A RADIAL BASIS NEURAL NETWORK CONTROLLER TO SOLVE CONGESTION IN WIRELESS SENSOR NETWORKS
DOI:
https://doi.org/10.25195/ijci.v44i1.103Keywords:
Radial Basis, Congestion, Controller, and memory utilizationthings.Abstract
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.
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