Computational Intelligence-Based Optimization of Wireless Sensor Networks for Smart City Environmental Monitorin

Abstract

In this paper, a computational intelligence optimization methodology of the Wireless Sensor Networks (WSNs) in smart cities has been presented. The algorithm proposed can greatly enhance the main performance indicators: energy use, network life, and data accuracy. Specifically, the algorithm reduces energy consumption to 40 units per sensor, extends network lifetime to 300 hours, and achieves 95% data reliability. These findings indicate that the algorithm is effective in optimizing WSNs in real-time environmental monitoring, and it is thus very suitable for large-scale applications of smart cities. The proposed algorithm is compared to the existing methods of the algorithm, including greedy algorithms, genetic algorithms, and the standard PSO-based methods. The findings indicate that compared to these methods, the new method is more energy efficient, has better network scalability, and has more data reliability. Moreover, the scalability of the algorithm guarantees its use in big city settings, as it will be able to respond to new circumstances and surging data requirements. Optimization of WSNs can facilitate the utilization of resources in a more efficient way, thus saving costs and promoting better monitoring of the environment, which is essential in smart city applications. These findings indicate that the incorporation of computational intelligence in the smart city infrastructure is essential to optimize WSNs to enhance the management and sustainability of the city. It is possible to conduct future research in the field of hybrid optimization models and practical testing to confirm the simulated findings and improve performance.