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A Novel Cross-Layer Cross-Domain Routing Model and It is Optimization for Cluster-Based Dense WSN - 2021

A Novel Cross-Layer Cross-Domain Routing Model and It is Optimization for Cluster-Based Dense WSN

Research Area:  Wireless Sensor Networks


Wireless Sensor Networks (WSNs) plays its adorable performance in the current day communication as it could sense different environmental and physical parameters by utilizing low-cost sensor devices. The networks growth due to scientific enrichment has altogether made it feasible to design a cross-layer protocol based on the energy-efficient network. This obviously concerns the prolonging of network lifetime. This research work attempts to introduce a novel Cross-Layer Design Routing model under the clustering approach. The implemented work depends on a cross-layer mechanism via diverse layers (comprising physical layer and network layer). A cluster-based routing is introduced, where the optimal cluster head is selected using a new hybrid algorithm named Alpha Wolf-assisted Whale Optimization Algorithm (AW-WOA). Thereby, the shortest path is defined and ensures the prolonging of network lifetime. The proposed hybrid algorithm is the hybridized form of the Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO). Moreover, the optimal cluster head selection is purely based on certain constraints like energy consumption, delay, and distance, respectively. In the end, the performance of the implemented technique is proved over other conventional approaches with regards to the alive node and network lifetime. In the alive node analysis of supernode, on considering the 1st test case, the presented AW-WOA model at 2000 rounds accomplishes 100% alive node than other existing models, wherein GWO and ATEER attain 91.67% and 75%, the other WOA, PSO, and AWOA ATEER attain 83.33% of alive nodes respectively.


Author(s) Name:  Shivaji R. Lahane,Krupa N. Jariwala

Journal name:  Wireless Personal Communications

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s11277-021-08154-3

Volume Information:  volume 118, pages 2765–2784 (2021)