Title: - Innovative Approaches to Parallel Processing in Constrained IoT Environments
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Implementation Plan:
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Step 1: Initially, we construct a network consisting of 50- IoT Devices, 2 - Base Station, 1-Cloud.

Step 2: Next, we perform Registration of Device data and Authentication of Devices using the Elliptic curve cryptography (ECC) for secure communication in the network.

Step 3: Next, we implement Improved differential evolution algorithm (IDEA) with Concurrent Federated Reinforcement Learning (CFRL) for resource allocation within the IoT Devices.

Step 4: Next, we implement Deep Reinforcement Learning (DRL) with Lyapunov optimization and the whale optimization algorithm (LOWOA) for the task offloading process in parallel computing.

Step 5: Next, we implement Deep Neural Network (DNN) for Parallel Data Processing.

Step 6: Next, we implement the Markov decision process (MDP) for effective routing devices in the network.

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

7.1: Number of tasks vs. communication overhead (%)
7.2: Number of tasks vs. response time (ms)
7.3: Number of iteration vs. Accuracy (%)
7.4: Number of tasks vs. Latency(s)

Software Requirement:
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1. Development Tool: Ns-3.35 with python

2. Operating System: 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) If the above plan satisfies your requirement please confirm with us.

4) We develop simulation based projects only, not in real time.

We implement an existing project

Reference 2: Title:- Joint Task Partitioning and Parallel Scheduling in Device-Assisted Mobile Edge Networks
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1. #IoTPerformance
2. #ParallelProcessing
3. #DataAnalysis
4. #SmartDevices
5. #TechTrends
6. #InternetOfThings
7. #PerformanceMetrics
8. #BigData
9. #CloudComputing
10. #InnovationInTech
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