The IoT sensor applications have grown in extreme numbers, generating a large amount of data, and it requires very effective data analysis procedures. However, the different IoT infrastructures and IoT sensor device layers possess protocol limitations in transmitting and receiving messages which generate obstacles in developing the smart IoT sensor applications. This difficulty prohibited existing IoT sensor implementations from adapting to other IoT sensor applications. In this article, we study and analyze how IoT sensor produces data for big data analytics, and it also highlights the existing challenges of intelligent solutions. IoT sensor applications required big data classification and analysis in a Fog computing (FC) environment using computation intelligence (CI). Our proposed Fog big data analysis model (FBDAM) and BPNN analysis model for IoT sensor application using fusion deep learning (FDL) pose new obstacles for potential machine-to-machine communication practices. We have applied our proposed FBDAM on the most significant Fog applications developed on smart city datasets (parking, transportation, security, and sensor IoT dataset) and got improving results. We compared different deep and machine learning algorithms (SVM, SVMG-RBF, BPNN, S3VM, and proposed FDL) on different smart city dataset IoT application environments.
In terms of growth, effect, and capability, the 5G-enabled Internet of Things (IoT) is incredible. The volume of data distributed and processed by IoT (Internet of Things) systems that trust connectivity and coverage raises some security problems. As IoT technology is directly used in our daily lives, the threats of present cyberspace may grow more prominent globally. Extended network life, coverage, and connectivity are all required for securing IoT-based 5G network devices. As a result of these failures, there are flaws that lead to security breaches. Because purposeful faults can quickly render the entire network dysfunctional, they are more difficult to identify than unexpected failures. Securing IoT-based 5G Network Device Connectivity and Coverage for expending Encryption and Authentication Scheme (EAS) framework is proposed in this study, which uses novel security flaws. In this research, we proposed a Boltzmann machine (BMKG)-based encryption algorithm for securing 5G-enabled IoT device network environment and compared various asymmetric algorithms for key exchange.
In recent times, the Internet of Medical Things (IoMT) is a new loomed technology, which has been deliberated as a promising technology designed for various and broadly connected networks. In an intelligent healthcare system, the framework of IoMT observes the health circumstances of the patients dynamically and responds to backings their needs, which helps detect the symptoms of critical rare body conditions based on the data collected. Metaheuristic algorithms have proven effective, robust, and efficient in deciphering real-world optimization, clustering, forecasting, classification, and other engineering problems. The emergence of extraordinary, very large-scale data being generated from various sources such as the web, sensors, and social media has led the world to the era of big data. Big data poses a new contest to metaheuristic algorithms. So, this research work presents the metaheuristic optimization algorithm for big data analysis in the IoMT using gravitational search optimization algorithm (GSOA) and reflective belief network with convolutional neural networks (DBN-CNNs). Here the data optimization has been carried out using GSOA for the collected input data. The input data were collected for the diabetes prediction with cardiac risk prediction based on the damage in blood vessels and cardiac nerves. Collected data have been classified to predict abnormal and normal diabetes range, and based on this range, the risk for a cardiac attack has been predicted using SVM. The performance analysis is made to reveal that GSOA-DBN_CNN performs well in predicting diseases. The simulation results illustrate that the GSOA-DBN_CNN model used for prediction improves accuracy, precision, recall, F1-score, and PSNR.
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