Trilateration and Multiverse Optimization-Based 3D Localization for Underwater Wireless Sensor Networks in Shadow Environment.
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| Title: | Trilateration and Multiverse Optimization-Based 3D Localization for Underwater Wireless Sensor Networks in Shadow Environment. |
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| Authors: | Rani, Seema1 (AUTHOR) seema.singroha@gmail.com, Sangwan, Anju1 (AUTHOR) anju.sangwan@yahoo.com |
| Source: | Wireless Personal Communications. Jul2025, Vol. 143 Issue 1/2, p1-34. 34p. |
| Subjects: | Localization theory, Optimization algorithms, Mathematical optimization, Triangulation, Underwater acoustic communication |
| Abstract: | Localization is one prime invention in Underwater Wireless Sensor Networks (UWSNs) as it is essential in many applications. Sensors deployed beneath water detect underwater events and convey the detected data to the base station. This data becomes valuable only when the exact location of the object is known. For localization, GPS (Global Positioning System) signals do not spread across water due to the different environmental conditions. For that reason, calculating the location of the nodes should be done using another GPS-less scheme. However, these schemes have high communication costs and their efficacy is affected by propagation delays, attenuation, multipath interference, shadowing etc. All these issues make it necessary to develop a novel localization scheme. The paper presents a novel UWSNs localization method, merging trilateration and Multiverse Optimization (MVO), while considering shadowing effects. MVO is a nature-inspired optimization algorithm that simulates the concept of multiple universes and their interactions. It introduces a level of diversity and adaptability that might make it advantageous in certain situations, including underwater localization. This scheme initially employs trilateration to estimate the sensor node's location, followed by refining its position through error minimization using MVO. The network is simulated, then error rate and accuracy of the proposed localization method is evaluated. Results from the MVO optimization scenario are anticipated to demonstrate enhanced communication reliability, reduced error rates, potentially decreased time consumption, and lower average energy consumption compared to non-optimization, GA(Genetic Algorithm), PSO(Particle Swarm Optimization), COA(Cuckoo Optimization Algorithm) scenarios. Moreover, error rate and accuracy are simulated considering the shadowing factor in the case of conventional approach, GA, PSO, COA and compared it to the MVO. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Localization is one prime invention in Underwater Wireless Sensor Networks (UWSNs) as it is essential in many applications. Sensors deployed beneath water detect underwater events and convey the detected data to the base station. This data becomes valuable only when the exact location of the object is known. For localization, GPS (Global Positioning System) signals do not spread across water due to the different environmental conditions. For that reason, calculating the location of the nodes should be done using another GPS-less scheme. However, these schemes have high communication costs and their efficacy is affected by propagation delays, attenuation, multipath interference, shadowing etc. All these issues make it necessary to develop a novel localization scheme. The paper presents a novel UWSNs localization method, merging trilateration and Multiverse Optimization (MVO), while considering shadowing effects. MVO is a nature-inspired optimization algorithm that simulates the concept of multiple universes and their interactions. It introduces a level of diversity and adaptability that might make it advantageous in certain situations, including underwater localization. This scheme initially employs trilateration to estimate the sensor node's location, followed by refining its position through error minimization using MVO. The network is simulated, then error rate and accuracy of the proposed localization method is evaluated. Results from the MVO optimization scenario are anticipated to demonstrate enhanced communication reliability, reduced error rates, potentially decreased time consumption, and lower average energy consumption compared to non-optimization, GA(Genetic Algorithm), PSO(Particle Swarm Optimization), COA(Cuckoo Optimization Algorithm) scenarios. Moreover, error rate and accuracy are simulated considering the shadowing factor in the case of conventional approach, GA, PSO, COA and compared it to the MVO. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 09296212 |
| DOI: | 10.1007/s11277-025-11816-1 |
