Please use this identifier to cite or link to this item: http://drsr.daiict.ac.in//handle/123456789/923
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dc.contributor.advisorSasidhar, P S Kalyan
dc.contributor.authorPatel, Charmy Bharatbhai
dc.date.accessioned2020-09-22T14:24:20Z
dc.date.available2023-02-16T14:24:20Z
dc.date.issued2020
dc.identifier.citationPatel, Charmy Bharatbhai (2020). WSN Network Analysis and Prediction using ML. Dhirubhai Ambani Institute of Information and Communication Technology. vii, 29 p. (Acc.No: T00845)
dc.identifier.urihttp://drsr.daiict.ac.in//handle/123456789/923
dc.description.abstractWireless sensor networks are a group of sensors that monitor and record the physical changes of the environment that change rapidly over time. This ability of WSNs help in various fields ranging from the engineering industry to immediate home environments. A sensor node is capable of performing some processing, gathering sensory information, and communicating with other connected nodes in the Network. To make some decisions in this Network, sensors adopt machine learning algorithms. The main aim of this project is to find out the parameters which can increase throughput and decrease the Delay in our Network. Different network sizes have been taken into consideration to find the parameter changes required to meet our above objective. This research project mainly includes the data collection phase observing a network and learning phase. It is simulated for different network scenarios. Three types of machine learning algorithms have been applied: Linear Regression, Neural Network, and Random Forest. By applying these algorithms, we get to know that RandomForest overfits the model, whereas Neural-Network underfits the model because they are non-linear algorithms. Hence we can say that it is showing linear behavior as non-linear algorithms like Neural-Network and RandomForest didn’t help us to estimate the throughput and delay in our Network, and hence they are not suitable.
dc.subjectWSN
dc.subjectIoT
dc.subjectThroughput
dc.subjectDelay
dc.subjectLinear Regression
dc.subjectRandom Forest
dc.subjectNeural Network
dc.classification.ddc621.384 PAT
dc.titleWSN Network Analysis and Prediction using ML
dc.typeDissertation
dc.degreeM. Tech
dc.student.id201811013
dc.accession.numberT00845
Appears in Collections:M Tech Dissertations

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