Two-way image communication in a wireless channel needs to be viable with channel properties such as transfer speed, energy-effective, time usage, and security because image capability consumes a huge space in the gadget and is quite effective. Is required in a manner. The figure goes through attacks. In addition, the quiesical issue for additional time of pressure is that the auxiliary interaction of pressure occurs through the dewar receiving extra time. To address these issues, compressed sensing emerges, which packs the image into hours of sensing, is generated in an expedient manner that reduces time usage and saves the use of data transfer capability, however Bomb in transmission. A variety of examinations cleared a way for dealing with security issues in compressive sensing (CS) through giving security as an alternative negotiation. In addition, univariate factors opted for CS as the issue of rearranging image quality is because of the aggregation of clutter. Along these lines related to the above issues, this paper proposed two-way image transmission to the Corvus Coron module, which presents an energy-effective with the CS model, as an inbuilt interaction in the CS transmission through the security framework. Receives what was designated as the pack-protected plot. Impeccable entertainment with the famous arbitrary network conjecture in CS. The result of the test is that the practical module presents energy-efficient and conserved transmission in the form of low error rate with low computational time.

Intelligent Secured Two-Way Image Transmission Using Corvus Corone Module over WSN

Since the last decade, cloud-based electronic health records (EHRs) have gained significant attention to enable remote patient monitoring. The recent development of Healthcare 4.0 using the Internet of Things (IoT) components and cloud computing to access medical operations remotely has gained the researcher's attention from a smart city perspective. Healthcare 4.0 mainly consisted of periodic medical data sensing, aggregation, data transmission, data sharing, and data storage. The sensitive and personal data of patients lead to several challenges while protecting it from hackers. Therefore storing, accessing, and sharing the patient medical information on the cloud needs security attention that data should not be compromised by the authorized user's components of E-healthcare systems. To achieve secure medical data storage, sharing, and accessing in cloud service provider, several cryptography algorithms are designed so far. However, such conventional solutions failed to achieve the trade-off between the requirements of EHR security solutions such as computational efficiency, service side verification, user side verifications, without the trusted third party, and strong security. Blockchain-based security solutions gained significant attention in the recent past due to the ability to provide strong security for data storage and sharing with the minimum computation efforts. The blockchain made focused on bitcoin technology among the researchers. Utilizing the blockchain which secure healthcare records management has been of recent interest. This paper presents the systematic study of modern blockchain-based solutions for securing medical data with or without cloud computing. We implement and evaluate the different methods using blockchain in this paper. According to the research studies, the research gaps, challenges, and future roadmap are the outcomes of this paper that boost emerging Healthcare 4.0 technology. 

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Integration of Healthcare 4.0 and blockchain into secure cloud-based electronic health records systems

A cross-flow wind turbine has a high torque coefficient at a low tip speed ratio; therefore, it is a good candidate for a self-starting turbine. This study aims to investigate the best configuration between guide vanes and cross-flow vertical axis wind turbine. The experiment test was carried out to determine the turbine with the highest power coefficient. The cross-flow turbine has 14, 18, and 22 blades with using 6,10 and 14 blades of guide vane (GV) was developed in this study, employing 15°, 25°, 35°, 45°, 55°, 65°, and 75° of tilt angles in fifth different wind speed conditions 4 m/s, 6 m/s, 7.5 m/s, 9.20 m/s, and 11 m/s. The turbine has 22 blades with 14 GV blades at 55° of tilt angle blades producing more remarkable turbine performance improvement than other blades. The highest power coefficient (CP) of cross-flow using 14 GV blades at 55° was 0.0162 at 0.289 TSR, which increased around 59% of the turbine's performance using GV.

Study the influence of using guide vanes blades on the performance of cross-flow wind turbine

Deepfake network is a prominent topic of research as an application to various systems about security measures. Although there have been many recent advancements in facial reconstruction, the greatest challenge to overcome has been the means of finding an efficient and quick way to compute facial similarities or matches. This work is created utilizing the rationale-augmented convolutional neural network (CNN) on MATLAB R2019a platform using the Kaggle DeepFake Video dataset with an accuracy of 95.77%. Hence, real-time deepfake facial reconstruction for security purposes is difficult to complete concerning limited hardware and efficiency. This research paper looks into rational augmented CNN state-of-the-art technology utilized for deepfake facial reconstruction via hardware such as webcams and security cameras in real time. Additionally, discuss a history of face reconstruction and provide an overview of how it is accomplished.

