Congestion Control in Wireless Sensor Networks based on Support Vector Machine, Grey Wolf Optimization and Differential Evolution

Kazmi, Hafiza Syeda Zainab; Javaid, Nadeem; Imran, Muhammad; Outay, Fatma

doi:10.1109/wd.2019.8734265

Cited by 16 publications

(7 citation statements)

References 18 publications

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“…This work is an extension of the work of Kazmi et al 42 This section is divided in three sections. The first section explains the proposed techniques for congestion avoidance, the second section gives an overview of the induced faults, and the third section describes the presented technique for fault detection in WSNs.…”

Section: Proposed Methodologymentioning

confidence: 98%

Congestion avoidance and fault detection in WSNs using data science techniques

Kazmi

Javaid

Awais

et al. 2019

Trans Emerging Tel Tech

Self Cite

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Transmission rate is one of the contributing factors in the performance of wireless sensor networks. Congested network causes reduced network response time, queuing delay, and more packet loss. To address the issue of congestion, we have proposed transmission rate control methods. To avoid the congestion, we have adjusted the transmission rate at current node based on its traffic loading information. Multiclassification is done to control the congestion using an effective data science technique, namely support vector machine (SVM). In order to get less miss classification error, differential evolution (DE) and grey wolf optimization (GWO) algorithms are used to tune the SVM parameters. The comparative analysis has shown that the proposed approaches DE-SVM and GWO-SVM are more proficient than other classification techniques. Moreover, DE-SVM and GWO-SVM have outperformed the benchmark technique genetic algorithm-SVM by producing 3% and 1% less classification errors, respectively.For fault detection in wireless sensor networks, we have induced four types of faults in the sensor readings and detected the faults using the proposed enhanced random forest. We have made a comparative analysis with state of the art data science techniques based on two metrics, ie, detection accuracy and true positive rate. Enhanced random forest has detected the faults with 81% percent accuracy and outperformed the other classifiers in fault detection.

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Section: Proposed Methodologymentioning

confidence: 98%

Congestion avoidance and fault detection in WSNs using data science techniques

Kazmi

Javaid

Awais

et al. 2019

Trans Emerging Tel Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…It shows better results than PSO. In [ 40 ] the proposed technique provide a transmission rate control mechanism based on (GFO) for WSNs. Support vector machine is used for controlling transmission rate of nodes and for tuning of (SVM) gray wolf optimizer is used.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Node Clustering for Underwater Sensor Networks

Khan

Bibi

Aadil

et al. 2021

Sensors

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Monitoring of an underwater environment and communication is essential for many applications, such as sea habitat monitoring, offshore investigation and mineral exploration, but due to underwater current, low bandwidth, high water pressure, propagation delay and error probability, underwater communication is challenging. In this paper, we proposed a sensor node clustering technique for UWSNs named as adaptive node clustering technique (ANC-UWSNs). It uses a dragonfly optimization (DFO) algorithm for selecting ideal measure of clusters needed for routing. The DFO algorithm is inspired by the swarming behavior of dragons. The proposed methodology correlates with other algorithms, for example the ant colony optimizer (ACO), comprehensive learning particle swarm optimizer (CLPSO), gray wolf optimizer (GWO) and moth flame optimizer (MFO). Grid size, transmission range and nodes density are used in a performance matrix, which varies during simulation. Results show that DFO outperform the other algorithms. It produces a higher optimized number of clusters as compared to other algorithms and hence optimizes overall routing and increases the life span of a network.

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“…In consequence, it profoundly impacts network energy efficiency as well as network lifespan. Several congestion control mechanisms have been presented in literature till date (Dashkova and Gurtov 2012;Hashemzeh et al 2013, Ghaffari 2015Shah et al 2017;Tabatabaei and Omrani 2018;Nicolaou et al 2019;Kazmi et al 2019). In Dashkova and Gurtov (2012), author explored existing congestion control mechanism and presented a comparative study of those protocols.…”

Section: Network Congestionmentioning

confidence: 99%

“…Simulation outcomes depicts that both suggested approaches performed well to alleviate congestion in a significant way. Author in Kazmi et al (2019) highlights another reason for congestion in the network and explained that it occurs when number of packets delivered to the node is more than the number of packets forwarded by that node.…”

Section: Network Congestionmentioning

confidence: 99%

Optimized clustering using soft computing approaches in wireless sensor networks: research dimensions and contributions

Sharma

2021

Int J Syst Assur Eng Manag

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Presently, Wireless Sensor Network (WSN) is considered as the most prominent technologies employed in commercial as well as in industrial sector. The WSN comprises of battery-operated nodes that are used to monitor the surroundings in order to keep record of the physical phenomenon like temperature, pressure, position, vibration, humidity, sound etc. These nodes can be utilized in several real-time application domains to perform different tasks like target tracking, home surveillance, pollution monitoring, structural monitoring etc. Depending on the type of nodes deployment and the application areas, WSNs can be exploited underground, underwater, terrestrial, wearable or environment embedded. Firstly, this paper categorizes research dimensions in wireless sensor networks primary and secondary research domains and the research contributions in those fields. Secondly, it discusses different soft computing-based clustering schemes from past two decades to deal with energy conservation issue in sensor networks. This paper provides a clear insight to the beginners of this field by covering literature review from 2008 to 2020.

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Congestion Control in Wireless Sensor Networks based on Support Vector Machine, Grey Wolf Optimization and Differential Evolution

Cited by 16 publications

References 18 publications

Congestion avoidance and fault detection in WSNs using data science techniques

Congestion avoidance and fault detection in WSNs using data science techniques

Adaptive Node Clustering for Underwater Sensor Networks

Optimized clustering using soft computing approaches in wireless sensor networks: research dimensions and contributions

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