Congestion detection in Wireless sensor network using neural network

Singhal, Prakul; Yadav, Anamika

doi:10.1109/i2ct.2014.7092259

Cited by 16 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This will also minimize the average number of samples required to make a decision for transmission of data onto the channel and hence leading to reduction in the communication delay, power consumption and the risk of collisions [12]. Neural Network based congestion detection (NNBCD) a layer based congestion protocol has been proposed to detect the level of congestion during the significant packet drop in the network by considering the buffer occupancy and traffic rate [13]. Further, a Delay-aware congestion control (DACC) protocol estimates channel occupancy based on buffer occupancy and transmission time of packets.…”

Section: Diverse Congestion Control Strategiesmentioning

confidence: 99%

Diverse Congestion Control Schemes for Wireless Sensor Networks

al.¹

2021

TURCOMAT

View full text Add to dashboard Cite

Wireless Sensor Networks (WSNs) comprised of battery operated sensor nodes that collect data from their neighbor nodes and transmit the aggregated information to the sink node or the Base Station (BS). This may result in congestion near the BS and leads to a bottleneck situation in the network. In this paper, an extensive study of earlier reported diverse congestion techniques explicitly diverse Algorithm based - and Layer based-congestion techniques is carried out. Accordingly, a recommendation is drawn based upon their performance comparison. Furthermore, a demonstration is carried out for contemporary earlier reported strategies such as Pro-AODV, CC-AODV, EDAPR, ED-AODV and PCC-AODV by evaluating delay, packet delivery ratio (PDR) and packet loss ratio (PLR). Accordingly, a recommended congestion strategy is suggested depending upon the comparison of the demonstrated schemes.

show abstract

Section: Diverse Congestion Control Strategiesmentioning

confidence: 99%

Diverse Congestion Control Schemes for Wireless Sensor Networks

al.¹

2021

TURCOMAT

View full text Add to dashboard Cite

show abstract

“…Singhal and Yadav [7] used neural networks to detect congestion in wireless sensor networks. The authors created their own dataset using NS-2 [8] to generate a random traffic.…”

Section: Related Workmentioning

confidence: 99%

Predicting Bandwidth Utilization on Network Links Using Machine Learning

Labonne

Chatzinakis

Olivereau

2020

2020 European Conference on Networks and Communications (EuCNC)

View full text Add to dashboard Cite

Predicting the bandwidth utilization on network links can be extremely useful for detecting congestion in order to correct them before they occur. In this paper, we present a solution to predict the bandwidth utilization between different network links with a very high accuracy. A simulated network is created to collect data related to the performance of the network links on every interface. These data are processed and expanded with feature engineering in order to create a training set. We evaluate and compare three types of machine learning algorithms, namely ARIMA (AutoRegressive Integrated Moving Average), MLP (Multi Layer Perceptron) and LSTM (Long Short-Term Memory), in order to predict the future bandwidth consumption. The LSTM outperforms ARIMA and MLP with very accurate predictions, rarely exceeding a 3% error (40% for ARIMA and 20% for the MLP). We then show that the proposed solution can be used in real time with a reaction managed by a Software-Defined Networking (SDN) platform.

show abstract

“…Congestion is a common problem in packet transferring networks. Congestion occurs when data sources send data at rates greater than the network's capacity in one or more intermediate methods, which pass through WSN over one or more nodes [4]. New research trends and dissatisfaction solutions have been studied to solve congestion problem in wireless sensor networks.…”

Section: Introductionmentioning

confidence: 99%

Modified Training Algorithm for Spiking Neural Network and its Application in Wireless Sensor Network

Al-Yasari¹,

Al-Jamali²

2018

International Advanced Research Journal in Science, Engineering

View full text Add to dashboard Cite

In this paper, controlling of data traffic of the Wireless Sensor Network (WSN) is implemented to improve the Quality of Service (QoS) in WSN with mobile sink node. The quality of service is measured in terms of throughput ratio, Packet Loss Ratio (PLR) and Network Energy Consumption (NEC). Spiking neural network is applied to implement the controller that is most suitable with changeable traffic data. The controller is implemented based on python simulation. Simulation results showed that the proposed controller provides better quality of service in comparison with wireless sensor network without controller by about 57%.

show abstract

Congestion detection in Wireless sensor network using neural network

Cited by 16 publications

References 10 publications

Diverse Congestion Control Schemes for Wireless Sensor Networks

Diverse Congestion Control Schemes for Wireless Sensor Networks

Predicting Bandwidth Utilization on Network Links Using Machine Learning

Modified Training Algorithm for Spiking Neural Network and its Application in Wireless Sensor Network

Contact Info

Product

Resources

About