Open AccessA RADIAL BASIS NEURAL NETWORK CONTROLLER TO SOLVE CONGESTION IN WIRELESS SENSOR NETWORKS
Author(s) -
Maab Alaa Hussain
Publication year - 2018
Publication title -
al-mağallaẗ al-ʿirāqiyyaẗ li-l-ḥāsibāt wa-al-maʿlūmāt/iraqi journal for computers and informatics
Language(s) - English
Resource type - Journals
eISSN - 2520-4912
pISSN - 2313-190X
DOI - 10.25195/ijci.v44i1.103
Subject(s) - computer science , network congestion , computer network , artificial neural network , controller (irrigation) , network traffic control , node (physics) , real time computing , wireless sensor network , artificial intelligence , network packet , engineering , structural engineering , agronomy , biology
In multihop networks, such as the Internet and the Mobile Ad-hoc Networks, routing is one of the most importantissues that has an important effect on the network’s performance. This work explores the possibility of using the shortest path routingin wireless sensor network . An ideal routing algorithm should combat to find an perfect path for data that transmitted within anexact time. First an overview of shortest path algorithm is given. Then a congestion estimation algorithm based on multilayerperceptron 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 trafficcongestion along the selected route. A comparison study between the network with and without controller in terms of: trafficreceived to the base station, execution time, data lost, and memory utilization . The result clearly shows the effectiveness of RadialBasis Neural Network Congestion Controller (RBNNCC) in traffic congestion prediction and control.