Routing Attack Detection Using Ensemble Deep Learning Model for IIoT

Vatambeti, Ramesh; Mamidisetti, Gowtham

doi:10.56578/ida020104

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…The trust aware routing protocol presented by Vatambeti and Mamidisetti [25] provides a safe routing method for WSNs. Based on response portability and energy limitations, the routing protocol is robust in the presence of hostile nodes nursing activity on the routing tiers.…”

Section: Related Workmentioning

confidence: 99%

An Energy-Aware Cluster Head Selection and Optimal Route Selection Algorithm for Maximizing Network Lifetime in MANETs

Devarayasamudram,

Chandrashekar,

Chetla

et al. 2024

MMEP

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Quality of Service (QoS) is a crucial aspect of Mobile Ad Hoc Networks (MANET) that needs examination to demonstrate optimal performance. The scientific community is increasingly concerned with the challenge of developing an energy-aware clustering method for MANETs. This is owing to the fact that the battery-operated sensor devices that form the backbone of these wireless networks cannot be recharged. The selection of a cluster's leader is a difficult problem in MANET. Additionally, the research focuses on Optimal Route Selection (ORS) within the MANET context, acknowledging the significance of establishing efficient communication paths between cluster heads and member nodes. Through the integration of a reliability pair factor and node energy considerations, the proposed ORS algorithm generates optimal paths based on maximizing energy efficiency while minimizing the sum of hops between nodes. The study suggests a new algorithm based on the way waterwheel plants move and change their places as they explore and exploit new territory in the quest for food. The suggested method is named Binary Waterwheel Plant Algorithm (BWPA). In this method, a novel model is used to represent both the binary search space and the mapping from continuous to discrete spaces. Particularly, mathematical models of the fitness and cost functions used by the algorithm are constructed. Through the use of a reliability pair factor and node energy, the proposed research paper on Optimal Route Selection (ORS) generates the optimal path between the cluster head and member node and establishes the path based on the maximum energy and sum of hops among the nodes. By fusing the reactive power of differential evolution with the exhaustive search efficiency of the BWPA, the proposed method extends the life of networks. The recommended method increases node lifetime by compounding the dynamic capabilities of discrepancy evolution with the high effectiveness of search.

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Section: Related Workmentioning

confidence: 99%

An Energy-Aware Cluster Head Selection and Optimal Route Selection Algorithm for Maximizing Network Lifetime in MANETs

Devarayasamudram,

Chandrashekar,

Chetla

et al. 2024

MMEP

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“…, 𝐿 . Here, k represents the various clusters, and l represents the various patches that form those clusters [21].…”

Section: The Denoising Processmentioning

confidence: 99%

Enhanced Speckle Noise Reduction in Breast Cancer Ultrasound Imagery Using a Hybrid Deep Learning Model

Reddy,

Chitteti,

Yesupadam

et al. 2023

ISI

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Ultrasonic imaging serves as a pivotal tool in mitigating overdiagnosis of breast cancer in women, owing to its high sensitivity, low false-positive rate, and ability to reduce unnecessary biopsies. Nevertheless, these images are impaired by speckle noise, which appears as granular interference obscuring tissue boundaries and diminishing image contrast. This noise impedes subsequent image processing tasks such as edge detection, segmentation, feature extraction, and classification. Existing strategies for speckle noise reduction in ultrasonic images either compromise on effectiveness or demand substantial processing time, presenting challenges in preserving fine edge details. Addressing these issues, we propose an innovative hybrid deep learning model, FCNN-IDOA, which synergizes a Fundamental Convolutional Neural Network (FCNN) with an optimization algorithm. Our FCNN model is built upon the framework of GoogLeNet, enhanced with fifteen additional layers to augment its expressiveness. Subsequently, an Improved Dragonfly Optimization Algorithm (IDOA) is deployed to optimize FCNN's parameters, thereby improving the computational efficiency of the model. The suggested model has demonstrated superior performance, outstripping previous models in terms of accuracy. During experimental validation, the model achieved an average t(s) value of 84.764421, a PSNR value of 66, an MSE value of 54.9143, an RMSE value of 0.491631, and a final t(s) value of 83.759067. The results indicate that this novel model significantly outperforms the BC models, rendering it a promising solution for speckle noise reduction in breast cancer ultrasound images.

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