How to Implement Network Data fusion in OMNeT++
To implement the network data fusion in OMNeT++ has encompasses merging data from multiple sources like sensors to give a more precise, reliable, or complete result than any one data source might offer. It is usually used in IoT systems, distributed systems, and sensor networks where numerous nodes gather data that wants to be combined or fused for decision-making. Given below is a approaches on how to executing network data fusion in OMNeT++ using the INET framework:
Step-by-Step Implementations:
- Set up OMNeT++ and INET Framework
- Install OMNeT++: Make sure that OMNeT++ is installed and configured on the system.
- Install INET Framework: Download and install the INET framework, which offers models for network communication, mobility, and other components wanted for data fusion.
- Define the Network Topology
Form a network topology that contains several sensor nodes and a central node or nodes responsible for combining the data gathered by the sensors.
Example NED File (DataFusionNetwork.ned):
package mynetwork;
import inet.node.inet.StandardHost;
import inet.node.inet.Router;
network DataFusionNetwork
{
parameters:
int numSensors = default(5); // Number of sensor nodes
submodules:
fusionCenter: StandardHost {
@display(“p=500,300”);
}
sensor[numSensors]: StandardHost {
@display(“p=100,100;is=square,red”);
}
router: Router {
@display(“p=300,300”);
}
connections allowunconnected:
for i=0..numSensors-1 {
sensor[i].pppg++ <–> ethernetLine <–> router.pppg++;
}
router.pppg++ <–> ethernetLine <–> fusionCenter.pppg++;
}
In this example:
- sensor[]: Signifies numerous sensor nodes that gather data.
- router: Performs as a central node to route data from sensors to the fusion center.
- fusionCenter: Denotes the node where data fusion gets place.
- Implement Data Collection at Sensor Nodes
For processing, each sensor node would gather data and send it to the fusion centre. The data can be basic like temperature readings or complex such as video frames.
Example: Simple Data Collection and Transmission (C++)
#include “inet/common/INETDefs.h”
#include “inet/applications/base/ApplicationBase.h”
#include “inet/applications/udpapp/UdpBasicApp.h”
Define_Module(SensorNode);
void SensorNode::initialize(int stage) {
ApplicationBase::initialize(stage);
if (stage == INITSTAGE_LOCAL) {
dataCollectionInterval = par(“dataCollectionInterval”).doubleValue();
scheduleAt(simTime() + dataCollectionInterval, collectDataTimer);
}
}
void SensorNode::handleMessageWhenUp(cMessage *msg) {
if (msg == collectDataTimer) {
collectAndSendData();
scheduleAt(simTime() + dataCollectionInterval, collectDataTimer);
} else {
ApplicationBase::handleMessageWhenUp(msg);
}
}
void SensorNode::collectAndSendData() {
double sensorReading = uniform(0, 100); // Simulate a sensor reading
EV << “Sensor reading: ” << sensorReading << “\n”;
// Create a packet with the sensor data
auto packet = new cPacket(“SensorData”);
packet->addPar(“sensorValue”) = sensorReading;
// Send the data to the fusion center
send(packet, “pppg$o”);
}
In this example:
- collectAndSendData(): Mimics gathering data from a sensor and transferring it to the fusion center.
- collectDataTimer: Activates data collection at regular intervals.
- Implement Data Fusion at the Fusion Center
The fusion logic can be as simple as close to the values or as difficult as executing Kalman filtering or other advanced methods. The fusion center obtains data from numerous sensors, processes it, and gives a fused result.
Example: Basic Data Fusion Logic (C++)
#include “inet/common/INETDefs.h”
#include “inet/applications/base/ApplicationBase.h”
#include “inet/applications/udpapp/UdpBasicApp.h”
Define_Module(FusionCenter);
void FusionCenter::initialize(int stage) {
ApplicationBase::initialize(stage);
if (stage == INITSTAGE_LOCAL) {
numSensors = par(“numSensors”).intValue();
sensorDataSum = 0;
sensorDataCount = 0;
}
}
void FusionCenter::handleMessageWhenUp(cMessage *msg) {
if (msg->isPacket()) {
handleSensorData(check_and_cast<cPacket *>(msg));
} else {
ApplicationBase::handleMessageWhenUp(msg);
}
}
void FusionCenter::handleSensorData(cPacket *packet) {
double sensorValue = packet->par(“sensorValue”).doubleValue();
sensorDataSum += sensorValue;
sensorDataCount++;
EV << “Received sensor data: ” << sensorValue << “\n”;
// Perform data fusion when data from all sensors is received
if (sensorDataCount == numSensors) {
double fusedData = sensorDataSum / sensorDataCount; // Simple average
EV << “Fused data: ” << fusedData << “\n”;
// Reset for next round of data fusion
sensorDataSum = 0;
sensorDataCount = 0;
// Process or send the fused data to another component
processFusedData(fusedData);
}
delete packet;
}
void FusionCenter::processFusedData(double fusedData) {
// Example: Log the fused data or send it to another node
EV << “Processing fused data: ” << fusedData << “\n”;
}
In this example:
- handleSensorData(): Obtains data from sensor nodes and adds it to a running sum.
- processFusedData(): Processes the fused result, which in this instance is a simple average.
- Simulate and Monitor Data Fusion
Run the simulation and observe the data fusion process. We can track metrics like the exactness of the fused data, the impact on network load, and the time taken to fuse data.
Example Configuration for Monitoring Metrics:
[General]
network = DataFusionNetwork
**.fusionCenter.fusedData.recordScalar = true
**.fusionCenter.fusionTime.recordScalar = true
This configuration records the fused data and the duration to complete the fusion process.
- Analyse and Optimize Data Fusion Techniques
Examine the results to determine the efficiency of the data fusion strategy, after running the simulation. Consider factors like:
- Accuracy: How close the fused data is to the expected or real value.
- Latency: The duration to gather data from all sensors and produce a fused result.
- Network Load: The influence of data collection and transmission on the network.
- Extend the Data Fusion Strategy
We can extend the simple data fusion strategy with further features like:
- Advanced Fusion Algorithms: Execute more sophisticated fusion methods such as Bayesian networks, or neural networks, Kalman filters.
- Data Validation: Execute techniques to detect and manage faulty or outlier sensor data before fusion.
- Distributed Fusion: Implement data fusion at intermediate nodes before a central node to decrease latency and network load.
Example: Distributed Data Fusion
void IntermediateNode::handleSensorData(cPacket *packet) {
double sensorValue = packet->par(“sensorValue”).doubleValue();
intermediateDataSum += sensorValue;
intermediateDataCount++;
if (intermediateDataCount == numSensorsInCluster) {
double localFusedData = intermediateDataSum / intermediateDataCount;
EV << “Locally fused data: ” << localFusedData << “\n”;
// Send local fused data to the central fusion center
sendLocalFusedData(localFusedData);
// Reset for next round of data fusion
intermediateDataSum = 0;
intermediateDataCount = 0;
}
delete packet;
}
- Document and Report Findings
Document the data fusion strategies verified, the results attained, and any optimizations created. It will be supported to knowing the efficiency of various data fusion methods in numerous network situations.
In this setup, we had learned about Network Data fusion, their approaches, techniques, and instances that helps to execute the Data fusion in OMNeT++ using INET framework. We will be given further details based on your requirements.
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