In the past few years, the implementation of blockchain technology for various applications has been widely discussed in the research community and the industry. There are sufficient number of articles that discuss the possibility of applying blockchain technology in various areas, such as, healthcare, IoT, and business. However, in this article, we present a comparative analysis of core blockchain architecture, its fundamental concepts, and its applications in three major areas: the Internet-of-Things (IoT), healthcare, business and vehicular industry. For each area, we discuss in detail, challenges and solutions that have been proposed from the research community and industry. This research studies also presented the complete ecosystem of blockchain of all the papers we reviewed and summarized. Moreover, analysis is performed of various blockchain platforms, their consensus models, and applications. Finally, we discuss key aspects that are required for the widespread future adoption of blockchain technology in these major areas. INDEX TERMS Blockchain, IoT blockchain, healthcare blockchain, permissioned blockchain, business blockchain.
Unmanned aerial vehicles (UAVs), also known as drones, once centric to military applications, are presently finding their way in many civilian and commercial applications. If national legislations permit UAVs to operate autonomously, one will see the skies become populated with many small UAVs, each one performing various tasks such as mail and package delivery, traffic monitoring, event filming, surveillance, search and rescue, and other applications. Thus, advancing to multiple small UAVs from a single large UAV has resulted in a new clan of networks known as flying ad-hoc networks (FANETs). Such networks provide reliability, ease of deployment, and relatively low operating costs by offering a robust communication network among the UAVs and base stations (BS). Although FANETs offer many benefits, there also exist a number of challenges that need to be addressed; the most significant of these being the communication one. Therefore, the article aims to provide insights into the key enabling communication technologies through the investigation of data rate, spectrum type, coverage, and latency. Moreover, application scenarios along with the feasibility of key enabling technologies are also examined. Finally, challenges and open research topics are discussed to further hone the research work.
The Internet of Health Things (IoHT) is an extended breed of the Internet of Things (IoT), which plays an important role in the remote sharing of data from various physical processes such as patient monitoring, treatment progress, observation, and consultation. The key benefit of the IoHT platform is the ease of time-independent interaction from geographically distant locations by offering preventive or proactive healthcare services at a lower cost. The communication, integration, computation, and interoperability in IoHT are provided by various low-power biomedical sensors equipped with limited computational capabilities. Therefore, conventional cryptographic solutions are not feasible for the majority of IoHT applications. In addition, executing computing-intensive tasks will lead to a slow response time that can deteriorate the performance of IoHT. We strive to resolve such a deficiency, and thus a new scheme has been proposed in this article, called an online-offline signature scheme in certificateless settings. The scheme divides the signing part into two phases, i.e., online and offline. In the absence of a message, the offline phase performs computationally intensive tasks, while lighter computations are executed in the online phase when there is a message. Security analyses and comparisons with the respective existing schemes are carried out to show the feasibility of the proposed scheme. The results obtained authenticate that the proposed scheme offers enhanced security with lower computational and communication costs.
This work presents a robust watermarking technique in hybrid domain for the copyright claim of medical images. The scheme is a fusion of three popular transforms: Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD). The input image is first separated into region of interest (ROI) and region of non-interest (RONI). The DWT is applied on RONI to get low and high frequency bands. The low frequency band is then segmented into 4 4 blocks. The Human Visual System (HVS) is applied to select the potential blocks for implanting watermark content. Each 4 4 selected block is further subdivided into four 2 2 carrier matrices. The SVD is applied to each carrier matrix. Finally, the hidden information is implanted by altering the largest diagonal singular values of four 2 2 matrices. The technique is blind, so host image is not needed for the extraction of hidden information. The proposed scheme achieves higher values of imperceptibility as well as robustness. Experimental results reveal that the proposed technique outperforms the techniques currently reported in the literature by achieving higher values of imperceptibility in the form of PSNR with value of 44.0567 decibels (dB) and SSIM value of 0.9800. At the same time it achieves excellent values of robustness with maximum NCC value of 1.000 and minimum BER with value of 0.000.
The growing number of security threats has prompted the use of a variety of security techniques. The most common security tools for identifying and tracking intruders across diverse network domains are intrusion detection systems. Machine Learning classifiers have begun to be used in the detection of threats, thus increasing the intrusion detection systems’ performance. In this paper, the investigation model for an intrusion detection systems model based on the Principal Component Analysis feature selection technique and a different Support Vector Machine kernels classifier is present. The impact of various kernel functions used in Support Vector Machines, namely linear, polynomial, Gaussian radial basis function, and Sigmoid, is investigated. The performance of the investigation model is measured in terms of detection accuracy, True Positive, True Negative, Precision, Sensitivity, and F-measure to choose an appropriate kernel function for the Support Vector Machine. The investigation model was examined and evaluated using the KDD Cup’99 and UNSW-NB15 datasets. The obtained results prove that the Gaussian radial basis function kernel is superior to the linear, polynomial, and sigmoid kernels in both used datasets. Obtained accuracy, Sensitivity, and, F-measure of the Gaussian radial basis function kernel for KDD CUP’99 were 99.11%, 98.97%, and 99.03%. for UNSW-NB15 datasets were 93.94%, 93.23%, and 94.44%.
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