Autonomous underwater vehicle: 5G network design and simulation based on mimetic technique control system

• Autores: Collantes Inga, Zoila Mercedes, Castro-Cayllahua, Fidel, Meza Carhuancho, Juan Luis, Fernández Díaz, Carlos Mario, Rasheed, Tariq, Cotrina-Aliaga, Juan Carlos
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/6531
• Enlace del recurso: https://hdl.handle.net/20.500.12867/6531; https://doi.org/10.17762/ijcnis.v14i3.5566
• Nivel de acceso: acceso abierto
• Materia: Autonomous underwater vehicle; Wireless communication; Memetic algorithm; https://purl.org/pe-repo/ocde/ford#1.02.01

The Internet of Underwater Things (IoUT) exhibits promising advancement with underwater acoustic wireless network communication (UWSN). Conventionally, IoUT has been utilized for the offshore monitoring and exploration of the environment within the underwater region. The data exchange between the IoUT has been performed with the 5G enabled-communication to establish the connection with the futuristic underwater monitoring. However, the acoustic waves in underwater communication are subjected to longer propagation delay andhigher transmission energy. To overcome those issues autonomous underwater vehicle (AUV) is implemented for the data collection and routing based on cluster formation. This paper developed a memeticalgorithm-basedAUV monitoring system for the underwaterenvironment. The proposed Autonomous 5G Memetic (A5GMEMETIC) model performs the data collection and transmission to increase the USAN performance. The A5GMEMETIC model data collection through the dynamic unaware clustering model minimizes energy consumption. The A5GMemetic optimizes the location of the nodes in the underwater environment for the optimal data path estimation for the data transmission in the network. Simulation analysis is performed comparatively with the proposed A5Gmemetic with the conventional AEDG, DGS, and HAMA models. The comparative analysis expressed that the proposed A5GMeMEMETIC model exhibits the ~12% increased packet delivery ratio (PDR), ~9% reduced delay and ~8% improved network lifetime.