy for task: $task”

}

}

}

  • Intrusion Detection:
    • Make agents, which observe the traffic and identify the anomalies like high packet drop rates, unauthorized access.
  • Mobility and Adaptive Routing:
    • Replicate the mobile nodes including agents, which actively adjust the routes.
  1. Analyze the Results
  • Metrics to Measure:
    • Network lifetime: Estimate how long the network functions before nodes execute beyond the energy.
    • Packet delivery ratio: Measure the rate of packets that are effectively distributed.
    • Energy consumption: Examine every single node and network-broad energy utilization.
    • Latency: Estimate the delays which are triggered by routing decisions.
  • Trace File Analysis: Envision the trace files (wsn_agent.tr) and obtain performance parameters using AWK or Python:

grep “ENERGY” wsn_agent.tr > energy_analysis.txt

  • Visualization with NAM: Go to the .nam file for monitoring the simulation:

nam wsn_agent.nam

  1. Advanced Intelligent Agent WSN Project Ideas
  • Energy-Efficient Routing: Execute an agent-based routing protocol such as LEACH or PEGASIS and then equate it with WSN routing protocols.
  • Data Aggregation and Compression: Collect and compress information to leverage intelligent agents for efficient transmission.
  • Fault Detection: Enhance the agents for identifying and separating faulty nodes, to make sure reliable interaction.
  • Intrusion Detection Systems (IDS): Mimic agents, which identify and reply to network attacks.
  • Load Balancing: Execute the agents to evenly deliver the traffic over network for preventing node overloading.
  1. Document Your Project
  • Include:
    • It provides detailed projects goals and problem statement.
    • Create network topology.
    • Estimate the simulation metrics like energy levels, node positions.
    • Examine the performance parameters such as energy consumption, delivery ratio.
    • Then, visualize the performance, graphs, and conclusions.

You can discover more details and simulation process for Intelligent Agent WSN Projects, which was simulated and analysed using NS2 environment. If you want any more guidance about custom configurations, agent-based logic, or advanced scenarios in NS2, we will provide it to you.