The pervasiveness of wireless sensor networks has made them appropriate for a wide range of critical applications, including environmental surveillance, health monitoring using implantable sensors, weather forecasting, and a variety of other scenarios. Due to the hundreds of thousands of tiny sensor nodes that exist in the networks, key challenges such as computation time, limited memory, and energy are more widespread. In this case, the network's lifespan is entirely dependent on how well available resources are used. The technique of grouping sensor nodes that are close together into clusters makes it easier to administer the cluster and extend the network's lifespan. Swarm intelligence and evolutionary algorithms that pertain to the NP-complete problem are determined at this point to achieve a superior optimal solution. For obtaining extended network lifetime in sensor networks, a Hybrid Artificial Bee Colony and Bacterial Foraging Algorithm-based Optimized Clustering (HABC-BFA-OC) is proposed in this research. The benefits of Bacterial Foraging Optimization are integrated in this suggested HABC-BFA-OC technique for boosting the local search potential of the ABC algorithm in order to achieve maximal exploitation and exploration across the parameters evaluated for cluster head selection. During its examination with a varying number of sensor nodes, simulation studies of the suggested HABC-BFA-OC technique confirmed an extended network lifetime with minimal energy consumptions.
Author(S) Details
Bandi Rambabu
CSE, CVR College of Engineering, India.
View Book:- https://stm.bookpi.org/NAER-V12/article/view/3842
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