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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| Published in: | Materials today : proceedings Vol. 80; pp. 3511 - 3516 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
2023
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| ISSN: | 2214-7853, 2214-7853 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Murali Krishna, K. Kusumadevi, G.H. Gite, Pratik Dilip, R. Shrivastava, Anurag Manohar Potdar, Ravindra |
| Author_xml | – sequence: 1 givenname: Pratik surname: Gite fullname: Gite, Pratik email: pratikgite135@gmail.com organization: Department of Computer Science and Engineering, IPSA-IES, Indore, India – sequence: 2 givenname: Anurag surname: Shrivastava fullname: Shrivastava, Anurag organization: Principal and Professor(ECE), Lakshmi Narain College of Technology and Science Indore, Madhya Pradesh, India – sequence: 3 givenname: K. surname: Murali Krishna fullname: Murali Krishna, K. organization: Department of Electronics and Communication Engineering, Vignan's Institute of Information Technology, Duvvada, Visakhapatnam, Andhra Pradesh, India – sequence: 4 givenname: G.H. surname: Kusumadevi fullname: Kusumadevi, G.H. organization: Department of Electrical and Electronics Engineering, Acharya Institute of Technology, Bangalore 560107, India – sequence: 5 givenname: R. surname: Dilip fullname: Dilip, R. organization: Department of Mechatronics Engineering, Acharya Institute of Technology, Bengaluru, Karnataka, 560107, India – sequence: 6 givenname: Ravindra surname: Manohar Potdar fullname: Manohar Potdar, Ravindra organization: Department Of Electronics & Telecommunication Engieering, Bhilai Institute Of Technology, Durg, Chhattisgarh, India |
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| Keywords | Wireless sensor network Localization algorithm Underwater sensor deployment Predictive algorithm Underwater sensor network Motion tracking and prediction |
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