CLUSTER OPTIMIZATION IN WIRELESS SENSOR NETWORK USING GENETIC ALGORITHM AND BACTERIAL CONJUGATION
Genetic Algorithm, Bacterial Conjugation, Clustering, Mobile wireless sensor network, optimization.
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 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 GA and BC in clustering algorithms for MWSNs. GA is a population-based optimization technique that mimics natural selection and genetic evolution to find optimal solution. 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 MWSNs. The article discusses the advantages and differences of these two techniques in the context of clustering algorithms for MWSNs. 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 GA and BC to develop more sophisticated clustering algorithms that efficiently handle the dynamic and heterogeneous nature of MWSNs. These algorithms could improve the overall performance of MWSNs by addressing issues such as energy efficiency, load balancing, and fault tolerance. In conclusion, GA and BC are promising optimization strategies for clustering in MWSNs. The article provides insights into the advantages and differences of these two techniques and highlights their potential for future development in the field of clustering algorithms for MWSNs. This research has implications for improving the efficiency and effectiveness of MWSNs for various applications, including environmental monitoring, disaster management, and healthcare.
"CLUSTER OPTIMIZATION IN WIRELESS SENSOR NETWORK USING GENETIC ALGORITHM AND BACTERIAL CONJUGATION", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 8, page no.20 - 30, August-2023, Available :https://ijsdr.org/papers/IJSDR2308004.pdf
Volume 8
Issue 8,
August-2023
Pages : 20 - 30
Paper Reg. ID: IJSDR_208020
Published Paper Id: IJSDR2308004
Downloads: 000347194
Research Area: Electronics & Communication Engg.
Country: sagar, madhya pradesh, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
