| dc.description.abstract | Wireless 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. | |
