How to Implement network Intelligent Agents in OMNeT++
To implement the Network Intelligent Agents in OMNeT++, according to the environment we have to generate an agent that can make decisions or use their behavior in the simulation network. The agents can be used to design different perspectives like network routing, resource management, or decision-making processes. Follow the step-by-step guide on how to set up and simulate intelligent agents in OMNeT++:
Step-by-Step Implementation:
- Understand Intelligent Agents
Intelligent agents are entities that:
- Perceive: Collect information about their environment.
- Decide: Make decisions depend on the gathered information and their objectives.
- Act: Take actions to accomplish their intents or enhance their performance.
- Set Up OMNeT++ Environment
Make certain OMNeT++ and the INET framework are installed. OMNeT++ offers the simulation environment, and INET contains network models that you can extend for intelligent agent scenarios.
- Create a New OMNeT++ Project
Open OMNeT++ and develop a new project for intelligent agent simulation.
- Define Network Topology
Generate the network components and their connectivity in .ned files. Contain nodes that will behave as intelligent agents.
Example:
network IntelligentAgentNetwork
{
submodules:
agent1: IntelligentAgent {
@display(“i=agent”);
}
agent2: IntelligentAgent {
@display(“i=agent”);
}
agent3: IntelligentAgent {
@display(“i=agent”);
}
connections:
agent1.out –> agent2.in;
agent2.out –> agent3.in;
agent1.out –> agent3.in;
}
- Define Intelligent Agent Module
State a module for the intelligent agents. This module should have parameters and gates, and the skill to perform actions depends on internal logic.
Example:
simple IntelligentAgent
{
parameters:
double decisionInterval = 10s; // Interval for making decisions
gates:
inout in;
inout out;
}
- Implement Intelligent Agent Logic
Execute the logic for intelligent agents in the node’s C++ code. This encompasses perception (gathering data), decision-making (processing data), and action (performing tasks relevant to the decisions).
Example:
class IntelligentAgent : public cSimpleModule
{
private:
double decisionInterval;
cMessage *decisionMsg;
protected:
virtual void initialize() override;
virtual void handleMessage(cMessage *msg) override;
void makeDecision();
void actOnDecision();
public:
// Method to gather information (e.g., network status)
void perceiveEnvironment();
};
void IntelligentAgent::initialize()
{
decisionInterval = par(“decisionInterval”);
decisionMsg = new cMessage(“decision”);
scheduleAt(simTime() + decisionInterval, decisionMsg); // Schedule initial decision
}
void IntelligentAgent::handleMessage(cMessage *msg)
{
if (msg == decisionMsg) {
makeDecision();
actOnDecision();
scheduleAt(simTime() + decisionInterval, decisionMsg); // Schedule next decision
} else {
// Handle other messages
}
}
void IntelligentAgent::makeDecision()
{
perceiveEnvironment();
// Implement decision-making logic here
// For example, decide on a routing path or resource allocation
}
void IntelligentAgent::actOnDecision()
{
// Implement action based on the decision made
// For example, update routing tables or send control messages
}
void IntelligentAgent::perceiveEnvironment()
{
// Gather information about the network or environment
// For example, check link quality, node status, etc.
}
- Configure Simulation Parameters
Alter the omnetpp.ini file to set parameters related to the intelligent agents like decision intermissions and any other relevant settings.
Example:
[Config IntelligentAgentNetwork]
network = IntelligentAgentNetwork
**.agent1.decisionInterval = 15s
**.agent2.decisionInterval = 20s
**.agent3.decisionInterval = 10s
- Run the Simulation
Compile and run the simulation. Monitor how the intelligent agents interact with each other and their environment, and how their decisions impacts network performance.
- Analyze Results
Assess performance metrics like:
- Decision Effectiveness: How well the agents’ decisions improve network performance.
- Adaptability: How agents adapt to differing conditions.
- Interaction: How agents’ actions influence the actions of other nodes in the network.
Use OMNeT++’s analysis tools and visualization features to understand the simulation results.
We presented the entire details on how to setup the simulation network including their properties and how to develop the required topology and how to implement the Network Intelligent Agents in OMNeT++ through this guide. omnet-manual.com is here to help you with your network Intelligent Agents project. If you have new and exciting ideas, feel free to reach out to us! We’ll support you through every stage of your project and provide performance analysis results. We focus on various areas like network routing, resource management, and decision-making processes based on what your project needs.