Performance analysis of TDMA Scheduling Algorithms in WSN

Title:- Energy-aware TDMA based Scheduling with Effective data communication in Wireless Sensor Network
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Implementation Plan:
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Step 1: Initially, we construct a WSN network consisting of 100- IOT Users, 5-Base stations.
Step 2: Next, we Implement the fault-tolerant clustering algorithm with optimal Cluster Head (CH) selection based on greatest weight values.
Step 3: Next, we Implement the Firefly Optimization with Feed-Forward Multi-Clustering Protocol (FO-FFMCP) for clustering in WSNs.
Step 4: Next, we implement the Correlated Multi Objective Markov Decision Process (CMOMDP) combined with EA-TDMA for Energy-Aware Scheduling
Step 5: Next, we Implement the A-star search method combined with the Emperor Penguin Optimisation (EPO) algorithm to improve routing and packet delivery.
Step 6: Finally, Performance Metrics will be plotted for the following:
6.1: No of users Vs. Energy Consumption
6.2: No of users Vs. Network Lifetime
6.3: No of users Vs. Number of Alive Nodes
6.4: No of users Vs. Throughput
6.5: No of users Vs. Packet Delivery Ratio
6.6: No of Users Vs. Delay
Software Requirement:
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1. Development Tool : Ns3.35 with Python
2. Development OS : Ubuntu 22.04 LTS
Note:
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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.
EXISTING:
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Title: Reinforcement-Learning-Based Energy Efficient Control and Routing Protocol for Wireless Sensor Networks
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1. #TDMAperformance
2. #WSNanalysis
3. #SchedulingAlgorithms
4. #WirelessPerformance
5. #NetworkAnalysis
6. #TDMAWSN
7. #AlgorithmEvaluation
8. #WirelessScheduling
9. #WSNperformance
10. #TDMAstudy
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