A Novel Energy Efficient Clustering Technique for Mobile Wireless Sensor Networks

Kadiravan, G.; Sariga, A.; Sujatha, P.

doi:10.1109/icscan.2019.8878705

Cited by 10 publications

(7 citation statements)

References 6 publications

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“…1 shows the framework of WSN model. WSN node localization technique could be categorized as to the range free and range based localization methods based on the distance measure is required in the localization procedure [4]. The previous need is to measure the distance among the anchor and unknown nodes while measuring the positions of unknown nodes.…”

Section: Introductionmentioning

confidence: 99%

Robust Node Localization with Intrusion Detection for Wireless Sensor Networks

Punithavathi¹,

Selvi²,

Latha³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Wireless sensor networks comprise a set of autonomous sensor nodes, commonly used for data gathering and tracking applications. Node localization and intrusion detection are considered as the major design issue in WSN. Therefore, this paper presents a new multi-objective manta ray foraging optimization (MRFO) based node localization with intrusion detection (MOMRFO-NLID) technique for WSN. The goal of the MOMRFO-NLID technique is to optimally localize the unknown nodes and determine the existence of intrusions in the network. The MOMRFO-NLID technique encompasses two major stages namely MRFO based localization of nodes and optimal Siamese Neural Network (OSNN) based intrusion detection. The OSNN technique involves the hyperparameter tuning of the traditional SNN using the MRFO algorithm and consequently increases the detection rate. In order to assess the enhanced performance of the MOMRFO-NLID technique, a series of simulations take place and the results reported superior performance compared to existing techniques interms of distinct evaluation parameters.

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Section: Introductionmentioning

confidence: 99%

Robust Node Localization with Intrusion Detection for Wireless Sensor Networks

Punithavathi¹,

Selvi²,

Latha³

et al. 2022

Intelligent Automation &Amp; Soft Computing

Self Cite

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“…Consequently, Underwater Sensor Network (UWSN) is one such advanced technique that enables underwater exploration. Being a network of independent sensor nodes [1,2], UWSN is a combination of wireless techniques with minuscule micromechanical sensors that are loaded with smart computation, smart sensing and communication capability. The sensor nodes in UWSN are spatially distributed under water to capture information on water-relevant features such as pressure, quality, and temperature.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Deep Learning Based Automated Fish Detection Model for UWSN

Duhayyim¹,

Alshahrani²,

Al‐Wesabi³

et al. 2022

Computers, Materials &Amp; Continua

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An exponential growth in advanced technologies has resulted in the exploration of Ocean spaces. It has paved the way for new opportunities that can address questions relevant to diversity, uniqueness, and difficulty of marine life. Underwater Wireless Sensor Networks (UWSNs) are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental conditions. In this scenario, it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water bodies. Several models have been developed earlier for fish detection. However, they lack robustness to accommodate considerable differences in scenes owing to poor luminosity, fish orientation, structure of seabed, aquatic plant movement in the background and distinctive shapes and texture of fishes from different genus. With this motivation, the current research article introduces an Intelligent Deep Learning based Automated Fish Detection model for UWSN, named IDLAFD-UWSN model. The presented IDLAFD-UWSN model aims at automatic detection of fishes from underwater videos, particularly in blurred and crowded environments. IDLAFD-UWSN model makes use of Mask Region Convolutional Neural Network (Mask RCNN) with Capsule Network as a baseline model for fish detection. Besides, in order to train Mask RCNN, background subtraction process using Gaussian Mixture Model (GMM) model is applied. This model makes use of motion details of fishes in video which consequently integrates the outcome with actual image for the generation of fish-dependent candidate regions. Finally, Wavelet Kernel Extreme Learning Machine (WKELM) model is utilized as a classifier model. The performance of the proposed IDLAFD-UWSN model was tested against benchmark underwater video dataset and the experimental results achieved by IDLAFD-UWSN model

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“…The Metaheuristic system doesn't continuity differentiability, require convexity, or particular early condition that corresponds to significant benefits related to other methods. In spite of stimulating outcomes, these search approaches preserve various problems while they are employed to higher multi modal optimization problems [7][8][9].…”

Section: Introductionmentioning

confidence: 99%