Secure MRI Brain Image Transmission Using IOT Devices Based on Hybrid Autoencoder and Restricted Boltzmann Approach

Building modern Internet of Things (IoT) systems is associated with a number of challenges. One of the most significant among them is the need for wireless technology, which will serve to build connectivity between the individual components of this technology. In the larger cities of Bulgaria, measures to ensure low levels of harmful emissions, reduce noise levels, and ensure comfort in urban environments have been taken. LoRa technology shows more advantages in transmission distance and low energy consumption compared to other technologies. That is why this technology was chosen for the design of wireless sensor networks (WSN) for six cities in Bulgaria. These networks have the potential to be used in IoT configurations. Appropriate modules and devices for building WSN for cities in Bulgaria have been selected. It has been found that the greater number of nodes in the WSN leads to an increase in the average power consumed in the network. On the other hand, depending on the location of these nodes, the energy consumed may decrease. The performance of wireless sensor networks can be optimized by applying appropriate routing protocols, which are proposed in the available literature. The methodology for energy efficiency analysis of WSN can be used in the design of wireless sensor networks to determine the parameters of the environment, with the possibility of application in IoT.

The protection of medical images against unauthorized access and tampering is paramount. This paper presents a robust watermarking framework that integrates Discrete Wavelet Transform (DWT), Hessenberg Decomposition (HMD), Singular Value Decomposition (SVD), and Arnold Scrambling to enhance the security of medical images. By applying DWT to decompose the medical image into frequency subbands and embedding the watermark into the most significant subband, the proposed algorithm ensures minimal impact on image quality. HMD simplifies the subband matrix, while SVD extracts and manipulates the essential features of the image. Arnold Scrambling is employed to further secure the watermark image before embedding. Experimental results on various medical image datasets demonstrate the algorithm’s effectiveness in maintaining imperceptibility, with a peak signal-to-noise ratio (PSNR) of up to 49 dB, and robustness against common image processing attacks, such as compression and noise addition. The proposed scheme achieves a balance between imperceptibility and robustness, making it suitable for securing medical images in digital environments. The proposed scheme has been implemented on different medical datasets and the performance is evaluated in terms of its imperceptibility and robustness. The PSNR value achieved by the proposed work is 49 dB which proves that the embedded watermark image is imperceptible while the NC value achieved is higher than 0.9 against most of the attacks, hence proves its robustness against multiple attacks.

The Internet of Medical Things (IoMT) is reforming healthcare by enabling interconnected medical devices and systems to facilitate efficient data collection, transmission, and analysis. While IoMT has significantly improved real-time monitoring and personalized care, the transmission of high-resolution medical images remains a challenge due to bandwidth constraints, latency issues, data loss, and computational overhead. Efficient and secure medical image transmission is critical to ensuring reliable diagnostics and timely patient care in this ecosystem. This research presents an optimized Deep Learning (DL) architecture developed to overcome the limitations of medical image transmission in IoMT environments. The proposed solution incorporates Convolutional Neural Networks (CNNs) for spatial feature extraction and dimensionality reduction while preserving diagnostic-critical information, and Long Short-Term Memory (LSTM) networks to manage sequential data and mitigate transmission issues such as packet loss and latency. The framework incorporates robust encryption mechanisms to ensure data security without significantly increasing computational overhead. Once predictions are made, the data is securely transferred to the cloud for further analysis and storage. Furthermore, Hippopotamus Optimization is utilised to enhance the model's performance and fine-tune hyperparameters, improving both efficiency and accuracy. Performance evaluations were conducted using real-world medical image datasets under varying IoMT network conditions. The results demonstrate that the proposed CNN-LSTM framework delivers superior performance across key metrics, like Peak Signal-to-Noise Ratio (PSNR), accuracy, F1 score, specificity, and sensitivity. Additionally, the framework optimizes encryption and decryption times and reduces bandwidth consumption, ensuring efficient and secure data transmission. This approach showcases a significant advancement in IoMT-based medical imaging, paving the way for enhanced reliability and efficiency in healthcare delivery systems.

