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Awatef Salem Balobaid¹, Saahira Banu Ahamed¹, Shermin Shamsudheen¹

¹Department of Computer Science, College of Computer Science & Information Technology, Jazan University, Jazan 45142, Saudi Arabia.

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Summary

This study introduces an efficient machine learning (ML) algorithm for wireless sensor networks (WSNs). The proposed ML-based clustering and routing enhances network performance and longevity.

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Area of Science:

Computer Science
Electrical Engineering
Network Security

Background:

Wireless Sensor Networks (WSNs) offer rapid deployment for critical applications like military operations and emergency response.
WSNs require robust routing and intrusion detection mechanisms due to their decentralized nature.
Limited energy and bandwidth in WSN nodes necessitate efficient data processing and transmission strategies.

Purpose of the Study:

To address limitations of existing WSN solutions, including poor reliability and short network lifespan.
To develop an efficient clustering and routing algorithm for WSNs utilizing machine learning (ML).
To improve data handling and energy efficiency in resource-constrained WSN environments.

Main Methods:

A novel machine learning-based algorithm was designed for clustering and routing in WSNs.
Simulations were conducted to evaluate the performance of the proposed algorithm against existing methods.
Key performance metrics such as accuracy, specificity, and sensitivity were analyzed.

Main Results:

The proposed ML-based algorithm demonstrated superior performance compared to state-of-the-art models.
Achieved high accuracy (0.93), specificity (0.93), and sensitivity (0.92) in simulations.
Indicated significant improvements in network efficiency and data management.

Conclusions:

The developed ML algorithm offers an effective solution for enhancing WSN performance.
The approach addresses critical challenges in WSNs, including energy constraints and data transmission.
This work contributes to more reliable and efficient WSNs for diverse applications.