Bio-inspired dual cluster heads optimized routing algorithm for wireless sensor networks

The optimal usage of scarce and inadequate resources is the current need of wireless sensor networks (WSNs). Reducing power consumption and increasing WSN's lifetime can contribute to this. Energy-efficient algorithms should be proposed for effective routing, as a lot of energy is consumed during the communication between sensor nodes. A principal practice followed to bring down energy consumption is, combining together in-network data aggregation and standardized routing. Numerous conventional data aggregation algorithms exist, and each of these have their own disadvantages like delayed delivery of packets, time constraints, or high costs. The most suitable solution can therefore be achieved via optimization alone. The current research seeks to address these challenges faced in the wireless network by using the dual-cluster heads technique based on Krill Herd Optimisation (DC-KHO) Routing algorithm. Since the existing wireless routing approaches choose a random path for data transmission, the end- to- end delay, which is directly proportional to energy consumption, is high. So, the current study chooses an optimized path by calculating its path trust value using the devised krill herd maximization algorithm. The proposed DC-KHO algorithm is energy- efficient, and invariably amplifies the network's lifetime. The study's results reveal the ability of the proposed method in overcoming the challenges faced in transmission time, residual energy and computational time. Further, the method increases the life span of the network by 64.58%, when a 9 V battery is used for the nodes.

Automated summary

The optimal usage of scarce and inadequate resources in wireless sensor networks is necessary, with a focus on reducing power consumption and increasing WSN's lifetime. Efficient energy algorithms are proposed for effective routing, combining in-network data aggregation and standardized routing. The DC-KHO Routing algorithm aims to overcome challenges in transmission time, residual energy, and computational time, leading to a significant increase in the network's lifespan.