Improving the Lifetime of Wireless Sensor Networks for Air Quality Monitoring Using Metaheuristic Algorithms

Document Type : Original Research Paper

Authors

1 Department of Environmental Engineering, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

2 Department of Mechanical Engineering, Payame Noor University, Tehran, Iran

10.22059/poll.2024.380231.2490

Abstract

Wireless sensor networks (WSNs) are crucial for environmental monitoring, particularly for assessing air quality. However, optimizing energy consumption remains a significant challenge due to the limited energy resources of the sensor nodes, which adversely affects the network's performance and lifespan. This study aims to enhance the longevity and efficiency of WSNs by implementing metaheuristic algorithms, specifically Ant Lion Optimization (ALO) and Cheetah Optimization (CO), for effective energy management through clustering strategies. Utilizing simulations, we compared the performance of ALO against CO in terms of energy efficiency, network lifespan, and resilience within heterogeneous network conditions. The results indicate that ALO optimizes data transmission by reducing network traffic through efficient cluster communication. Additionally, ALO's scalability enables the network to adapt to changing sensor deployments, while data aggregation at the cluster head level further minimizes energy consumption. This load balancing ensures a more even distribution of energy usage, further ALO outperforms CO by extending network lifespan, improving energy management, and providing better scalability. The findings suggest that ALO is a robust approach for optimizing clustering and energy consumption in WSNs.

Keywords

Main Subjects

References

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Pollution
Volume 11, Issue 2
February 2025
Pages 425-439

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