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The Various Challenges Involved in Sensor Based Cloud System to Protect the Data and to Avoid Attacks: A Technical Review
In these studies, we introduce a unique protection framework for the integration of Wireless Sensor Networks (WSN) with cloud computing, aimed closer to enhancing statistics-centric programs consisting of far-flung healthcare structures. The framework's cornerstone is a robust, bendy safety version that ensures immoderate-degree information confidentiality, integrity, and terrific-grained get proper of access to control, addressing the important protection demanding situations in WSN-Cloud integration. By the use of a hybrid encryption mechanism that mixes the strengths of symmetric and uneven encryption techniques, our method gives a entire safety answer that protects information during transmission and garage. Furthermore, the version includes an efficient key manipulate gadget, facilitating the dynamic era and relaxed distribution of encryption keys. This contemporary framework is designed to mitigate common safety threats, such as Man-in-the-Middle (MITM) and Denial of Service (DoS) attacks, even as preserving the overall performance and standard performance of the blanketed gadget. Our research offers a massive contribution to securing statistics-centric packages in WSN-Cloud ecosystems, making sure dependable and comfortable facts verbal exchange and get right of entry to for a way off healthcare programs and past. 2024 IEEE. -
Machine Learning-Driven Energy Management for Electric Vehicles in Renewable Microgrids
The surge in demand for sustainable transportation has accelerated the adoption of electric vehicles (EVs). Despite their benefits, EVs face challenges such as limited driving range and frequent recharging needs. Addressing these issues, innovative energy optimization techniques have emerged, prominently featuring machine learning-driven solutions. This paper reviews work in the areas of Smart EV energy optimization systems that leverage machine learning to analyse historical driving data. By understanding driving patterns, road conditions, weather, and traffic, these systems can predict and optimize EV energy consumption, thereby minimizing waste and extending driving range. Concurrently, renewable microgrids present a promising avenue for bolstering power system security, reliability, and operation. Incorporating diverse renewable sources, these microgrids play a pivotal role in curbing greenhouse gas emissions and enhancing efficiency. The review also delves into machine learning-based energy management in renewable microgrids with a focus on reconfigurable structures. Advanced techniques, such as support vector machines, are employed to model and estimate the charging demand of hybrid electric vehicles (HEVs). Through strategic charging scenarios and innovative optimization methods, these approaches demonstrate significant improvements in microgrid operation costs and charging demand prediction accuracy. The Authors, published by EDP Sciences, 2024. -
High Gain Miniature Antenna Arrays for 2.4 GHz Applications
In this paper, miniature corporate feed Four Element Array (FEA), Eight Element Array (EEA) and Sixteen Element Array (SEA) are presented. The proposed antenna arrays are created on Rogers Duroid 5880 substrate with permittivity 2.2 and thickness of 0.782 mm. Initially, a single element antenna was created, then it was used in a corporate feed network designed for the 4-element array. As an extension, the 4-element array was used as a template and created an 8-element array and 16-element array to achieve high gain and directivity at 2.4 GHz. The proposed FEA, EEA, and SEA exhibit reflection coefficients of -25.55 dB, -37.14 dB, and -30.61 dB respectively. The peak gains obtained are 11.5 dB, 13.67 dB, and 16.76 dB respectively for FEA, EEA, and SEA. Also, the directivity has improved corresponding to the increase in the number of elements. Therefore, it can be a suitable candidate for applicationswhere extended range and coverage with better signal quality and higher data transfer rates is a priority. 2024 IEEE. -
Novel hybrid metamaterial to improve the performance of a beamforming antenna
This paper investigates the design and implementation of a novel hybrid metamaterial unit cell to improve a beamforming Wi-Fi antenna's performance. The proposed metamaterial unit cell is created on an FR-4 substrate (?? = 4.4) and a thickness of 1.6 mm. The metallization height of the unit cell is maintained at 0.035 mm. The designed metamaterial unit cell is simulated using HFSS Ver. 18.2 to verify the double negative behaviour. The unit cell consists of five Split Ring Resonators (SRR's) at the bottom and a hexagonal ring of six triangles. Initially, a conventional inset fed microstrip patch antenna is designed then an array of the proposed unit cell is created and used as a superstrate to study the performance. A Three Element Antenna Array (TEAA) is designed to operate at 2.4 GHz Wi-Fi band, and the superstrate created out of the proposed unit cell is used to study its effect. Metamaterial superstrate improved the conventional Single Element Antenna (SEA) gain by approximately 2 dB. Superstrate with TEAA exhibited an improved gain of 1 dB over TEAA. Published under licence by IOP Publishing Ltd. -
