Multimedia encryption innovation is one of the primary ways of securely and privately guaranteeing the security of media transmission. There are many advantages when utilizing the attributes of chaos, for example, arbitrariness, consistency, ergodicity, and initial condition affectability, for any covert multimedia transmission. Additionally, many more benefits can be introduced with the exceptional space compliance, unique information, and processing capability of real mitochondrial deoxyribonucleic acid (mtDNA). In this article, color image encryption employs a confusion process based on a hybrid chaotic map, first to split each channel of color images into n-clusters; then to create global shuffling over the whole image; and finally, to apply intrapixel shuffling in each cluster, which results in very disordered pixels in the encrypted image. Then, it utilizes the rationale of human mitochondrial genome mtDNA to diffuse the previously confused pixel values. Hypothetical examination and trial results demonstrate that the anticipated scheme exhibits outstanding encryption, as well as successfully opposes chosen/known plain text, statistical, and differential attacks.
Modern multimedia communications technology requirements have raised security standards, which allows for enormous development in security standards. This article presents an innovative symmetric cryptosystem that depends on the hybrid chaotic Lorenz diffusion stage and DNA confusion stage. It involves two identical encryption and decryption algorithms, which simplifies the implementation of transmitting and receiving schemes of images securely as a bijective system. Both schemes utilize two distinctive non-consecutive chaotic diffusion stages and one DNA scrambling stage in between. The generation of the coded secret bit stream employs a hybrid chaotic system, which is employed to encrypt or decrypt the transmitted image and is utilized in the diffusion process to dissipate the redundancy in the original transmitted image statistics. The transmitted image is divided into eight scrambled matrices according to the position of the pixel in every splitting matrix. Each binary matrix is converted using a different conversion rule in the Watson–Crick rules. The DNA confusion stage is applied to increase the complexity of the correlation between the transmitted image and the utilized key. These stages allow the proposed image encryption scheme to be more robust against chosen/known plaintext attacks, differential attacks, cipher image attacks, and information entropy. The system was revealed to be more sensitive against minimal change in the generated secret key. The analysis proves that the system has superior statistical properties, bulkier key space, better plain text sensitivity, and improved key sensitivity compared with former schemes.
In contemporary wireless communication systems, the multiple-input and multiple-output systems are extensively utilized due to their enhanced spectral efficiency and diversity. Densely packed antenna arrays play an important role in such systems to enhance their spatial diversity, array gain, and beam scanning capabilities. In this article, a slotted meta-material decoupling slab (S-MTM-DS) with dual reflexes slotted E-shapes and an inductive stub is proposed. Its function was validated when located between two microstrip patch antenna elements to reduce the inter-element spacing, the mutual coupling, the return losses, and manufacturing costs due to size reduction. A prototype is simply fabricated in a volume of 67.41 × 33.49 × 1.6 mm3 and frequency-span measured from 8.4:11 GHz. At 9.4 GHz frequency, the spaces between the transmitting elements are decreased to 0.57 of the free space wavelength. When the proposed isolation S-MTM-DS is applied, the average isolation among them is measured to be −36 dB, the operational bandwidth is enhanced to be 1.512 GHz, the fractional bandwidth improved to be 16.04%, and the return losses are decreased to be −26.5 dB at 9.4 GHz center frequency. Consequently, the proposed design has the potential to be implemented simply in wireless contemporary communication schemes.
In today’s technological and stressful world, when everyone is busy in their daily routines and places blind faith in pharmaceutical advancements to protect their health, the sudden, horrifying effects of the COVID-19 pandemic have resulted in serious emotional and psychological impacts in the general population. In spite of advanced vaccination campaigns, fear and hesitation have become a part of human life since there are a number of people who do not want to take these immunity boosting vaccinations. Such people may become carriers of infectious viruses, leading to a more rapid rate of spread; therefore, this class of spreaders needs to be screened at the earliest opportunity. In this context, there is a need for advanced health monitoring systems which can assist the pharmaceutical industry to monitor and record the health status of people. To address this need and reduce the uncertainty of the situation, this study has designed and tested an Internet of Things (IoT) and Fog computing-based multi-node architecture was for real-time initial screening and recording of such subjects. The proposed system was able to record current body temperature and location coordinates along with the facial images. Further, the proposed system was able to transmit data to a cloud database using internet-connected services. An implementation and reviews-based working environment analysis was conducted to determine the efficacy of the proposed system. It was observed from the statistical analysis that the proposed IoT Fog-enabled ecosystem could be utilized efficiently.