Deep learning has attracted great attention in the medical imaging community as a promising solution for automated, fast and accurate medical image analysis, which is mandatory for quality healthcare. Convolutional neural networks and its variants have become the most preferred and widely used deep learning models in medical image analysis. In this paper, concise overviews of the modern deep learning models applied in medical image analysis are provided and the key tasks performed by deep learning models, i.e. classification, segmentation, retrieval, detection, and registration are reviewed in detail. Some recent researches have shown that deep learning models can outperform medical experts in certain tasks. With the significant breakthroughs made by deep learning methods, it is expected that patients will soon be able to safely and conveniently interact with AI-based medical systems and such intelligent systems will actually improve patient healthcare. There are various complexities and challenges involved in deep learning-based medical image analysis, such as limited datasets. But researchers are actively working in this area to mitigate these challenges and further improve health care with AI.

<span>Wireless sensor network (WSN) has attained wide adoption across various sectors and is considered to be key component of future real-time application such as BigData, Internet of things (IoT) etc. The modern application requires low latency and scalable real-time data access considering heterogeneous network. However, provisioning low latency real-time data access incurs energy overhead among sensor device. Clustering technique aided in providing scalability and minimizing energy consumption among sensor device. However, it incurs energy overhead among cluster head and sensor device closer to sink. To address, many optimization technique is been presented in recent time for optimal cluster selection. However, these technique are designed considering homogenous network. To address, this work presented Low Latency and Energy Efficient Routing (LLEER) design for heterogeneous WSN. The LLEER adopts multi-objective function such as</span><span>connectivity, connection time, radio signal strength, coverage time, and network traffic for cluster head and hop node selection. Experiment are conducted to evaluate LLEER design shows significant performance improvement over state-of-art model in terms of network lifetime considering total node death, first node death, and loss of connectivity, communication overhead, and packet transmission latency. Proposed LLEER brings a good trade-off between energy efficiency, and latency requirement of future real-time application.<span> </span></span>

A scheme for WSNs (wireless sensor networks) security is given by dividing sensing tetrahedron into clusters and using the symmetric polynomials in this paper.The sensing tetrahedron is divided into a number of small grids.All those sensor nodes, both ordinary sensor nodes and heterogeneous sensor nodes are distributed in the sensing tetrahedron.In a grid, all ordinary sensor nodes and heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.All the heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.At last, all sensor nodes establish their keys directly or indirectly in the whole sensing tetrahedron.Analysis and comparison demonstrate this scheme enhances the WSN security, has good network connectivity, saves node storage, reduces network computing load, and extends the network lifetime.

Using wireless sensor networks (WSNs) in delay and reliability critical environments is highly desired due to their unique advantages such as low cost, ease of deployment, and redundancy. However, successful deployment of resource-limited WSNs in such applications requires strict quality-of-service (QoS) provisioning techniques to meet the desired latency and reliability targets of those applications. Implementation of QoS techniques in WSNs is significantly challenging since WSNs have been initially devised for low-data-rate non-real-time applications. Therefore, WSN designers and developers resort to different cross-layer interaction and optimization techniques to provision QoS in WSNs. In this paper, we present a survey on the state of the art of cross-layer QoS approaches in wireless terrestrial sensor networks to achieve delay and reliability bounds in critical applications. Our paper provides a unique classification of cross-layer QoS approaches in WSNs that allows surveying a large amount of studies with utmost clarity. Furthermore, we highlight the main challenges of implementing QoS protocols in WSNs and present an overview of QoS-aware WSN applications.

This paper presents the design of wireless sensor network (WSN) based on low-power wide area network technology for high-tech agriculture. This WSN allows the farmer to collect data such as air temperature, air humidity, soil moisture. The WSN system consists of components: 02 wireless sensor nodes, 01 gateway, 01 cloud server and smartphone app. This WSN tested for data transmission in two zones: zone 1 (dense urban environments) at a distance of 500m and zone 2 (urban environments - less obstacles) at a distance of 1,500m and 1,700m. The data collected at different times of the day and updated every 15 minutes. The results show that the wireless sensor network system operates stably, data constantly updated to LoRa Server and there was not data packet loss. The power consumption of sensor node and gateway determined in three operating modes: transmitting, receiving, turn-off. This shows the advantages of LoRa technology in the development of wireless sensor network which is the distance of data transmission distance and low power consumption. Besides this WSN also tested in the net house of aquaponics of the Research Center for High-tech Application in Agriculture (RCHAA), University of Science, Vietnam National University-HCM. The results show that the WSN system is working reliably and promising which brings significantly benefits to smart agriculture as aquaponics, clean vegetable farms, aquaculture farms…

