GENETIC ALGORITHMS (GA) AND BACTERIAL CONJUGATION (BC) FOR DEVELOPING SOPHISTICATED CLUSTERING ALGORITHMS

Abstract

Mobile wireless sensor networks (MWSNs) have emerged as a promising technology for various applications, including environmental monitoring, disaster management, and healthcare. However, the efficient clustering of sensors in Mobile wireless sensor networks (MWSNs) remains a challenging task due to the dynamic and heterogeneous nature of these networks. To address this challenge, researchers have explored the use of bio-inspired optimization techniques such as genetic algorithms (GA) and bacterial conjugation (BC) as clustering strategies. This article provides a comprehensive review of the use of Genetic algorithms (GA) and bacterial conjugation (BC) in clustering algorithms for mobile wireless sensor networks (MWSNs). Genetic algorithms (GA), an artificial intelligence based optimization technique, mimics natural selection and genetic evolution to find optimal solutions. Bacterial conjugation (BC), on the other hand, simulates the exchange of genetic material between bacteria to optimize the clustering process. Both techniques have been shown to be effective in addressing issues such as energy efficiency, load balancing, and network scalability in mobile wireless sensor networks (MWSNs). The article discusses the advantages and differences of these two techniques in the context of clustering algorithms for mobile wireless sensor networks (MWSNs). Genetic algorithms (GA)-based algorithms are suitable for optimizing multiple objectives simultaneously and provide a better trade-off between conflicting objectives. However, they are computationally expensive due to the large population size. BC-based algorithms, on the other hand, are less computationally expensive as they use a smaller population size. They are also distributed in nature and maintain network connectivity even when nodes fail. The article highlights the potential of combining Genetic algorithms (GA) and bacterial conjugation (BC) to develop more sophisticated clustering algorithms that efficiently handle the dynamic and heterogeneous nature of mobile wireless sensor networks (MWSNs). These algorithms could improve the overall performance of mobile wireless sensor networks (MWSNs) by addressing issues such as energy efficiency, load balancing, and fault tolerance. Keywords: Genetic algorithm, bacterial conjugation, clustering, wireless sensor network, optimization, Mobile wireless sensor networks (MWSNs), cloud-based system, Sensor Cloud Model.