Multidimensional Multi-Attribute Approach to Counter the Routing Attacks on MANET

Marathe, Nilesh; Shinde, Subhash K.

doi:10.1007/s11277-021-08315-4

Cited by 3 publications

(4 citation statements)

References 31 publications

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“…In 2021, Nilesh R. Marathe and Subhash K. Shinde 1 has introduced ITCA to eliminate the malicious nodes that were possible in a hybrid technique environment via merged efforts against numerous dimensions. The discovered route was safeguarded by separating the defective nodes at the beginning stage with the utilization of reputation as well as a trust‐based computation procedure.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, concerning the overfitting and greater risk in regression models are still a difficult procedure. Using paper, 1 the author achieved high flexible and superior based on environmental fluctuations with MDMA model. Yet, it is still complicated to manage dynamic self‐organizing system.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Mobile Ad Hoc Network (MANET) is made up of self-configurable independent nodes that collaborate in a dispersed fashion and have no dependence on certain permanent facilities to function. 1 Individual devices in a MANET can move independently in any manner. Additionally, there are a lot of features that distinguish ad hoc networks with mobile nodes from traditional mobile wireless networks, which include dynamic topology configuration, infrastructure-less, node mobility, and multi-hop relaying.…”

Section: Introductionmentioning

confidence: 99%

“…Mobile Ad Hoc Network (MANET) is made up of self‐configurable independent nodes that collaborate in a dispersed fashion and have no dependence on certain permanent facilities to function 1 . Individual devices in a MANET can move independently in any manner.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Mobile Ad Hoc Network cluster based routing: Energy prediction via improved deep learning technique

Murugan,

Unhelkar

2024

Int J Communication

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SummaryMobile Ad Hoc Network (MANETs) are indeed autonomous, fast‐deployable wireless networks that are ideal for communications in areas with limited radio infrastructure, outdoor events, military operations, and disaster relief. But the primary problem is with routing because of the nodes' mobility. This paper proposes a cluster‐based routing under energy prediction via deep learning techniques. Here, proposes a new model termed as Concatenation of Convolutional with Max‐Avg Pooling layer in Deep Convolutional Neural Network (CCMAP‐DCNN) for energy prediction, in which additional layers are inserted into the extant DCNN structure to ensure the effectiveness of energy prediction. As a result, clusters are created by grouping the nodes together. Constraints like energy, trust, distance, and latency are taken into account while choosing the cluster leader. For cluster head selection and optimal routing, proposes a new hybrid Namib Beetle Upgraded Jellyfish Search Optimization (NBUJSO) algorithm that utilizes the NBO strategy to the JSO algorithm making the selection process more optimal. The best route is then chosen by the optimum routing method, which takes into account variables like network quality and mobility to send data packets from source to destination as efficiently as possible. Finally, the data aggregation process is followed for eliminating the redundant transmission.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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