Emerging Trends: Nano-Scale Wireless Sensor Networks and Applications

Julissa E. Reyna-Gonzalez DRA; N. K. Rayaguru; Gowrishankar J.; Bhargavi Gaurav Deshpande; Madhur Grover; Daxa Vekariya

doi:https://doi.org/10.54216/JISIoT.130202

Journal of Cybersecurity and Information Management | 2024

Novel Slumped SRAM Configuration using QCA Leveraging Differential Voltage Sensing for Enhanced Stability and Efficiency

This paper presents a novel Slumped Static Random-Access Memory (SRAM) configuration utilizing Quantum-dot Cellular Automata (QCA) technology, aimed at achieving enhanced stability and efficiency. Traditional CMOS-based SRAM designs face significant challenges related to power consumption and scalability as technology nodes shrink. QCA, with its potential for ultra-low power dissipation and high-density integration, emerges as a promising alternative. Our proposed SRAM configuration leverages a unique differential voltage sensing mechanism to bolster the stability of the memory cells, particularly under conditions of variability and noise. Through detailed simulations and comparative analysis, we demonstrate that the Slumped SRAM configuration not only improves static noise margin (SNM) but also reduces power consumption and enhances overall read/write speed. The results indicate a substantial improvement in stability and operational efficiency, positioning this design as a viable solution for future high-performance, low-power memory applications. Through detailed simulations and comparative analysis, we demonstrate that the Slumped SRAM configuration achieves a static noise margin (SNM) improvement of 35% over conventional CMOS-based SRAM designs. Additionally, the proposed design reduces power consumption by 40% and enhances read/write speed by 25%. These results indicate a substantial improvement in stability and operational efficiency, positioning this design as a viable solution for future high-performance, low-power memory applications.

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N. Naga Saranya mail -

V. Jean Shilpa mail -

K. Jayakumar mail -

P. Senthil mail -

M. Arun mail

link https://doi.org/10.54216/JCIM.140114

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2024

Prediction of Traffic Congestion in Vehicular Ad-Hoc Networks Employing Extreme Deep Learning Machines (Edrlm)

Vehicular Ad-Hoc Networks (VANETs) represent a crucial component of intelligent transportation systems (ITS), enabling vehicles to communicate with each other and with roadside infrastructure. Predicting traffic congestion in VANETs is essential for enhancing road safety, optimizing traffic flow, and improving overall transportation efficiency. Traditional machine learning methods have shown promise in this domain; however, they often fall short in handling the complex, high-dimensional data typical of VANETs. To address these challenges, this study employs Extreme Deep Learning Machines (EDRLM), an advanced deep learning technique, for traffic congestion prediction. The EDRLM framework leverages the strengths of deep neural networks and extreme learning machines, offering a robust and scalable solution for processing the dynamic and heterogeneous data in VANETs. By integrating feature extraction, selection, and prediction into a unified model, EDRLM can capture intricate patterns and temporal dependencies within traffic data. The proposed model is trained and validated using real-world VANET datasets, incorporating various traffic parameters such as vehicle speed, density, and inter-vehicular distances. Our experimental results demonstrate that EDRLM outperforms conventional machine learning algorithms in terms of prediction accuracy, computational efficiency, and robustness to noise and missing data. The model's ability to provide timely and precise congestion predictions can facilitate proactive traffic management strategies, including dynamic routing and adaptive traffic signal control, ultimately leading to reduced travel times and enhanced road safety. This study underscores the potential of EDRLM in transforming traffic management in VANETs, paving the way for more intelligent and adaptive ITS solutions. Future research directions include exploring hybrid models combining EDRLM with other advanced machine learning techniques and expanding the framework to accommodate emerging vehicular communication technologies such as 5G and Internet of Things (IoT) devices.

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R. Logesh Babu mail -

Jagannadha Naidu K. mail -

V. Jeya Ramya mail -

Regan D. mail

link https://doi.org/10.54216/JCIM.140115

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2024

Sharp Estimates for the Zalcman Conjecture and Second Order Hankel Determinant

In this work, we found sharp estimates for the Zalcman conjecture and second order Hankel determinant for the inverse function when it belongs to the class of starlike functions with respect to symmetric points, denoted by . These results are new.

