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DC Field | Value | Language |
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dc.contributor.author | Prajose, Anushka | - |
dc.date.accessioned | 2022-09-08T08:22:13Z | - |
dc.date.available | 2022-09-08T08:22:13Z | - |
dc.date.issued | 2022-06-01 | - |
dc.identifier.uri | http://10.1.7.192:80/jspui/handle/123456789/11248 | - |
dc.description.abstract | n Underwater Wireless Sensor Networks (UWSNs), the sensors perform a variety of tasks for tracking and detection. These tasks have high energy consumption mainly because of the lengthy data packet transmission time which requires a large amount of transmission power. In order to optimize the network lifetime and improve the stability of the network, it is necessary to have an appropriate selection of cluster heads, cluster size, and proper routing. The proposed protocol is designed for routing in a three-dimensional (3D) underwater sensor network. The concept of clustering is used to balance the energy consumption of the sensor network. The Cluster Head(CH) selection is done based on fuzzy logic and multi-path routing is implemented for intra-cluster communication. In this approach, the sinks are mobile to balance the load on all nodes. The nodes closer to the sink die early in the case of static sink nodes as they forward more packets and have an unbalanced node. Hence, by using sink mobility, clustering, and utilizing the fuzzy interference system, balanced energy consumption can be achieved in the network. This will result in improved network stability and network lifetime. | en_US |
dc.publisher | Institute of Technology | en_US |
dc.relation.ispartofseries | 20MECE02; | - |
dc.subject | EC 2020 | en_US |
dc.subject | Project Report 2020 | en_US |
dc.subject | EC Project Report | en_US |
dc.subject | EC (ES) | en_US |
dc.subject | Embedded Systems | en_US |
dc.subject | Embedded Systems 2020 | en_US |
dc.subject | 20MEC | en_US |
dc.subject | 20MECE | en_US |
dc.subject | 20MECE02 | en_US |
dc.title | Fuzzy Logic Based Energy Efficient Clustering and Multi-Hop Routing Protocol for Underwater Wireless Sensor Network | en_US |
dc.type | Dissertation | en_US |
Appears in Collections: | Dissertation, EC (ES) |
Files in This Item:
File | Description | Size | Format | |
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20MECE02.pdf | 20MECE02 | 1.52 MB | Adobe PDF | ![]() View/Open |
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