Cluster Based Dense using Hybrid Genetic and Grasshopper Optimization algorithm in WSN

Cluster Based Dense using Hybrid Genetic and Grasshopper Optimization algorithm in WSN


  • K.Srinivas Professor, Department of Computer Science and Engineering, Geethanjali College of Engineering and Technology


Delay, Distance, Energy, Lifetime, Network, WSN


Nowadays, Wireless Sensor Networks (WSNs) play an important role in the communication due to their performance in diverse physical and environmental parameters by using minimum cost sensor devices. Owing to the scientific enhancement, the development of networks is practicable to model the cross-layer protocol on the basis of the energy effectual network. This clearly distresses the extending the lifespan of the network. Hence, this paper tries to develop a new Cross-Layer Design Routing technique in the clustering approach.The developed technique is based upon the cross-layer model through different layers such as physical and network layers. Here, a cluster-based routing model is developed, therefore, an optimal cluster head can be performed by exploiting a novel optimization algorithm named Hybrid Genetic Algorithm (GA) and Grasshopper Optimization Algorithm (GOA). Hence, the extension of the network lifetime can be attained by defining the shortest path. Additionally, based on numerous constraints such as energy utilization, distance, and delay, the optimal cluster head selection is performed. At last, the adopted model performance is confirmed with the existing techniques regarding the lifetime of the network and alive node.


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How to Cite

K.Srinivas. (2021). Cluster Based Dense using Hybrid Genetic and Grasshopper Optimization algorithm in WSN: Cluster Based Dense using Hybrid Genetic and Grasshopper Optimization algorithm in WSN. Journal of Networking and Communication Systems, 4(3). Retrieved from