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PhD in WSN Data Dissemination Using Machine Learning.
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PhD in Wireless Sensor Network Data Dissemination Using Machine Learning.
Wireless sensor network data dissemination using machine learning is one of the very interesting issues since the current model has multiple limitations. Usually, data dissemination is performed from sensor nodes to the sink in which each of the sensor nodes is directed to move in a forward direction towards the sink.
The proposed architecture is designed to offer faster conduction of the Wireless Sensor Network Data Dissemination using Machine Learning which can further support the comprehensive decision-making will of the network. It is also meant to resolve traditional issues with features like fault-tolerance and data integrity with data centric and distributed storage, besides enhanced energy efficiency.
The proposed model is structured on the idea of the cellular framework in which the data is stored in a base location which is positioned at the center of the cell. For disseminating the data, action and relay stations also known as ARSs are used.
ISSUES IN THE EXISTING TECHNOLOGY
In the existing technology, there is a challenge of limited energy availability as the nodes cannot be recharged. The timeline of the entire structure depends upon the energy level of the clustered nodes.
Secondly, there is a great challenge to offer effective and automatic action for a timely approach. Moreover, there is a need to terminate or reduce the data availability issue in order to witness an effective and timely response. Another drawback is that the sensor data behaves differently in many ways from the data. WSNs need a mechanism so that the data must be stored in a decentralized manner.
PROPOSED SOLUTION
In this architecture, a centralized clustering algorithm has been used for smaller areas that can help in making the model energy efficient and will ensure well dispersion of CH nodes along with an increased rate of the clustering process. Its results are then compared using the LEACH-C. In the large area, the clustering is done on many levels and the outcome values are then compared with the HEED algorithm.
CH of various clusters is manipulated to maintain the battery level. Also, in this model, the data travels from a lower layer to the higher layer. In this, cellular technology is used to solve the issue of fault-tolerant operation.
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