I.O Omoifo

Wireless Sensor Networks (WSNs) play a crucial role in modern communication systems, particularly in environmental monitoring, industrial automation, and smart cities. However, a major challenge in WSNs is optimizing energy consumption due to the limited power resourcesof sensor nodes. One of the most effective ways to enhance energy efficiency is through data compression, which reduces the amount of transmitted data while preserving essential information.
This project explores the integration of machine learning-based data compression techniques to improve energy-efficient transmission in WSNs. A hybrid approach is proposed, combining Run-Length Encoding (RLE) as a traditional lossless compression method with Principal Component Analysis (PCA) as a machine learning algorithm to reduce data redundancy while maintaining accuracy. The study focuses on temperature sensor datasets collected over a specified period, ensuring real-world applicability.
The methodology involves preprocessing raw temperature data, applying Run-Length Encoding (RLE) for initial redundancy reduction, and then leveraging PCA to extract principal components, further reducing data dimensions before transmission. The efficiency of the proposed model is evaluated based on key metrics such as compression ratio, reconstruction accuracy, and energy savings. Performance comparisons are made with conventional lossless compression algorithms like Huffman Coding and Arithmetic Coding to assess improvements.
Preliminary results indicate that the combined approach achieves a higher compression ratio while preserving critical temperature variations, leading to significant energy savings in wireless
transmissions. This work contributes to advancing energy-efficient data handling in WSNs, making it highly relevant for resource-constrained environments. Future research directions include expanding the model to handle multi-sensor data streams and implementing real-time adaptive compression strategies

Wireless Sensor Networks (WSNs) play a crucial role in modern communication systems, particularly in environmental monitoring, industrial automation, and smart cities. However, amajor challenge in WSNs is optimizing energy consumption due to the limited power resourcesof sensor nodes. One of the most effective ways to enhance energy efficiency is throughdatacompression, which reduces the amount of transmitted data while preserving essential
information. This project explores the integration of machine learning-based data compression techniquestoimprove energy-efficient transmission in WSNs. A hybrid approach is proposed, combiningRun-Length Encoding (RLE) as a traditional lossless compression method with Principal
Component Analysis (PCA) as a machine learning algorithm to reduce data redundancywhilemaintaining accuracy. The study focuses on temperature sensor datasets collectedover aspecified period, ensuring real-world applicability. The methodology involves preprocessing raw temperature data, applying Run-Length Encoding(RLE) for initial redundancy reduction, and then leveraging PCA to extract principal components, further reducing data dimensions before transmission. The efficiency of the proposed model isevaluated based on key metrics such as compression ratio, reconstruction accuracy, andenergysavings. Performance comparisons are made with conventional lossless compression algorithmslike Huffman Coding and Arithmetic Coding to assess improvements. Preliminary results indicate that the combined approach achieves a higher compressionratiowhile preserving critical temperature variations, leading to significant energy savings in wirelesstransmissions. This work contributes to advancing energy-efficient data handling inWSNs, making it highly relevant for resource-constrained environments. Future research directionsinclude expanding the model to handle multi-sensor data streams and implementing real-timeadaptive compression strategies.