Current Issue : July - September Volume : 2019 Issue Number : 3 Articles : 5 Articles
The Internet of vehicles (IoV) is a newly emerged wave that converges Internet of things (IoT) into vehicular networks to benefit\nfrom ubiquitous Internet connectivity. Despite various research efforts, vehicular networks are still striving to achieve higher data\nrate, seamless connectivity, scalability, security, and improved quality of service, which are the key enablers for IoV. It becomes\neven more critical to investigate novel design architectures to accomplish efficient and reliable data forwarding when it comes to\nhandling the emergency communication infrastructure in the presence of natural epidemics. The article proposes a heterogeneous\nnetwork architecture incorporating multiple wireless interfaces (e.g., wireless access in vehicular environment (WAVE), longrange\nwireless fidelity (WiFi), and fourth generation/long-term evolution (4G/LTE)) installed on the on-board units, exploiting\nthe radio over fiber approach to establish a context-aware network connectivity. This heterogeneous network architecture attempts\nto meet the requirements of pervasive connectivity for vehicular ad hoc networks (VANETs) to make them scalable and\nadaptable for IoV supporting a range of emergency services. The architecture employs the Best Interface Selection (BIS) algorithm\nto always ensure reliable communication through the best available wireless interface to support seamless connectivity required\nfor efficient data forwarding in vehicle to infrastructure (V2I) communication successfully avoiding the single point of failure.\nMoreover, the simulation results clearly argue about the suitability of the proposed architecture in IoV environment coping with\ndifferent types of applications against individual wireless technologies....
This paper proposes a new adaptive watermarking scheme for digital images, which has the properties of blind extraction,\ninvisibility, and robustness against attacks. The typical scheme for invisibility and robustness consisted of two main techniques:\nfinding local positions to be watermarked and mixing or embedding the watermark into the pixels of the locations. In finding the\nlocation, however, our scheme uses a global space such that the multiple watermarking data is spread out over all four lowestfrequency\nsubbands, resulting from n-levelMallat-tree 2D (dimensional) DWT, where n depends on the amount of watermarking\ndata and the resolution of the host image, without any further process to find the watermarking locations. To embed the watermark\ndata into the subband coefficients, weighting factors are used according to the type and energy of each subband to adjust the\nstrength of the watermark, so we call this an adaptive scheme. To examine the ability of the proposed scheme, images with various\nresolutions are tested for various attacks, both pixel-value changing attacks and geometric attacks.With experimental results and\ncomparison to the existing works we show that the proposed scheme has better performance than the previous works, except those\nwhich specialize in certain types of attacks....
Based on the Adomian decomposition method and Lyapunov stability theory, this paper constructs a fractional-order\nmemristive hyperchaos. Then, the 0â??1 test analysis is applied to detect random nature of chaotic sequences exhibited by the\nfractional-order systems. Comparing with the corresponding integer-order hyperchaotic system, the fractional-order\nhyperchaos possesses higher complexity. Finally, an image encryption algorithm is proposed based on the fractional-order\nmemristive hyperchaos. Security and performance analysis indicates that the proposed chaos-based image encryption algorithm\nis highly resistant to statistical attacks....
Clustering analysis of massive data in wireless multimedia sensor networks (WMSN) has\nbecome a hot topic. However, most data clustering algorithms have difficulty in obtaining latent\nnonlinear correlations of data features, resulting in a low clustering accuracy. In addition, it is\ndifficult to extract features from missing or corrupted data, so incomplete data are widely used in\npractical work. In this paper, the optimally designed variational autoencoder networks is proposed\nfor extracting features of incomplete data and using high-order fuzzy c-means algorithm (HOFCM)\nto improve cluster performance of incomplete data. Specifically, the feature extraction model is\nimproved by using variational autoencoder to learn the feature of incomplete data. To capture\nnonlinear correlations in different heterogeneous data patterns, tensor based fuzzy c-means algorithm\nis used to cluster low-dimensional features. The tensor distance is used as the distance measure\nto capture the unknown correlations of data as much as possible. Finally, in the case that the\nclustering results are obtained, the missing data can be restored by using the low-dimensional\nfeatures. Experiments on real datasets show that the proposed algorithm not only can improve the\nclustering performance of incomplete data effectively, but also can fill in missing features and get\nbetter data reconstruction results....
MPEG-4 AVC encoded video streams have been analyzed using video traces\nand statistical features have been extracted, in the context of supporting efficient\ndeployment of networked and multimedia services. The statistical features\ninclude the number of scenes composing the video and the sizes of different\ntypes of frames, within the overall trace and each scene. Statistical\nprocessing has been performed upon the traces and subsequent fitting upon\nstatistical distributions (Pareto and lognormal). Through the construction of\na synthetic trace, based upon this analysis, our selections of statistical distribution\nhave been verified. In addition, different types of content, in terms of\nlevel of activity (quantified as different scene change ratio) have been considered.\nThrough modelling and fitting, the stability of the main statistical parameters\nhas been verified as well as observations on the dependence of these\nparameters upon the video activity level....
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