Performance Analysis of RPL Low Power Lossy IoT Networks

Performance Analysis of RPL on Objective Function for Low Power and Lossy IoT Networks

Implementation Plan:
——————————-
Step 1: Initially we construct a network with 50-IoT Nodes, 1-Sink Nodes.

Step 2: Then, we implement a Dynamic Decision System Based on Learning Automata (DDSLA) technique to manage the data traffic.

Step 3: Next we implement Multi-Sink Load Balancing objective Function (MSLBOF-S-S) with a sink to sink framework to balance the load efficiently.

Step 4: Then we implement the DODAG Information Solicitation (DIS) message with Trickle fitted with Learning Automata (DIS-LA-T) to speed up the network convergence.

Step 5: Next we implement Support Vector Machine Improved Principle Component Analysis with Reinforcement-Learning (SVM-IPCA-RL-RAD) technique for Attack detection in the network.

Step 6: Then we implement the Secure RPL (Sec- RPL) to reduce the Packet Delivery Ratio.

Step 7: Finally we Plot the graph for the following:
7.1: No of malicious nodes Vs. Packet Delivery Ratio (%)
7.2: Simulation time (in min) Vs. Delay (ms)
7.3: No. of nodes Vs. Throughput (Mbps)
7.4: Time (in min) Vs. Energy (in joules)
7.5: No. of Nodes Vs. Convergence time (sec)
7.6: No of Nodes VS. Energy Consumption (in Joules)

Software Requirements:
———————————-
1) Development Tool: Contiki-2.7 (Or Above versions) with Cooja

2) Operating System: Ubuntu 14.04 (Or Above versions)

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) This project is only based on simulations. Not a real time project.

4) If the above plan satisfies your requirement please confirm with us.

We will implement this project based on the Existing Paper Reference-1 : Title:- RI-RPL: a new high quality RPLbased routing protocol using Q-learning algorithm

Related Pages