Image compression plays a significant role in digital image storage and transmission because of limited availability of storage devices space and insufficient bandwidth and is beneficial for all multimedia applications. Magnetic resonance imaging (MRI) of a human body produces an image of huge size and is to be compressed but medical field demands high image quality for better diagnosis of disease. In this technologically advanced world, intelligence systems try to simulate human intelligence. It is applied in the field of engineering, industry, medicine and education problems and it makes decisions by using the several inputs. However, the search process is enormous and convergence time depends on algorithm structure. In this paper first time methaheuristic algorithms are used for near optimum solutions. This paper introduces flower pollination algorithm (FPA)-based vector quantisation for better image compression with better reconstructed image quality. Performance of proposed method is evaluated by using peak signal to noise ratio (PSNR), mean square error (MSE) and fitness function.

In this article, we consider a stochastic model of wireless sensor networks (WSNs) in which each sensor node randomly and alternatively stays in an active mode or a sleep mode. The active mode consists of two phases, called the full-active phase and the semi-active phase. When a referenced sensor node is in the full-active phase of the active mode, it may sense data packets, transmit the sensed packets, receive packets, and relay the received packets. However, when the phase of the sensor node switches from the full active phase to the semi-active phase, it is only able to transmit/relay data. When the referenced sensor node is in a sleep mode, it does not interact with the external world. In this article, first, we develop a stochastic model for the sensor node of a WSN, and then we derive an explicit expression of the stationary distribution of the number of data packets in the sensor node. Furthermore, we figure out some important performance measures, including the sensor node’s energy consumption for transmission, the energy consumption of the sensor operations, and the average energy consumption of the sensor node in a cycle of active and sleep modes. Also, a numerical analysis is provided to validate the proposed model and the results obtained. The novel aspects of our research are the development of a stochastic model for WSN with active and sleep features and the development of important analytical formulae for evaluating the energy consumption of a WSN. These results are expected to be useful as significant contributions to the fundamental theory of the design of various WSNs with active and sleep mode considerations.

The research that used digital image processing to detect glaucoma has been known as one of the popular methods. The beginning step of the research is to choose the best channel among red, green, or blue (RGB) so it can ease the glaucoma segmentation. In choosing the channel, it is important to analyze deeply the used retinal images. Choosing of best component can affect the accuracy of glaucoma diagnosis results. In this research, the most suitable component will be analyzed to detect glaucoma based on the visual, MSE (Mean Square Error) value, and PSNR (Peak Signal to Noise Ratio). In this research, we used 85 images of glaucoma from the DRISTHI-GS database and 101 normal images from the RIM-ONE database. From the visualization, it showed that the red component had high brightness level so it can differ the optic disc and other parts of retinal eyes. The green component still has vessel blood so it will make it more difficult to segment the images. Blue component results in very dark of retinal image. From MSE and PNSR values, it showed that the green component had the smallest MSE value while the blue component has the biggest MSE value. PSNR value was obtained from the green component. Both red and blue components had PSNR value which had a small difference. From these results, it can be concluded that the MSE and PSNR values do not guarantee visual results. So that for further research, it is expected that the MSE and PSNR values will be obtained from the part that we want to observe

This chapter presents a combined approach to visualize the IoT and cyber-physical system in any wireless communication networks. The brief overview of IoT and its applications in wireless sensor network (WSN) are elaborated in current era. In today’s present situation and the challenges faced during COVID-19 pandemic how the things can be monitored through IoT is being focused in this chapter. Also, the threats challenges and monitoring of cyber-physical system for security is presented in analysis form. WSN provides a bridge between the physical and virtual world and finds use in numerous applications like environmental monitoring, civil structural health monitoring and industrial process monitoring. Lately, it has found much use in the latest technology called Internet of Things (IoT). It seems that IoT has a great potential in shaping our future where everything will be connected together all the time. The major challenges faced by WSN include energy-efficiency, heterogeneity, and systematic design. WSN comprise of autonomous sensors which are capable of wirelessly transmitting their sensed parameter-values (also called packets) to remotely 188located sink node. The sink node is a WSN node which has the capability to assemble the data from all the nodes and forward it to farther distances to the remote base station through wired connections. The data transmission from source node to sink node is done by taking multi-hops which means that data is not sent directly to the destination node but through jumping from one intermediate node to another until the sink is reached. The sensor nodes also act as wireless routers which transmit data to other sensor-routers. So, sensor devices are very useful in deployment, collection of data, act as a IoT sensor devices as an application in technology. IoT is a device which can be used as application while a cyber-physical system is a collection of various mechanical components which are controlled and managed by different devices and algorithms. An IFM (Information Flow Monitor) is an application to backtrack queries and find the culprit in an error that has occurred in the cyber system. However, while backtracking and flow monitoring is not always guaranteed in terms of secure and efficient transaction. In some cases, the sender may deny the authority of the data so the information flow monitors may fail to provide a trustful and authentic source of data. Thus, the chapter focuses on both the issues of IoT integration with cyber-physical security (CPS) system. The key challenges and some solutions are proposed for the researchers for future work.

