T-TOHIP: Trust-based topology-hiding multipath routing in mobile ad hoc network

Ambekar, Rahul K.; Kolekar, Uttam D.

doi:10.1007/s12065-019-00280-z

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…The above Equation ( 12) is improved by changing the random number with the following formulation can be defined as in Equation (13). Here, ρrand…”

Section: Mathematical Modelingmentioning

confidence: 99%

“…Furthermore, the researchers are investigating a variety of characteristics to effectively assess route durability and also overcome the aforementioned issues. 13 However, higher mobility in the network creates heavy packet loss, frequent link failure and high overhead. The issues can be resolved by the clustering techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Path selection statistics in MANET also take into account the path's durability and the nodes' capabilities. Furthermore, the researchers are investigating a variety of characteristics to effectively assess route durability and also overcome the aforementioned issues 13 . However, higher mobility in the network creates heavy packet loss, frequent link failure and high overhead.…”

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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“…The above Equation ( 12) is improved by changing the random number with the following formulation can be defined as in Equation (13). Here, ρrand…”

Section: Mathematical Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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