The use of information technology and technological medical devices has contributed significantly to the transformation of healthcare. Despite that, many problems have arisen in diagnosing or predicting diseases, either as a result of human errors or lack of accuracy of measurements. Therefore, this paper aims to provide an integrated health monitoring system to measure vital parameters and diagnose or predict disease. Through this work, the percentage of various gases in the blood through breathing is determined, vital parameters are measured and their effect on feelings is analyzed. A supervised learning model is configured to predict and diagnose based on biometric measurements. All results were compared with the results of the Omron device as a reference device. The results proved that the proposed design overcame many problems as it contributed to expanding the database of vital parameters and providing analysis on the effect of emotions on vital indicators. The accuracy of the measurements also reached 98.8% and the accuracy of diagnosing COVID-19 was 64%. The work also presents a user interface model for clinicians as well as for smartphones using the Internet of things.
Increasing waste generation has become a key challenge around the world due to the dramatic expansion in industrialization and urbanization. This study focuses on providing effective solutions for real-time monitoring garbage collection systems via the Internet of things (IoT). It is limited to controlling the bad odor of blowout gases and the spreading of overspills by using an IoT-based solution. The inadequate and poor dumping of waste produces radiation and toxic gases in the environment, creating an adversarial effect on global warming, human health, and the greenhouse system. The IoT and deep learning (DL) confer active solutions for real-time data monitoring and classification, correspondingly. Therefore, this paper presents an artificial ecosystem-based optimization with an improved deep learning model for IoT-assisted sustainable waste management, called the AEOIDL-SWM technique. The presented AEOIDL-SWM technique exploits IoT-based camera sensors for collecting information and a microcontroller for processing the data. For waste classification, the presented AEOIDL-SWM technique applies an improved residual network (ResNet) model-based feature extractor with an AEO-based hyperparameter optimizer. Finally, the sparse autoencoder (SAE) algorithm is exploited for waste classification. To depict the enhancements of the AEOIDL-SWM system, a widespread simulation investigation is performed. The comparative analysis shows the enhanced outcomes of the AEOIDL-SWM technique over other DL models.
MANET is a mobile ad hoc network with many mobile nodes communicating without a centralized module. Infrastructure-less networks make it desirable for many researchers to publish and bind multimedia services. Each node in this infrastructure-less network acts as self-organizing and re-configurable. It allows services to deploy and attain from another node over the ad hoc network. The service composition aims to provide a user’s requirement by combining different atomic services based on non-functional QoS parameters such as reliability, availability, scalability, etc. To provide service composition in MANET is challenging because of the node mobility, link failure, and topology changes, so a traditional protocol will be sufficient to obtain real-time services from mobile nodes. In this paper, the ad hoc on-demand distance vector protocol (AODV) is used and analyzed based on MANET’s QoS (Quality of Service) metrics. The QoS metrics for MANET depends on delay, bandwidth, memory capacity, network load, and packet drop. The requester node and provider node broker acts as a composer for this MANET network. The authors propose a QoS-based Dynamic Secured Broker Selection architecture (QoSDSBS) for service composition in MANET, which uses a dynamic broker and provides a secure path selection based on QoS metrics. The proposed algorithm is simulated using Network Simulator (NS2) with 53 intermediate nodes and 35 mobile nodes of area 1000 m × 1000 m. The comparative results show that the proposed architecture outperforms, with standards, the AODV protocol and affords higher scalability and a reduced network load.
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