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Audy Hatim Saheb mail

link https://doi.org/10.54216/GJMSA.0100206

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2024

Neutrosophic analysis of the factors determining the development of humorous discourse in videos using the TOPSIS method

YouTube is moving towards personalized media. In 2011, Enchufe TV became an Ecuadorian online comedy series known for its witty humor. Taking advantage of the openness of the Internet, the video is currently available to watch on the YouTube platform. The purpose of this study is to conduct an unbiased analysis of the factors that determine the development of humorous discourse in TV Antufe's YouTube videos using the TOPSIS method. To understand the growth of the show and its audience, we compared its premiere year to 2022 across 10 years. At the same time, data such as comedy type, language level, audio-visual narrative, and humorous discourse were collected to quantitatively understand the popularity and influence of the play at the time. There. Variables such as views, audience engagement, and subscriber base growth are analyzed, as well as objective measures of content relevance and influence within the platform environment. Enchufe TV's decline in user activity can also be explained by several factors, such as the emergence of new platforms and content saturation. We also found that blue spoken words were the most widely used, with popularity varying by year of study.  

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Alejandra A. Vera Vera mail -

Xiomara N. Lindo Quito mail -

Paolo A. Ortiz mail -

Muhammad Eid Balbaa mail

link https://doi.org/10.54216/IJNS.240423

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2024

Selection process based on new type neutrosophic interval-valued set applied to logarithm operator

We introduce the new type neutrosophic interval-valued set (NIVS) problems relevant to multiple attribute decision making (MADM). Pythagorean interval-valued fuzzy set (PIVFS) and neutrosophic set (NS) can be extended into new type neutrosophic interval-valued set. We discusses new type neutrosophic interval-valued weighted averaging (new type NIVWA), new type neutrosophic interval-valued weighted geometric (new type NIVWG), generalized new type neutrosophic interval-valued weighted averaging (new type GNIVWA) and generalized new type neutrosophic interval-valued weighted geometric (new type GNIVWG). A number of algebraic properties of new type NIVSs have been established such as associativity, distributivity and idempotency. Using expert judgments and criteria, we will be able to decide which options are the most appropriate. Several of the proposed and current models are also compared in order to demonstrate the reliability and usefulness of the models under study. Additionally, the findings of the study are fascinating and intriguing.  

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Lejo J. Manavalan mail -

Sadeq Damrah mail -

Ibraheem Abu Falahah mail -

Abdallah Al-Husban mail -

M. Palanikumar mail

link https://doi.org/10.54216/IJNS.240424

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

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Pure Mathematics for Theoretical Computer Science | 2024

Incorporating Kernels into Convolutional Neural Networks for Enhanced Feature Extraction

The simulation was used to evaluate the method of kernel K , Neural network NN, Convolution Neural network CNN bys using (MINIST) data set. The accuracy of the method was tested and compared with the convolutional neural network as well as with the kernel function for the same input data (training and testing). The results of simulation showed that there is a high accuracy of the method, and at the same time there is a decreasing loss over the epochs, which indicates the. We note high smooth by method for recognize among features.

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Sackineh Shamil Jasim mail

link https://doi.org/10.54216/PMTCS.040102

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2024

Digital Forensic Based Object Recognition for Enhanced Crime Scene Interpretation

This research introduces a novel and comprehensive framework for digital forensics-based crime scene interpretation. The proposed framework comprises five algorithms, each serving a distinct purpose in enhancing image quality, extracting features, matching, and constructing a database, recognizing, and reconstructing objects in 3D, and conducting context-aware analysis. An ablation study validates the necessity of each algorithmic step. The framework consistently outperforms existing methods in terms of accuracy, precision, recall, and processing time. A detailed comparative analysis of parameters further highlights its cost-effectiveness, moderate complexity, superior data integration, and scalability. Visualizations underscore its dominance across multiple metrics and parameters, positioning it as an advanced solution for digital forensic-based object recognition in crime scene interpretation.

