Improving QOS using Artificial Neural Networks in Wireless Sensor Networks

Abinaya, R.; Kamakshi, S.

doi:10.17485/ijst/2015/v8i12/63283

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Furthermore, congestion in WSNs reacts with the limitation of node energy, low memory space capacity of nodes, sensitivity to delay and changing the topology of sensors. In order to increase the longevity and reliability of the network, one should control on the congestion [4]. If the capacity of the network is overload, then the congestion will be occurred.…”

Section: Congestion In Wsns:-mentioning

confidence: 99%

“…The following figure (10) represent the number of traffic in the memory of the base station that coming from ON nodes with and without RBFNNCC. From the above figure one can see the role of RBFNNCC in managing the traffic that received to the base station in 10 times in fig ( 9) the base station has congestion (in rounds time 1,2,3,4,6,7,8,9) and losses some of the received data, and other once has no congestion (time 5,10) in the case without using RBFNNCC. But with using RBFNNCC all times has traffic less than the size of the memory of base station (270).…”

Section: Tr(t) = ) Datas Of On Nodes)mentioning

confidence: 99%

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A Radial Basis Neural Network Controller to Solve Congestion in Wireless Sensor Networks

Hussain¹

2018

ijci

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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.

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Section: Congestion In Wsns:-mentioning

confidence: 99%