Deepfake detection using rationale-augmented convolutional neural network

The Internet of Things (IoT) is a subclass of the Industry 4.0 standard. The functionality of IoT depends on the Wireless Sensor Networks (WSNs) design. The IoT-empowered WSNs received the researcher's attention for the Smart Farming (SF) applications. SF nowadays is required to enhance farm productivity while minimizing the cost and resources. The agriculture sensors devices disposed over the farm collect the on-field farm data and transfer it wirelessly to the base station for decision-making and agriculture monitoring. As the nodes are resource restrained, the process of periodic farm data gathering and multi-hop delivery needs to be effective in terms of Quality of Service (QoS) and energy-efficiency of information transmission by reflecting the long-distance transmission difficulties of SF applications. To enhance the network lifetime substantially of densely deployed WSN for periodically monitoring of farm conditions, we propose a novel Nature-Inspired algorithm-based Cross-layer Clustering (NICC) protocol. We design NICC to find a reasonably better solution for clustering and routing in SF applications. NICC explores the idea of a nature-inspired optimization algorithm called Bacterial Foraging Optimization (BFO) with optimal fitness function, which models the trade-off among the energy efficiency and optimal data transmission. We design a BFO algorithm to select the optimal sensor node for clustering and routing problems based on cross-layer parameters-based fitness value computation. The cross-layer parameter includes the sensor parameters from layers like network layer, physical layer, and Medium Access Control (MAC). The numerical results show the superiority of the NICC protocol for various WSN-assisted SF scenarios against state-of-art clustering techniques.

Cross-Layer Protocol for WSN-Assisted IoT Smart Farming Applications Using Nature Inspired Algorithm

Internet of Things (IoT) for Intelligent Manufacturing of Smart Farming gained significant attention from researchers to automate various farming applications called Smart Farming (SF). The sensors and actuators deployed across the farm using which farmers receive periodic farm information related to temperature, soil moisture, light intensity, and water used, etc. The clustering-based methods are proven energy-efficient solutions for Wireless Sensor Networks (WSNs). However, by considering long-distance communications and scalable networks of IoT enabled SF; the present clustering solutions cannot be feasible and having higher delay and latency for various SF applications. To focus on requirements SF applications, an efficient and scalable protocol for remote monitoring and decision making of farms in rural regions called CL-IoT protocol proposed. A cross-layer-based clustering and routing algorithms have designed to reduce network communication delay, latency, and energy consumption. The cross-layer-based optimal Cluster Head (CH) selection solution proposed to overcome the energy asymmetry problem in WSN. The parameters of different layers like a physical, medium access control (MAC), and network layer of each sensor used to evaluate and select optimal CH and efficient data transmission. The nature-inspired algorithm proposed with a novel probabilistic decision rule functions as a fitness function to discover the optimal route for data transmission. The performance of the CL-IoT protocol analyzed using NS2 by considering the energy-efficiency, computational-efficiency, and QoS-efficiency factors. Compared to state-of-art IoT-based farming methods, the CL-IoT reduces energy consumption, communication overhead, and end-to-end delay up to a certain extent and maximizes the network throughput.

CL-IoT: cross-layer Internet of Things protocol for intelligent manufacturing of smart farming

The emergence of the Industry 4.0 revolution to upgrade the Internet of Things (IoT) standards provides the prominence outcomes for the future wireless communication systems called 5G. The development of 5G green communication systems suffers from the various challenges to fulfill the requirement of higher user capacity, network speed, minimum cost, and reduced resource consumption. The use of 5G standards for Industry 4.0 applications will increase data rate performance and connected device's reliability. Since the arrival of novel Covid-19 disease, there is a higher demand for smart healthcare systems worldwide. However, designing the 5G communication systems has the research challenges like optimum resource utilization, mobility management, cost-efficiency, interference management, spectral efficiency, etc. The rapid development of Artificial Intelligence (AI) across the different formats brings performance enhancement compared to conventional techniques. Therefore, introducing the AI into 5G standards will optimize the performances further considering the various end-user applications. We first present the survey of the terms like 5G standard, Industry 4.0, and some recent works for future wireless communications. The purpose is to explore the current research problems using the 5G technology. We further propose the novel architecture for smart healthcare systems using the 5G and Industry 4.0 standards. We design and implement that proposed model using the Network Simulator (NS2) to investigate the current 5G methods. The simulation results show that current 5G methods for resource management and interference management suffer from the challenges like performance trade-offs. 

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Hemant B. Mahajan

Papers

Intelligent Secured Two-Way Image Transmission Using Corvus Corone Module over WSN

CL-IoT: cross-layer Internet of Things protocol for intelligent manufacturing of smart farming

Integration of Healthcare 4.0 and blockchain into secure cloud-based electronic health records systems

Cross-Layer Protocol for WSN-Assisted IoT Smart Farming Applications Using Nature Inspired Algorithm

5G standards for the Industry 4.0 enabled communication systems using artificial intelligence: perspective of smart healthcare system