Performance Analysis in Cross Layer protocol in IoT

Performance Analysis in Cross Layer protocol in IoT

 Scenario 1: MCROSS Protocol With Static network

Step 1: Initially, we constructed an IoT network with 200 Sensor nodes , 1 Base Station (BS), and 1 Server .

Step 2: Then, we implement the MCROSS protocol with DSDMAC-based MAC layer optimization and directional communication.

Step 3: Next, we simulate the network and collect the multimedia network traffic data generated during transmission.

Step 4: Finally, we analyzed performance metrics for the following:

4.1 Number of Sensor Nodes Vs. Throughput
4.2 Number of Sensor Nodes Vs. Delay
4.3 Number of Sensor Nodes Vs. Energy Consumption
4.4 Number of Sensor Nodes Vs. Load

 Scenario 2: CL-HHO Protocol With Static network

Step 1: Initially, we constructed an IoT network with 200 Sensor nodes , 1 Base Station (BS), and 1 Server .

Step 2: Then, we implement the CLHHO protocol using cross-layer Harris Hawk Optimization for adaptive route selection

Step 3: Next, we simulate the network and collect the multimedia network traffic data generated during transmission.

Step 4: Finally, we analyzed performance metrics for the following:

4.1 Number of Sensor Nodes Vs. Throughput
4.2 Number of Sensor Nodes Vs. Delay
4.3 Number of Sensor Nodes Vs. Energy Consumption
4.4 Number of Sensor Nodes Vs. Load

 Scenario 3: ONCP Protocol With Static network

Step 1: Initially, we constructed an IoT network with 200 Sensor nodes , 1 Base Station (BS), and 1 Server .

Step 2: Then, we implement the ONCP protocol using modified PSO with crossover and mutation for optimal clustering and routing,

Step 3: Next, we simulate the network and collect the multimedia network traffic data generated during transmission.

Step 4: Finally, we analyzed performance metrics for the following:

4.1 Number of Sensor Nodes Vs. Throughput
4.2 Number of Sensor Nodes Vs. Delay
4.3 Number of Sensor Nodes Vs. Energy Consumption
4.4 Number of Sensor Nodes Vs. Load

Scenario 4: MCROSS Protocol With dynamic network

Step 1: Initially, we constructed an IoT network with 30 Sensor  nodes (dynamic mobility) , 1 Base Station (BS), and 1 Server .

Step 2: Then, we implement the MCROSS protocol with DSDMAC-based MAC layer optimization and directional communication.

Step 3: Next, we simulate the network with dynamic mobility and collect the multimedia network traffic data generated during transmission.

Step 4: Finally, we analyzed performance metrics for the following:

4.1 Number of Sensor Nodes Vs. Throughput
4.2 Number of Sensor Nodes Vs. Delay
4.3 Number of Sensor Nodes Vs. Energy Consumption
4.4 Number of Sensor Nodes Vs. Load

 Scenario 5: CL-HHO Protocol With dynamic network

Step 1: Initially, we constructed an IoT network with 30 Sensor  nodes (dynamic mobility) , 1 Base Station (BS), and 1 Server .

Step 2: Then, we implement the CLHHO protocol using cross-layer Harris Hawk Optimization for adaptive route selection

Step 3: Next, we simulate the network with dynamic mobility and collect the multimedia network traffic data generated during transmission.

Step 4: Finally, we analyzed performance metrics for the following:

4.1 Number of Sensor Nodes Vs. Throughput
4.2 Number of Sensor Nodes Vs. Delay
4.3 Number of Sensor Nodes Vs. Energy Consumption
4.4 Number of Sensor Nodes Vs. Load

 Scenario 6: ONCP Protocol With dynamic network

Step 1: Initially, we constructed an IoT network with 30 Sensor nodes  (dynamic mobility), 1 Base Station (BS), and 1 Server .

Step 2: Then, we implement the ONCP protocol using modified PSO with crossover and mutation for optimal clustering and routing,

Step 3: Next, we simulate the network with dynamic mobility and collect the multimedia network traffic data generated during transmission.

Step 4: Finally, we analyzed performance metrics for the following:

4.1 Number of Sensor Nodes Vs. Throughput
4.2 Number of Sensor Nodes Vs. Delay
4.3 Number of Sensor Nodes Vs. Energy Consumption
4.4 Number of Sensor Nodes Vs. Load

Software Requirements:

1. Development Tool:  NS 2 with Python
2. Operating System:  Ubuntu 18.04 LTS (64-bit) or above

4) Project based on Simulation only.

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