Gain and bandwidth enhancement by optimizing four elements corporate-fed microstrip array for 2.4GHz applications
This paper presents the performance analysis of an optimized corporate-fed Rectangular Microstrip Antenna Array of four elements and Rectangular Microstrip Antenna array with Semi-Circular Tabs on the nonradiating edges of each element of the array to operate at 2.4 GHz, with detailed steps of the design process. The proposed antenna structures have been designed using FR4 dielectric substrate having a permittivity ?r of 4.4 with a thickness of 1.6 mm. The simulations have been carried out by using Antenna simulator HFSS version 15.0.0 and performance was analyzed for gain, bandwidth, VSWR, return loss and radiation pattern. The gain of these simulated antenna arrays is 2.4381 dB, 8.2684 dB and 8.5621 dB with a return loss of ?22.4123 dB, ?14.1095 dB and ?15.7621 dB for Single-Element patch, conventional Rectangular Microstrip array and Rectangular Microstrip Antenna array with semicircular tabs respectively at 2.4 GHz. Bandwidths exhibited by Single-Element patch, RMSACT and RMSA are 59.8 MHz, 83.9 MHz, and 212.7 MHz, respectively. 2020, Springer Nature Singapore Pte Ltd. -
Miniaturization of Microstrip Antenna with Enhanced Gain Using Defected Ground Structures
The rapid advancement and growth in the wireless technology demands miniaturized communication equipment's. Microstrip antennas attracted many researchers over the past decades because of its various features like small in size, light weight, low cost and conformability. These antennas can operate at high frequencies and multiple bands with high gain and larger bandwidths if suitably designed. This work presents a Rectangular Microstrip Antenna (RMSA)performance improvement using defected Ground Structures (DGS). The simulation results revealed that the creation of Complementary Split Ring Resonator (CSRR)and Phi as a defect in the ground of proposed antenna has improved its gain. Introduction of DGS improved the gain by 27% and reduced the size by approximately 3.35%. Proposed Rectangular Microstrip Antenna with Defected Ground (RMSA-DGS)exhibits gain of 3 dB at 2.4 GHz with S11 response of -30.44 dB. In addition to this the antenna also shows one more resonance at 4.66 GHz with S11 of -14.29 dB and gain of -1.24 dB. RMSA-DGS has an overall dimension of 37.2 47.23 mm2. 2019 IEEE. -
Demystifying Data Justice: Legal Response To India's Privacy And Security Standards: Challenges In Cloud Computing
Data is the new oil of this economy. Cloud Computing acts in the capacity of storing databases, in operational analytics, networking and intelligence. Indian cloud computing market is valued at 2.2 billion dollars, which is said to scale by 30 percent in 2022. It's therefore pertinent to understand Indian's data protection landscape in the light of Personal Data Protection Bill, 2018 to answer the questions of ownership, controlling, processing of data in order to reflect upon the liability, obligations, and compliances by intermediaries, dispute resolution forums, data portability and indemnification. The authors will explore by means of doctrinal method, the challenges posed on the content regulatory mechanism for the internet architecture which paves responsibility of data classification into lawful and unlawful, with the exception of section 79 of Information Technology Act. The authors will further examine the encryption standard tools exhibiting data security and the obstacles created by the 40-bit limit encryption standard as part of the DoT's telecom licensing conditions and section 84A IT Act, 2008, to provide suggestions towards pragmatic delimitation. Cloud computing being the next growth frontier of the IT industry, makes it more evident to enable cloud forensics in entrusting with investigations and establishing confidence within the end-users. Goal 16 of SDG's deal with Promote just, peaceful and inclusive societies. The Electrochemical Society -
Irreducible tensor approach to study ? + d ? d + ? 0
The study of photoproduction of mesons plays an important role in understanding the properties of strong interactions. Pion photoproduction on deuterons has been studied theoretically for several decades. At the VEPP - 3 storage rings, tensor analysing powers in ? + d ? d + ?0 have recently been measured. In light of these advances, we suggest adopting an irreducible tensor technique to explore the reaction ? + d ? d + ?0 at close to threshold energies. Our method, which is model-independent, works well for predictions regarding spin observables. By describing the differential cross section in terms of multipole amplitudes, the angular dependence of the cross section will be studied. 2023 Author(s). -