Prior research studies have shown that the peak signal to noise ratio (PSNR) is the most frequent watermarked image quality metric that is used for determining the levels of strength and weakness of watermarking algorithms. Conversely, normalised cross correlation (NCC) is the most common metric used after attacks were applied to a watermarked image to verify the strength of the algorithm used. Many researchers have used these approaches to evaluate their algorithms. These strategies have been used for a long time, however, which unfortunately limits the value of PSNR and NCC in reflecting the strength and weakness of the watermarking algorithms. This paper considers this issue to determine the threshold values of these two parameters in reflecting the amount of strength and weakness of the watermarking algorithms. We used our novel watermarking technique for embedding four watermarks in intermediate significant bits (ISB) of six image files one-by-one through replacing the image pixels with new pixels and, at the same time, keeping the new pixels very close to the original pixels. This approach gains an improved robustness based on the PSNR and NCC values that were gathered. A neural network model was built that uses the image quality metrics (PSNR and NCC) values obtained from the watermarking of six grey-scale images that use ISB as the desired output and that are trained for each watermarked image’s PSNR and NCC. The neural network predicts the watermarked image’s PSNR together with NCC after the attacks when a portion of the output of the same or different types of image quality metrics (PSNR and NCC) are obtained. The results indicate that the NCC metric fluctuates before the PSNR values deteriorate.

Background & Objective: The Internet of things (IoT) refers to the connection of smart electronics devices to the Internet which enables these devices to collect and exchange data. Wireless sensor networks (WSNs) are effectively connected in an ad-hoc manner which consists of collaborative sensing devices that have limited resources capabilities. The sensor nodes can work together to organize real-time monitoring, sensing, and collecting information about the various environments within the event area. Method: Connecting WSNs to the internet will allow new generations of WSN applications to appear. One of the main standards of Internet of Things (IoT) over WSN is IPv6 over Low-power Wireless Personal Area Network (6LoWPAN) which has been designed to enable low-power wireless personal area network devices to integrate with IPv6 networks. This paper presents a 6LoWPAN test bed which has been conducted using MICAZ and TELOSB platforms. It implements TCP/IPv6 layers using Berkeley IP (BLIP) in TELOSB and MICAZ platforms. Conclusion: The finding in this paper opens up new Internet applications over WSNs. Keywords: Internet of Things, 6LoWPAN, WSNs, BLIP, electronic devices, sensor nodes.

A common challenge in the implementation of Internet of Things (IOT) Wireless Sensor Networks (WSN) is that the sensor nodes are known to be power hungry devices. The energy stored in the batteries which power up these sensor nodes deplete quickly especially when more data is transmitted to the cloud or when multiple sensors are attached to a single sensor node. In the context of flood and environmental monitoring, increasing the number of sensor nodes in a Wireless Sensor Network is desirable in order to increase the spatial resolution of the data and hence achieve better representation about rising water levels and overall water quality in a particular city. However, having more sensor nodes in a Wireless Sensor Network results in more challenges for the power supply management of the overall Wireless Sensor Network. A drawback of the sensor nodes which are usually powered by Lithium Ion batteries is that there is a limited number of cycles in which a battery can be charged and discharged before the battery is considered to be fully degraded and therefore methods which can lengthen the duration of each charging and discharging cycle will be useful to increase the overall battery longevity. In this paper, a method which combines solar energy harvesting together with a fuzzy logic based algorithm for adaptive sampling is proposed in order to achieve a continuous source of energy for the sensor nodes and also increase the duration between each charging and discharging cycle resulting in batteries which can last for a longer duration. The developed sensor nodes have been deployed to measure river water levels and dissolved oxygen.