H-WSN with maximized QoS using secure data aggregation

Suryawanshi, Renuka

doi:10.1109/ic3i.2016.7917950

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…45,[49][50][51][52][53] ARG binding arrests inter-helical motions and stabilizes a coaxial conformation in which bulge residue U23 forms a base triple with helical residues A27 and U38, and junctional residues A22 and U40 become stably hydrogen bonded. 49,53 Several studies measuring TAR binding to Tat-like peptides or ARG-derived compounds have reported evidence for two binding sites, 43,[54][55][56][57] while others report single apparent affinities, 44,50,[58][59][60][61] and it remains unclear what role the other Tat arginine residues located in the arginine-rich RNA binding domain (RBD, residues G48-R57) 62 play in vivo. Overall, the results in the literature hint at conformational heterogeneity present in the TAR•ARG bound state that has not been fully explored.…”

Section: Introductionmentioning

confidence: 99%

Exposing Hidden High-Affinity RNA Conformational States

2019

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RNA recognition frequently results in conformational changes that optimize intermolecular binding. As a consequence, the overall binding affinity of RNA to its binding partners depends not only on the intermolecular interactions formed in the bound state but also on the energy cost associated with changing the RNA conformational distribution. Measuring these "conformational penalties" is, however, challenging because bound RNA conformations tend to have equilibrium populations in the absence of the binding partner that fall outside detection by conventional biophysical methods. In this study we employ as a model system HIV-1 TAR RNA and its interaction with the ligand argininamide (ARG), a mimic of TAR's cognate protein binding partner, the transactivator Tat. We use NMR chemical shift perturbations and relaxation dispersion in combination with Bayesian inference to develop a detailed thermodynamic model of coupled conformational change and ligand binding. Starting from a comprehensive 12-state model of the equilibrium, we estimate the energies of six distinct detectable thermodynamic states that are not accessible by currently available methods. Our approach identifies a minimum of four RNA intermediates that differ in terms of the TAR conformation and ARG occupancy. The dominant bound TAR conformation features two bound ARG ligands and has an equilibrium population in the absence of ARG that is below detection limit. Consequently, even though ARG binds to TAR with an apparent overall weak affinity K d app ≈ 0.2mM , it binds the prefolded conformation with a K d in the nM range. Our results show that conformational penalties can be major determinants of RNA-ligand binding affinity as well as a source of binding cooperativity, with important implications for a predictive understanding of how RNA is recognized and for RNA-targeted drug discovery.

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Section: Introductionmentioning

confidence: 99%

Exposing Hidden High-Affinity RNA Conformational States

2019

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“…Each gadget has the fitness to move and receive parcels from elective hubs, presenting advantages in terms of its difference in access [4]. Advances in remote correspondence make it possible to create WSNs comprising modest gadgets that gather information by collaboration [5].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Energy Efficient Adaptive Whale Optimization Based Routing for Wireless Sensor Network

Bali

Gill²,

Choudhary³

et al. 2022

Energies

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In Wireless Sensor Networks (WSNs), routing algorithms can provide energy efficiency. However, due to unbalanced energy consumption for all nodes, the network lifetime is still prone to degradation. Hence, energy efficient routing was developed in this article by selecting cluster heads (CH) with the help of adaptive whale optimization (AWOA) which was used to reduce time-consumption delays. The multi-objective function was developed for CH selection. The clusters were then created using the distance function. After establishing groupings, the supercluster head (SCH) was selected using the benefit of a fuzzy inference system (FIS) which was used to collect data for all CHs and send them to the base station (BS). Finally, for the data-transfer procedure, hop count routing was used. An Oppositional-based Whale optimization algorithm (OWOA) was developed for multi-constrained QoS routing with the help of AWOA. The performance of the proposed OWOA methodology was analyzed according to the following metrics: delay, delivery ratio, energy, NLT, and throughput and compared with conventional techniques such as particle swarm optimization, genetic algorithm, and Whale optimization algorithm.

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“…In given system cache memory is provided by the cluster head for recovery of data loss. At last test results shows depending on parameter like time as well as energy consumption on Jung simulator that system presented is good compared to the available system [9].…”

Section: Literature Surveymentioning

confidence: 99%

Enhanced Technique for Selection of Cluster Head in Wireless Sensor Network

2017

IJAERD

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H-WSN with maximized QoS using secure data aggregation

Cited by 7 publications

References 8 publications

Exposing Hidden High-Affinity RNA Conformational States

Exposing Hidden High-Affinity RNA Conformational States

Multi-Objective Energy Efficient Adaptive Whale Optimization Based Routing for Wireless Sensor Network

Enhanced Technique for Selection of Cluster Head in Wireless Sensor Network

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