Theoretical Studies on Pion Photoproduction on Deuterons
The study of nuclear reactions between elementary particles and atomic nuclei plays an important role in understanding the interdisciplinary area of Nuclear Physics and Particle Physics. The study of photoproduction of mesons has a long history going back to 19500s. It was in the next decade, studies on photoproduction of ? meson on deuteron started. Since then coherent and incoherent photoproduction of ? meson on deuteron have been studied theoretically and experimentally. The study of photoproduction of pions describes the coupling among photon, meson and nucleon fields and also gives information about strong interactions that indirectly hold the nucleus together. A thorough investigation of the photoproduction process is firmly believed to give first hand information on two important aspects, one being the threshold of ? photoproduction amplitude and the other being propagation of low-energy pions in nuclear medium. The purpose of the present contribution is to theoretically study pion photoproduction on deuterons using model independent irreducible tensor formalism developed earlier to study the photodisintegration of deuterons[1]. Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0) -
ByWalk: Unriddling Blind Overtake Scenario with Frugal Safety System
Safety is crucial, and the truth is ineluctable with its practicality. We strive forward to rev up the safety protocols even more in the field of Road Safety in particular. Countries like India face around 5,00,000 accidents, which lead to 1,80,000 demises each year. The two-lane one-way roads present a risk of the overtaking vehicle crashing onto an incoming car (from the opposite direction) that the overtaking vehicle is unaware of. We seek to achieve two equivocal milestones with our idea in the blind overtake issue, namely, technological aid and economic feasibility. This makes our concept equally impactful in all situations. The technological precision and advancement will help anyone with enough resources to use them tangibly, and economic feasibility ensures a threshold of safety levels that must be put into action. In fact, we are slightly inclined toward the frugality of the architecture paradigm of our idea because safety is everyones right. On the economic side, we propose an LED board-based solution that presents enough information about the incoming vehicle with which a blind overtake condition can be avoided. Besides, we put forward the idea of vehicle-to-vehicle communication for streaming the video content to the trailing cars with smarter selection and added ease to the drivers. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
The Development of ID System for Detecting Attacks in WSN Through Ontology Method and its Strategy
Cybercriminals are becoming increasingly targeted by the rapid expansion of the Internet of Things (IoT), leading to an increase in cyberattacks targeting IoT devices and their communication channels These attacks, if failure to detect may result in significant service disruption, financial loss or damage to sensitive data. Real-time intrusion detection is essential to ensure reliability, security and profitability of IoT applications. This study introduces a new intrusion detection system designed for IoT devices that uses deep learning (DL). Utilizing ontology in wireless sensor networks (WSN), this intelligent system detects suspicious activities that pose a threat to connected IoT devices with configuration-neutral design provides ease of use, while the test performance analysis is simulated and real-world. It highlights its strong performance in determining admissions. The effectiveness of the system against many types of attacks such as denial of service, workholes, blackholes, opportunistic service attacks, etc. is confirmed by experimental research and furthermore, the system achieves F1 scores, accuracy and the number of memories. This advanced deep learning intrusion detection system shows great promise to improve IoT network security due to its high detection rate. 2024 IEEE. -
Securing Trust in the Connected World: Exploring IoT Security for Privacy in Connected Environments
This abstract delves into IoT security measures to ensure privacy in connected environments. It examines encryption, authentication, access control, and data privacy techniques. Key considerations include end-to-end security, vulnerability mitigation, regulatory compliance, and user trust. By addressing these challenges, trust can be established in the connected world, enabling the widespread adoption of IoT technologies while safeguarding user privacy. 2024 IEEE. -
TumorInsight: GAN-Augmented Deep Learning for Precise Brain Tumor Detection
