A Radial Basis Neural Network Controller to Solve Congestion in Wireless Sensor Networks
Maab Alaa Hussain · Iraqi Journal for Computers and Informatics · 2018
In multihop networks, such as the Internet and the Mobile Ad-hoc Networks, routing is one of the most important issues that has an important effect on the network’s performance. This work explores the possibility of using the shortest path routing in wireless sensor network . An ideal routing algorithm should combat to find an perfect path for data that transmitted within an exact time. First an overview of shortest path algorithm is given. Then a congestion estimation algorithm based on multilayer perceptron neural networks (MLP-NNs) with sigmoid activation function, (Radial Basis Neural Network Congestion Controller (RBNNCC) )as a controller at the memory space of the base station node. The trained network model was used to estimate traffic congestion along the selected route. A comparison study between the network with and without controller in terms of: traffic received to the base station, execution time, data lost, and memory utilization . The result clearly shows the effectiveness of Radial Basis Neural Network Congestion Controller (RBNNCC) in traffic congestion prediction and control