Under water motion tracking and monitoring using wireless sensor network and Machine learning

Wireless Sensor Networks (WSNs) plays an indispensable role in different application and development in new generation technology. In 5G technology, one of the essential features is the connectivity with registered and unregistered networks to promote remote automation. By which the technology can b...

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Bibliographic Details
Published in:Materials today : proceedings Vol. 80; pp. 3511 - 3516
Main Authors: Gite, Pratik, Shrivastava, Anurag, Murali Krishna, K., Kusumadevi, G.H., Dilip, R., Manohar Potdar, Ravindra
Format: Journal Article
Language:English
Published: Elsevier Ltd 2023
Subjects:
Localization algorithm
Motion tracking and prediction
Predictive algorithm
Underwater sensor deployment
Underwater sensor network
Wireless sensor network
Wireless sensor network
Localization algorithm
Underwater sensor deployment
Predictive algorithm
Underwater sensor network
Motion tracking and prediction
ISSN:2214-7853, 2214-7853
Online Access:Get full text
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Summary:Wireless Sensor Networks (WSNs) plays an indispensable role in different application and development in new generation technology. In 5G technology, one of the essential features is the connectivity with registered and unregistered networks to promote remote automation. By which the technology can be harnessed at its best and scaled in multi-dimensional applications. These applications are also enabled for diverse motion detection and object tracking applications. In this presented work, an underwater object tracking technique has been proposed, which enable the prediction of the mobile sensor node. The method involves a simulation which demonstrates a 2D-UASN architecture of underwater scenario and in this network using a different number of nodes using random positioning static sensor nodes are deployed. Finally, a mobile sensor node with random mobility is introduced into the network. The approach follows statistical analysis to compute the next possible position of the node. Based on experiments, the accuracy of the predictive methodology is measured in two different scenarios. In the first scenario, the number of nodes is variable, and the simulation area kept fixed size, i.e., 1000X1000. Additionally, in the second scenario, the number of nodes remains fixed, and the simulation area is variable. The results demonstrate that when we deploy sensor nodes randomly in a large area and network node density is less, the detection accuracy significantly affects and demonstrates low accuracy compared to dense area networks. Secondly, the prediction trend from dense to less dense area demonstrates the reducing accuracy.
ISSN:2214-7853
2214-7853
DOI:10.1016/j.matpr.2021.07.283