In addition to the shortage in data as well as the low quality of MRI images, one of the most difficult tasks in contemporary medical imaging is the diagnosis of tumors in brain. This work presents a new approach to enhance diagnostic accuracy using sophisticated preprocessing techniques. Combining BRATS 2023 and Cheng et al. datasets to apply cutting-edge deep learning preprocessing methods with Generative Adversarial Networks (GANs), specifically DCGAN, Contrast Limited Adaptive Histogram Equalization (CLAHE), and gamma correction, it aims to significantly improve the quality of MRI images. As a result, updated data should be generated with greater precision and detail, making it possible to identify tumor-affected areas with greater accuracy. Thorough assessment, demonstrated by metrics such as Accuracy (0.98), Specificity (0.99), Sensitivity (0.99), AUC (0.65), Dice Coefficient (0.67), and Precision (0.71), highlights possible advancements in brain tumor identification and treatment, thereby highlighting the effectiveness of the suggested approach. 2024 IEEE. -
Medical Ultrasound Image Segmentation Using U-Net Architecture
This research article discusses the implementation aspects of a Deep Learning architecture based on U-Net for medical image segmentation. A base model of the U-Net architecture is extended and experimented. Unlike the existing model, the input images are enhanced by applying a Non-Local Means filter optimized using a metaheuristic Grey wolf optimization method. Further, the model parameters are modified to achieve better performance. Tests were performed using two benchmark B-mode Ultrasound image datasets of 200 Breast lesion images and 504 Skeletal images. Experimental results demonstrate that the modifications resulted in more accurate segmentation. The performance of the modified implementation is compared with the base model and a Bidirectional Convolutional LSTM architecture. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Role of Filters in Speckle Reduction in Medical Ultrasound Images- A Comparative Study
To diagnose and predict complex disorders in human body, various Medical Imaging Techniques are used. Widely accepted technique among them is the Ultrasound imaging modality, because of its low cost and noninvasive nature. But the images produced by ultrasound scanning are of low quality and amenable to faster degradation due to the presence of speckle noise. This led to various studies for effectively removing speckle noise from ultrasound images. In this paper, an endeavor is made for a comparative analysis of chosen set of post filtering methods for Speckle reduction, VIZ Anisotropic Diffusion, Wavelet, Adaptive Median Filter, Hybrid Algorithm, Modified Fourier Transform and Sparse Code Shrinkage using ICA. The different methods are tested on a collection of ultrasound images and their performance evaluated with the Normalized Cross Correlation metric (NCC), Peak Signal to Noise Ratio (PSNR), Structural Content (SC), Universal Quality Index (UQI), Edge Preservation Index (EPI) and Structural Similarity Index (SSI). Further relative execution time of different approaches are also analyzed. On analysis of the values of different metrics and execution time, Wavelet Based Hybrid Thresholding is found to outperform the other filters considered. 2019 IEEE. -
A Hybrid Grayscale Image Scrambling Framework Using Block Minimization and Arnold Transform
Image disarranging is the process of randomly rearranging picture elements to make the visibility unreadable and break the link among neighboring elements. Pixel values often don't change while they are being scrambled. There has been a slew of proposed image encryption techniques recently. The two steps that most image encryption algorithms go through are confusion and diffusion. Using a scrambling technique, the pixel positions are permuted during the confusion phase, and an inverse-able function is used to modify the pixel values during the diffusion phase. A good scrambling method practically eliminates the high relationships between adjacent pixels in a picture. In the proposed scheme, XOR based minimization operator is applied on blocks of images followed by Arnold Transform. The suggested design is assessed using a matrix comprising the Structured Similarity Index and the Peak Signal to Noise Ratio. The computed PSNR value less than 10 indicates the input image and scrambled image has high variation. The SSIM value nearer to 0 indicates no similarity in the structure of the input image and scrambled image. 2024 IEEE. -
A Two-Pass Hybrid Mean and Median Framework for Eliminating Impulse Noise From a Grayscale Image
