Aquila Optimized Localization of Mobile nodes in Heterogeneous WSN with Reduced Complexity using MCL Square

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-4
Year of Publication : 2022
Authors : Swapna M P, G. Satyavathy
  10.14445/22315381/IJETT-V70I4P221

MLA 

MLA Style: Swapna, M P., and Satyavathy, G. "Aquila Optimized Localization of Mobile nodes in Heterogeneous WSN with Reduced Complexity using MCL Square." International Journal of Engineering Trends and Technology, vol. 70, no. 4, Apr. 2022, pp. 245-257. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I4P221

APA Style: Swapna, M P., & Satyavathy, G. (2022). Aquila Optimized Localization of Mobile nodes in Heterogeneous WSN with Reduced Complexity using MCL Square. International Journal of Engineering Trends and Technology, 70(4), 245-257. https://doi.org/10.14445/22315381/IJETT-V70I4P221

Abstract
Localization of mobile nodes in Heterogeneous Wireless Sensor Network (HSWN) requires more research and experiments. The majority of the localization protocols discuss locating the static nodes in the wireless network. This paper proposes the localization of mobile nodes in an HWSN, considering energy efficiency. The protocol Aquila Optimized Monte Carlo Localization(AOMCL) is a novel attempt to combine the mobile node localizing algorithm MCL and the new swarm intelligence algorithm, Aquila Optimizer. The protocol AOMCL reduces the sampling and filtering process of traditional MCL. AOMCL localizes the unknown nodes by generating an MCL square around the location-aware anchor nodes. The method efficiently reduces the time and complexity of localizing the unknown nodes. The experimental analysis of AOMCL in the Matlab simulator illustrates that the proposed protocol, AOMCL, has high localization accuracy, better localization coverage, and reduced complexity compared with the existing protocols, DEMCL, RMCL, and QMCL.

Keywords
Filtering, HWSN, Localization, Location Prediction, MCL Square, Sampling.

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