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Vikash Kumar Singh mail -

Durga Sivashankar mail -

Siddharth Sriram mail -

Manish Nagpal mail -

Warish Patel mail -

Shweta Loonkar mail

link https://doi.org/10.54216/JISIoT.130201

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

Comprehensive Analysis of Implementation and Evaluation IoT based Techniques in Networked Security Systems

This research introduces an advanced network security methodology based on IoT, combining five innovative algorithms: Dynamic Threat Detection (DTD), Adaptive Intrusion Prevention System (AIPS), Anomaly-Based Security Metrics (ABSM), Context-Aware Firewall (CAF), and Cognitive Security Assessment (CSA). Each algorithm contributes specific functionalities, ranging from real-time threat detection and adaptive policy adjustments to anomaly quantification, contextual rule modifications, and holistic security risk assessments. The ablation study conducted on each algorithm reveals critical components driving their performance, ensuring a deep understanding of their inner workings. The proposed method demonstrates superior performance in accuracy, scalability, usability, and adaptability compared to existing network security methods. Visual representations and a comprehensive evaluation further validate the proposed method's effectiveness, positioning it as an advanced and efficient solution for addressing evolving network security challenges.

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Raenu Kolandaisamy mail -

Suhas Gupta mail -

Shashikant Patil mail -

Jaymeel Shah mail -

Abhinav Mishra mail -

N. Gobi mail

link https://doi.org/10.54216/JISIoT.130203

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

Machine Learning and Internet of Things Driven Energy Optimization in Wireless Sensor Networks through Crossbreed Clustering

Key challenges in Wireless Sensor Networks (WSNs) include reduced dormancy, energy efficacy, reportage worries, and network lifetime. To solve the issues of energy efficiency and network longevity, more study of cluster-based WSNs is required. In order to address the challenges and constraints of WSNs, creative approaches are needed. WSNs use machine-learning techniques because of their unique characteristics. These characteristics include high communication costs, low energy reserves, high mobility, and frequent topological shifts.  The current method picks cluster heads at random at the beginning of each cycle, not considering the remaining energy of these nodes. It is possible that the newly chosen CH nodes will have the lowest energy level in the network and will die off fast as a result. Energy is wasted while communicating over long distances between cluster heads and the BS, which occurs frequently in a big network due to Internet of things. This would mean that WSNs have a finite lifespan. Therefore, to increase the network's longevity and efficiency, we propose a machine-learning-based strategy called energy proficient crossbreed clustering methodology (ECCM). The experimental results reveal that the ECCM is superior to the LEACH approach, increasing residual energy by 35%, extending network lifetime by 37%, and increasing throughput by 15%.  

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Ahmed Saeed Alabed mail -

Rajesh Kumar Samala mail -

Asha KS mail -

Sorabh Sharma mail -

Amit barve mail -

Deepak Minhas mail

link https://doi.org/10.54216/JISIoT.130204

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

Emerging Trends: Nano-Scale Wireless Sensor Networks and Applications

New Adaptive Nano-Scale Sensor Network (ANSN) can quickly feel nanoscale surroundings. ANSN uses data in many scenarios to improve networks, consume less energy, and gain more accurate data. ANSI essentials are covered in detail here. This group has numerous parts. Making service better, collecting data with less energy, sending data with Q-learning, merging sensor data to increase accuracy, controlling power dynamically, and protecting data using AES are examples. Energy collection and sensor use are key to this effort. Academic research has proven that ANSN outperforms other techniques in several areas. Improvements include speed, security, latency, sensor accuracy, and network stability. With these changes, ANSN may be suitable for small wireless sensor networks.

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Julissa E. Reyna-Gonzalez DRA mail -

N. K. Rayaguru mail -

Gowrishankar J. mail -

Bhargavi Gaurav Deshpande mail -

Madhur Grover mail -

Daxa Vekariya mail

link https://doi.org/10.54216/JISIoT.130202

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new