Improved Deep Convolutional Neural Network Based Malicious Node Detection and Energy-Efficient Data Transmission in Wireless Sensor Networks

Kumar, Mohit; Mukherjee, Priya; Kavita, Kavita; Verma, Sahil; Rawat, Danda B.

doi:10.1109/tnse.2021.3098011

Cited by 101 publications

(35 citation statements)

References 27 publications

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“…Based on the used benchmark tests, our method obtains good results, generally outperforming the outputs obtained by other state-of-the-art techniques. Thus, we are confident that it can be adapted and tested on other NP-hard tasks, even different from of clustering, such as machine learning hyperparameter tuning, feature selection [72], wireless sensor network localization, intrusion detection within networks [73,74], energy, cloud task scheduling, path planning, portfolio optimization, etc.…”

Section: Discussionmentioning

confidence: 99%

Hybrid Fruit-Fly Optimization Algorithm with K-Means for Text Document Clustering

et al. 2021

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The fast-growing Internet results in massive amounts of text data. Due to the large volume of the unstructured format of text data, extracting relevant information and its analysis becomes very challenging. Text document clustering is a text-mining process that partitions the set of text-based documents into mutually exclusive clusters in such a way that documents within the same group are similar to each other, while documents from different clusters differ based on the content. One of the biggest challenges in text clustering is partitioning the collection of text data by measuring the relevance of the content in the documents. Addressing this issue, in this work a hybrid swarm intelligence algorithm with a K-means algorithm is proposed for text clustering. First, the hybrid fruit-fly optimization algorithm is tested on ten unconstrained CEC2019 benchmark functions. Next, the proposed method is evaluated on six standard benchmark text datasets. The experimental evaluation on the unconstrained functions, as well as on text-based documents, indicated that the proposed approach is robust and superior to other state-of-the-art methods.

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Section: Discussionmentioning

confidence: 99%

Hybrid Fruit-Fly Optimization Algorithm with K-Means for Text Document Clustering

et al. 2021

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“…In [20], the hybrid logical security framework-provides the data confidentiality and authentication in Internet of Things. A lightweight cryptographic mechanism is used by HLSF.…”

Section: Literature Surveymentioning

confidence: 99%

Energy Efficient Unequal Fault Tolerance Clustering Approach

Ramisetty¹,

Anand²,

Kavita³

et al. 2023

Computer Systems Science and Engineering

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For achieving Energy-Efficiency in wireless sensor networks (WSNs), different schemes have been proposed which focuses only on reducing the energy consumption. A shortest path determines for the Base Station (BS), but fault tolerance and energy balancing gives equal importance for improving the network lifetime. For saving energy in WSNs, clustering is considered as one of the effective methods for Wireless Sensor Networks. Because of the excessive overload, more energy consumed by cluster heads (CHs) in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to failure. For increasing the WSNs' lifetime, the CHs selection has played a key role in energy consumption for sensor nodes. An Energy Efficient Unequal Fault Tolerant Clustering Approach (EEUFTC) is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization (PSO). In this approach, an optimal Master Cluster Head (MCH)-Master data Aggregator (MDA), selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator (MDA), and Master Cluster Head. The data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the BS. Thus, the MCH overhead reduces. During the heavy communication of data, overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA). To describe the proposed method superiority based on various performance metrics, simulation and results are compared to the existing methods.

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“…Linear prediction approaches are used for the estimation of fundamental component of speech. In this method, input is first prehighlighted using filter of high pass and its transfer function will be [ 49 ]:

…”

Section: Feature Removalmentioning

confidence: 99%

Feature Extraction Approach for Speaker Verification to Support Healthcare System Using Blockchain Security for Data Privacy

Upadhyay¹,

Kumar²,

Kumar³

et al. 2022

Computational and Mathematical Methods in Medicine

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Speech is one form of biometric that combines both physiological and behavioral features. It is beneficial for remote-access transactions over telecommunication networks. Presently, this task is the most challenging one for researchers. People’s mental status in the form of emotions is quite complex, and its complexity depends upon internal behavior. Emotion and facial behavior are essential characteristics through which human internal thought can be predicted. Speech is one of the mechanisms through which human’s various internal reflections can be expected and extracted by focusing on the vocal track, the flow of voice, voice frequency, etc. Human voice specimens of different ages can be emotions that can be predicted through a deep learning approach using feature removal behavior prediction that will help build a step intelligent healthcare system strong and provide data to various doctors of medical institutes and hospitals to understand the physiological behavior of humans. Healthcare is a clinical area with data concentrated where many details are accessed, generated, and circulated periodically. Healthcare systems with many existing approaches like tracing and tracking continuously disclose the system’s constraints in controlling patient data privacy and security. In the healthcare system, majority of the work involves swapping or using decisively confidential and personal data. A key issue is the modeling of approaches that guarantee the value of health-related data while protecting privacy and observing high behavioral standards. This will encourage large-scale perception, especially as healthcare information collection is expected to continue far off this current ongoing pandemic. So, the research section is looking for a privacy-preserving, secure, and sustainable system by using a technology called Blockchain. Data related to healthcare and distribution among institutions is a very challenging task. Storage of facts in the centralized form is a targeted choice for cyber hackers and initiates an accordant sight of patients’ facts which will cause a problem in sharing information over a network. So, this research paper’s approach based on Blockchain for sharing sufferer data in a secured manner is presented. Finally, the proposed model for extracting optimum value in error rate and accuracy was analyzed using different feature removal approaches to determine which feature removal performs better with different voice specimen variations. The proposed method increases the rate of correct evidence collection and minimizes the loss and authentication issues and using feature extraction based on text validation increases the sustainability of the healthcare system.

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Improved Deep Convolutional Neural Network Based Malicious Node Detection and Energy-Efficient Data Transmission in Wireless Sensor Networks

Cited by 101 publications

References 27 publications

Hybrid Fruit-Fly Optimization Algorithm with K-Means for Text Document Clustering

Hybrid Fruit-Fly Optimization Algorithm with K-Means for Text Document Clustering

Energy Efficient Unequal Fault Tolerance Clustering Approach

Feature Extraction Approach for Speaker Verification to Support Healthcare System Using Blockchain Security for Data Privacy

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