DC-DC Step-down Converter with wide Switching range and Low Ripple Voltage for Wireless Sensor Node applications

Prasad, Rajendra; Das, Arindam; Laha, Rajshekhar; Mohita,; Arijoy,

doi:10.17485/ijst/2018/v11i20/109855

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…In order to overcome these issues, we need an effective and accurate system which provides better security to the WSN. On the other hand, Machine learning algorithms are one of the better solutions for providing security services in this type of network by including monitoring and decision intelligence (9) .…”

Section: Introductionmentioning

confidence: 99%

Wireless Sensor Node Authentication and Data Security Framework Using Machine Learning

Nirmala,

Rao

2023

IJST

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Objectives: To implement Wireless Sensor Node Authentication and Data Security Framework Using Machine Learning. Methods: The study is centered on a Generative Adversarial Networks (GAN) learning technique which explains about authenticity of secured wireless sensor network. This method is developed using two main components like Generator (G) network which produces false data for confusing the attacker and Discriminator (D) network which consists of multiple layers that can efficiently differentiate between real and fake data for interpretation at the end node (Rx) for providing secure communication. Unauthenticated sensor nodes can be detected with higher accuracy, throughput and low end-to end delay using machine learning based framework. In this work, Probability of False Detection, Reliability and Uniqueness, Delay and throughput are evaluated to determine the performance of presented approach. Findings: Simulation results reveal that the proposed IFAs antennas can provide isolation with a mutual coupling reduction of 18 dB with respect to the transmission coefficients and can also obtain sufficient bandwidth by the proposed antenna array for the 5G mobile terminals applications. Novelty: GANs algorithm is used for the first time to resolve WSN middleware having security issues.

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Section: Introductionmentioning

confidence: 99%

Wireless Sensor Node Authentication and Data Security Framework Using Machine Learning

Nirmala,

Rao

2023

IJST

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“…The in-advance demise of the heads of cluster is due to overload caused by inappropriate assignments of sensor nodes to the heads of cluster during formation of the cluster which in due course increases the latency thereby reducing the performance (22) .…”

Section: Introductionmentioning

confidence: 99%

An efficient hybrid model for cluster head selection to optimize wireless sensor network using simulated annealing algorithm

Umashankar¹,

Anitha²,

Mallikarjunaswamy³

2021

IJST

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Objective: Energy efficiency aspect in wireless sensor networks (WSN) can be achieved by small sized rechargeable and easily replaceable batteries. The lifetime of wireless sensor network can be improved by identifying the efficient and reliable nodes as a cluster heads using Hybrid Simulated Annealing algorithm. The proposed algorithm identifies cluster head to reduce overhead and is capable of handling high volume of nodes with minimum node death rate. Methods: This study proposed initialization of population vectors using the opposite point procedure, self-adaptive control approach by node mutation rate, crossover rate, node capacity and cluster head allocation Methods. Findings: A case study in the proposed work is found to be better in throughput, accuracy, efficiency, energy utilization, batteries recharge ability and replacement procedures compared to the conventional methods. By the analysis and comparison of the proposed method with existing methods, it is identified that the reduction of the number of dead nodes gradually increases the throughput and lifetime of the nodes with respect to the number of iterations. Novelty: To overcome the limitations of conventional Low Energy Adaptive Clustering Hierarchy (LEACH), harmony search algorithm (HSA), modified HSA and differential evolution, we propose a hybrid optimal model using simulated annealing algorithm which includes a node capability function. It is used to improve the network lifetime of the cluster heads and sensor nodes. The proposed method have capability of batteries recharge ability and replacement option to improve network throughput and reliability of network.

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DC-DC Step-down Converter with wide Switching range and Low Ripple Voltage for Wireless Sensor Node applications

Cited by 2 publications

References 3 publications

Wireless Sensor Node Authentication and Data Security Framework Using Machine Learning

Wireless Sensor Node Authentication and Data Security Framework Using Machine Learning

An efficient hybrid model for cluster head selection to optimize wireless sensor network using simulated annealing algorithm

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