In Wireless Sensor Networks (WSNs), the data transmission from the sensing node to the sink node consumes a lot of energy as the number of communications increases, so the battery life of nodes is limited, and the network also has a limited lifetime. Recent studies show that the bio-inspired meta-heuristic algorithms for solving engineering problems such as energy reduction in autonomous networks in the multidisciplinary areas of WSN, Internet of Things (IoT) and Machine learning models. Hence to increase Network lifetime, optimized clustering and energy-efficient routing techniques are required. In all applications of WSN, not only energy-efficient but also delay and throughput of the network are important for efficient transmission of data to the destination. This paper analyses optimized clustering by selecting cluster heads based on fractional calculus earthworm optimization algorithm (FEWA). The route from cluster heads to sink node is selected based on the fit factor. This paper's main intention is to provide an extensive comparative study of the FEWA with all standard optimization-based clustering and routing techniques. This method's performance is compared with existing optimized clustering methods like GA, PSO, ACO, DE and EWO in terms of the number of energy, delay, and throughput. At the end of 1000 iterations, the analysis shows that the FEWA outperforms existing methods with maximum average remaining energy of the nodes as 0.216J, the minimum average delay of 0.208 sec and maximum average throughput of 88.57% for 100 nodes.
The most dominant applications of wireless sensor networks (WSNs) is Environmental monitoring, it generally needs long time to operate. Although, the energy of inherent restriction has the bottle neck in scale of each WSN applications. This articler demonstrates the framework for an integration of compressive sensing and blocks tri-diagonal matrices (BDMs) for the clustering in WSNs that can be used as the matrices of measurement by the combination of data prediction that is involved with the compression and retrieval to achieve data processing precision and effectiveness in clustered WSNs simultaneously. On basis of the analysis theoretically, this can be designed for the implementation in number of algorithms. The proposed framework furnishes the real world data demonstration which can be utilized to get the simulation results for a solution of cost effective for the applications on basis of cluster in WSNs for environmental monitoring
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