I-CSA based Cluster Head Selection Model in Wireless Sensor Network

Shelgaonkar, Sadanand L

doi:10.46253/jnacs.v3i2.a5

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…In accordance with the centralized algorithms in WSN, the distributed algorithm has less information about the specific region or the neighboring nodes, which makes the network attain better performance 22,23 . In order to obtain efficient data communication among sensor nodes, it is required to consider interference.…”

Section: Introductionmentioning

confidence: 99%

Detection of collision using optimized deep model and mitigation of collision using dolphin ant lion optimizer in wireless sensor network

Khare

Selvakumar

Dugyala

2023

Int J Communication

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Summary Wireless sensor network (WSN) comprises automatic sensors that are dispersed into a huge region. WSN is constructed from huge sensors, which is allocated to a particular task and the majority of task involves reporting and monitoring. However, as the network can be extended to several sensor nodes, there is a high chance of collision. Thus, this paper devises a novel technique for performing both collision detection and mitigation in WSN. Initially, the simulation of WSN is performed, and then the selection of cluster head is done using fractional artificial bee colony (FABC). Here, the network‐based parameter is extracted that involves received signal strength index (RSSI), priority level, delivery rate, and energy consumed. The deep recurrent neural network (DRNN) is adapted for collision detection. Here, the training of DRNN is done using lion crow search optimizer (LCSO). After collision detection, the collision mitigation is performed with a pre‐scheduling algorithm, namely dolphin ant lion optimizer (Dolphin ALO). Here, fitness is considered for collision mitigation that includes energy, sleep index (SI), delivery rate, priority level, E‐waste, and E‐save. The proposed method outperformed with the smallest energy consumption of 0.185, highest throughput of 0.815, highest packet delivery ratio (PDR) of 0.815, and highest collision detection rate of 0.930.

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Section: Introductionmentioning

confidence: 99%

Detection of collision using optimized deep model and mitigation of collision using dolphin ant lion optimizer in wireless sensor network

Khare

Selvakumar

Dugyala

2023

Int J Communication

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“…In recent days, fabric defect detection based on computer vision has become an interesting research domain [13]. Automatic visual inspection of fabric adapts several computer vision methods [14], which can enhance efficiency, save cost and ensure the quality of the product [4,4].…”

Section: Introductionmentioning

confidence: 99%

Fabric Defect Detection Using Competitive Cat Swarm Optimizer Based RideNN and Deep Neuro Fuzzy Network

Biradar¹,

Sheeparamatti²,

Patil³

2021

Sens Imaging

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The rising rates of costs in labor and growth of computerization in fabric industries made defect detection in fabric a promising domain. For a huge time, manual discovery is extensively utilized in textile industries by trained staff that results in high cost. Meanwhile, the strict quality assessment is done by modern textile industries, which made automatic fabric defect detection a reliable choice. Since defect detection is an important and challenging aspect of modern industrial manufacturing, it is necessary to determine the quality and acceptability of garments and to reduce the cost and time waste caused by defects. Different methods are in practice for effective detection of fabric defects, however, they limit due to many reasons. Thus we proposed a new method named Competitive Cat Swarm Optimizer (CCSO) based Deep neuro-fuzzy network (DNFN) for effective fabric detection. Here, the pre-processing is performed with a median filter for eliminating noise contained in the image. Furthermore, features are extracted that involves Tetrolet transform-based features, statistical features, like energy, entropy, homogeneity, contrast, correlation, and texture feature, like Local gradient pattern. The data augmentation is carried out based on obtained features to make it apposite for processing. The defect detection is carried out using RideNN and DNFN. Here, the training of DNFN is done using the proposed CCSO, which is devised by combining Cat Swarm Optimization and Competitive Swarm Optimizer. The correlation is performed on the outputs of RideNN and DNFN for generating the final output of fabric defect detection. The performance of the proposed CCSO-based DNFN is compared with the different existing methods and the proposed CCSO-based DNFN outperforms with the highest specificity of 0.920, the accuracy of 0.919, and sensitivity of 0.916.

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Energy and Trust Efficient Cluster Head Selection in Wireless Sensor Networks Under Meta-Heuristic Model

Dattatraya

Ananthakumaran

2022

Expert Clouds and Applications

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I-CSA based Cluster Head Selection Model in Wireless Sensor Network

Cited by 6 publications

References 16 publications

Detection of collision using optimized deep model and mitigation of collision using dolphin ant lion optimizer in wireless sensor network

Detection of collision using optimized deep model and mitigation of collision using dolphin ant lion optimizer in wireless sensor network

Fabric Defect Detection Using Competitive Cat Swarm Optimizer Based RideNN and Deep Neuro Fuzzy Network

Energy and Trust Efficient Cluster Head Selection in Wireless Sensor Networks Under Meta-Heuristic Model

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