In a digital era, Image recuperation plays a vital role in the area of digital image processing. Image instauration offers more visualization on the quality of the image thereby eliminating noise. Elimination of Gaussian and impulse noise is a challenging problem in the area of image restoration. Rigorous research is pursued to restore salt-and-pepper (SAP) noise utilizing spatial filters. Mean and Median are two contributing spatial filters for eliminating impulse noise. This paper applies a two-pass hybrid mean and median framework on a corrupted grayscale image to replace salt and pepper noise. The hybrid framework is effectively restoring the image by abstracting the low, medium, and high-density impulse noise. The efficacy of the recommended strategy is evaluated by quantifying the peak signal to noise ratio and structural similarity index metric. The result obtained when compared with recent recuperation strategies outperforms to remove noise from grayscale images. 2021 IEEE -
A Cooperative Global Sequencing Algorithm for Distributed Wireless Sensor Networks
Data gathering is a very fundamental use for wireless sensor networks. The area to be monitored has sensor units distributed. They can tell how much demand there is. Temperature, pressure, humidity, sun rays, and other factors could be involved. The detected data is sent to a centralized device called a sink or just a base station. Networks are frequently distributed in character, meaning that more than one kind of instrument is placed in a particular area. There is only one kind of component in uniform networks. A tree is created and anchored at the sink after the nodes have been distributed. In distributed networks, flawless aggregation is challenging to accomplish. In contrast to uniform networks, nodes may receive and transmit multiple types of packets. Every message should be forwarded by the node to a parent so that it can be combined in order to increase the likelihood of aggregation. As a result, a node might need to choose more than one progenitor. This implies that various parameters should be taken into account while forming trees. We have improved the literature's suggested combined distributed scheduling and tree generation for distributed networks. We discover that the expanded method maximizes aggregation, schedules the network with fewer time slots, and uses less energy. Additionally, it is discovered that distributed networks require more management costs to schedule than uniform networks do. 2023 IEEE. -
Building an International Entrepreneurship Index using the PSR framework
This paper builds an International Index for Entrepreneurship (IIE) for the year 2018, by using a conceptual framework named PSR (Pressures-State-Response) to encapsulate the contextual aspect of entrepreneurship globally. In the past, the indices have used a methodological framework of composite indices. This paper uses the PSR framework to show how these indicators fall into the categories of pressure, state, and response, and concentrates on how these subsystems are interrelated. The study considers 41 countries for the construction of the index. We also check the correlation between the IIE and other growth indicators such as the corruption perception index, the economic freedom summary index, GDP per capita, and trade openness using suitable statistical tools.The correlation analysis demonstrates that the IIE and the Economic Freedom Summary Index have a positive association. 2022 IEEE. -
Enhancing Software Cost Estimation using COCOMO Cost Driver Features with Battle Royale Optimization and Quantum Ensemble Meta-Regression Technique
This research suggests a unique method for improving software cost estimates by combining Battle Royale Optimisation (BRO) and Quantum Ensemble Meta-Regression Technique (QEMRT) with COCOMO cost driver characteristics. The strengths of these three strategies are combined in the suggested strategy to increase the accuracy of software cost estimation. The COCOMO model is a popular software cost-estimating methodology that considers several cost factors. BRO is a metaheuristic algorithm that mimics the process of the fittest people being selected naturally and was inspired by the Battle Royale video game. The benefits of quantum computing and ensemble learning are combined in the machine learning approach known as QEMRT. Using a correlation-based feature selection technique, we first identified the most important COCOMO cost drivers in our study. To get the best-fit model, we then used BRO to optimize the weights of these cost drivers. To further increase the estimation's accuracy, QEMRT was utilized to meta-regress the optimized model. The suggested method was tested on two datasets for software cost estimating that are available to the public, and the outcomes were compared with other cutting-edge approaches. The experimental findings demonstrated that our suggested strategy beat the other approaches in terms of accuracy, robustness, and stability. In conclusion, the suggested method offers a viable strategy for improving the accuracy of software cost estimation, which might help software development organizations by improving project planning and resource allocation. 2023 IEEE.