Performance Analysis of Machine Learning WSN Routing

Performance Analysis of Machine Learning Wireless Sensor Network Routing

Implementation plan:

Step 1: Initially, we construct the network with 50 – IOT Nodes and 2- Gateways.

Step 2: Then, we collect and preprocess the data from the IoT Healthcare Security Dataset.

Step 3: Next, we secure the data using a hybrid encryption scheme by combining ECC with ChaCha20-Poly1305 .

Step 4: Next, we Train the data using Random Forest with the Regularization-Based Adaptive Factor (RA),

Step 5: Next, we Classify the data using the Support Vector Machine (SVM) classifier.

Step 6: Next, we implement Decision trees and Priority-based data routing DT-PBD algorithm to avoid congestion.

Step 7: Finally, we plot graph for the following metrics:

7.1: No. of IOT Nodes vs. Precision (%)
7.2: No. of IOT Nodes vs. Classification accuracy (%)
7.3: No. of IOT Nodes vs. Recall (%)
7.4: No. of IOT Nodes vs. Throughput (Kbps)
7.5: No. of IOT Nodes vs. Packet delivery ratio (%)

Software Requirements:

1. Development Tool: OMNeT++ 6.0.1,Inet 4.5.4 with python
2. Operating System: Windows-10(64-bit) or above

Dataset:

Link : https://www.kaggle.com/datasets/faisalmalik/iot-healthcare-security-dataset

Note :-

1) If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.

2) Please note that this implementation plan does not include any further steps after it is put into implementation.

3) If the plan satisfies your requirement, Please confirm with us.

4) Project based on Simulation only, not a real time project.

5) Please understand that any modifications made to the confirmed implementation plan will not be made before or after the project development.

We perform with an Existing Reference 5: Title:-ACluster-Based Energy-Efficient Secure Optimal Path-Routing Protocol for Wireless Body-Area Sensor Networks

Related Pages