COMPARITIVE ANALYSIS OF DIMENSIONALITY REDUCTION TECHNIQUES FOR COMPRESSION OF REAL-WORLD SENSOR DATA
Budget: ₹1,500 – ₹12,500 INR
Problem Statement:
Wireless sensor networks generate large volumes of real-world sensor data, which poses challenges in terms of memory consumption and communication bandwidth during transmission between edge devices and the cloud. Therefore, data compression is instrumental in reducing memory consumption and communication bandwidth as far as transmission between edge and cloud is concerned.
Solution Idea:
To address this issue, effective compression techniques are needed. Specifically, there is a need for a comparative analysis of dimensionality reduction techniques, focusing on feature extraction and feature selection methods, to identify the most suitable approaches for compressing real-world sensor data.
By conducting this comparative analysis, the project aims to identify and evaluate the most effective dimensionality reduction techniques utilizing feature extraction and feature selection for compressing real-world sensor data. The analysis will provide valuable insights into the trade-offs and performance characteristics of different approaches, aiding in the selection of the most appropriate technique for compression in real-world sensor networks.
Wireless sensor networks generate large volumes of real-world sensor data, which poses challenges in terms of memory consumption and communication bandwidth during transmission between edge devices and the cloud. Therefore, data compression is instrumental in reducing memory consumption and communication bandwidth as far as transmission between edge and cloud is concerned.
Solution Idea:
To address this issue, effective compression techniques are needed. Specifically, there is a need for a comparative analysis of dimensionality reduction techniques, focusing on feature extraction and feature selection methods, to identify the most suitable approaches for compressing real-world sensor data.
By conducting this comparative analysis, the project aims to identify and evaluate the most effective dimensionality reduction techniques utilizing feature extraction and feature selection for compressing real-world sensor data. The analysis will provide valuable insights into the trade-offs and performance characteristics of different approaches, aiding in the selection of the most appropriate technique for compression in real-world sensor networks.