Optimization of Distributed Computing Algorithms for Real-Time Sensor Data Processing
Table Of Contents
<p>1. Introduction<br> - IoT and Sensor Data Processing Challenges<br> - Objectives of the Project<br>2. Literature Review<br> - Distributed Computing Models<br> - Real-Time Data Processing Techniques<br> - IoT Sensor Data Characteristics<br>3. System Architecture and Design<br> - Data Partitioning and Distribution Strategies<br> - Parallel Processing and Task Scheduling<br> - Fault Tolerance and Scalability<br>4. Implementation Details<br> - Distributed Computing Framework Selection<br> - Algorithm Design and Implementation<br> - Integration with IoT Platforms<br>5. Evaluation and Testing<br> - Performance Metrics and Benchmarks<br> - Scalability and Responsiveness Analysis<br> - Real-World Deployment Scenarios<br></p>
Thesis Abstract
<p> This project focuses on the optimization of distributed computing algorithms for real-time processing of sensor data in large-scale IoT (Internet of Things) environments. The project aims to develop and implement efficient algorithms and protocols that can handle the high volume, velocity, and variety of sensor data generated by IoT devices. By leveraging parallel processing, data partitioning, and distributed computing frameworks, the project seeks to improve the scalability, responsiveness, and resource utilization of sensor data processing systems in IoT applications. <br></p>Thesis Overview
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