Routing and clustering based energy optimization techniques in 5G networks for improving the performance and lifetime
Abstract
The 5G networks are the integral components of Wireless sensor networks also known as WSNs, providing critical data collection and monitoring capabilities. However, the limited energy resources of sensor nodes pose a significant challenge in maintaining the network’s efficiency and longevity. To address this challenge, routing and clustering techniques have been extensively studied and employed to optimize energy consumption in WSNs. In the context of 5G networks, these techniques are tailored to improve the lifetime and overall performance of the network. Routing and clustering techniques in WSNs can be adapted and optimized for 5G networks to enhance energy efficiency and extend the network lifetime. The selection of specific techniques depends on the network characteristics, application requirements, and available resources. Ongoing research and development in this field aim to further enhance energy optimization techniques for the evolving needs of 5G networks. WSNs consist of numerous devices called sensor nodes which communicate with each other. Their main function is to sense the factors of the environment in which they have been deployed. The wireless communication techniques are utilized appropriately by the sensor nodes to communicate with each other. These techniques are governed by routing protocols based on the clustering process. Clustering is implemented using a new technique that is based on the social behavior of the network. Based on the above proposal, clusters are formed and these clusters are divided into two groups analogous to male and female herds. In the male group, the cluster head changes after every round of communication whereas in the female group, cluster heads continue until their energy is not drained. This results in improving the lifetime of networks resulting in a long time of communication as the nodes remain alive for a longer amount of time and clusters also continue for a longer period of time. In comparison with the existing techniques, this method based on Routing and Clustering based optimization gives better results in terms of lifetime, energy consumption, and the number of alive nodes. Also, energy consumption reduces substantially resulting in an optimal network.
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DOI: https://doi.org/10.32629/jai.v7i5.1134
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