Security in Cloud Computing: State-of-the-Art, Key Features, Challenges, and Opportunities

2019 ◽  
Author(s):  
Abdallah Tubaishat
Keyword(s):  
Cloud Computing ◽  
State Of The Art ◽  
Challenges And Opportunities ◽  
Key Features

scholarly journals Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 110
Author(s):  
Darwin Quezada-Gaibor ◽  
Joaquín Torres-Sospedra ◽  
Jari Nurmi ◽  
Yevgeni Koucheryavy ◽  
Joaquín Huerta
Keyword(s):  
Systematic Review ◽  
Cloud Computing ◽  
Fault Tolerance ◽  
State Of The Art ◽  
Comprehensive Analysis ◽  
Cloud Services ◽  
Location Based Services ◽  
Efficient Computation ◽  
Key Features ◽  
Essential Resource

Cloud Computing and Cloud Platforms have become an essential resource for businesses, due to their advanced capabilities, performance, and functionalities. Data redundancy, scalability, and security, are among the key features offered by cloud platforms. Location-Based Services (LBS) often exploit cloud platforms to host positioning and localisation systems. This paper introduces a systematic review of current positioning platforms for GNSS-denied scenarios. We have undertaken a comprehensive analysis of each component of the positioning and localisation systems, including techniques, protocols, standards, and cloud services used in the state-of-the-art deployments. Furthermore, this paper identifies the limitations of existing solutions, outlining shortcomings in areas that are rarely subjected to scrutiny in existing reviews of indoor positioning, such as computing paradigms, privacy, and fault tolerance. We then examine contributions in the areas of efficient computation, interoperability, positioning, and localisation. Finally, we provide a brief discussion concerning the challenges for cloud platforms based on GNSS-denied scenarios.

scholarly journals Disaster Resilience of Optical Networks: State of the Art, Challenges, and Opportunities

2021 ◽  
pp. 100619
Author(s):  
Jacek Rak ◽  
Rita Girão-Silva ◽  
Teresa Gomes ◽  
Georgios Ellinas ◽  
Burak Kantarci ◽  
...  
Keyword(s):  
Optical Networks ◽  
State Of The Art ◽  
Disaster Resilience ◽  
Challenges And Opportunities

scholarly journals Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3800
Author(s):  
Sebastian Krapf ◽  
Nils Kemmerzell ◽  
Syed Khawaja Haseeb Khawaja Haseeb Uddin ◽  
Manuel Hack Hack Vázquez ◽  
Fabian Netzler ◽  
...  
Keyword(s):  
Deep Learning ◽  
System Analysis ◽  
State Of The Art ◽  
Critical Role ◽  
Semantic Segmentation ◽  
Energy System ◽  
Aerial Images ◽  
Potential Analysis ◽  
3D Data ◽  
Challenges And Opportunities

Roof-mounted photovoltaic systems play a critical role in the global transition to renewable energy generation. An analysis of roof photovoltaic potential is an important tool for supporting decision-making and for accelerating new installations. State of the art uses 3D data to conduct potential analyses with high spatial resolution, limiting the study area to places with available 3D data. Recent advances in deep learning allow the required roof information from aerial images to be extracted. Furthermore, most publications consider the technical photovoltaic potential, and only a few publications determine the photovoltaic economic potential. Therefore, this paper extends state of the art by proposing and applying a methodology for scalable economic photovoltaic potential analysis using aerial images and deep learning. Two convolutional neural networks are trained for semantic segmentation of roof segments and superstructures and achieve an Intersection over Union values of 0.84 and 0.64, respectively. We calculated the internal rate of return of each roof segment for 71 buildings in a small study area. A comparison of this paper’s methodology with a 3D-based analysis discusses its benefits and disadvantages. The proposed methodology uses only publicly available data and is potentially scalable to the global level. However, this poses a variety of research challenges and opportunities, which are summarized with a focus on the application of deep learning, economic photovoltaic potential analysis, and energy system analysis.

scholarly journals Integrated multiscale biomaterials experiment and modelling: a perspective

2016 ◽  
Vol 6 (1) ◽  
pp. 20150098 ◽  
Author(s):  
Markus J. Buehler ◽  
Guy M. Genin
Keyword(s):  
State Of The Art ◽  
Biological Systems ◽  
The State ◽  
Computational Power ◽  
Multiscale Models ◽  
Predictive Capacity ◽  
Hierarchical Levels ◽  
Challenges And Opportunities

Advances in multiscale models and computational power have enabled a broad toolset to predict how molecules, cells, tissues and organs behave and develop. A key theme in biological systems is the emergence of macroscale behaviour from collective behaviours across a range of length and timescales, and a key element of these models is therefore hierarchical simulation. However, this predictive capacity has far outstripped our ability to validate predictions experimentally, particularly when multiple hierarchical levels are involved. The state of the art represents careful integration of multiscale experiment and modelling, and yields not only validation, but also insights into deformation and relaxation mechanisms across scales. We present here a sampling of key results that highlight both challenges and opportunities for integrated multiscale experiment and modelling in biological systems.

scholarly journals A Rigorous Evaluation of State-of-the-Art Scheduling Algorithms for Cloud Computing

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 75033-75047 ◽  
Author(s):  
Altaf Hussain ◽  
Muhamamd Aleem ◽  
Muhammad Arshad Islam ◽  
Muhammad Azhar Iqbal
Keyword(s):  
Cloud Computing ◽  
State Of The Art ◽  
Scheduling Algorithms ◽  
Rigorous Evaluation

Towards Update-Efficient and Parallel-Friendly Content-Based Indexing Scheme in Cloud Computing

2018 ◽  
Vol 12 (02) ◽  
pp. 191-213
Author(s):  
Nan Zhu ◽  
Yangdi Lu ◽  
Wenbo He ◽  
Hua Yu ◽  
Jike Ge
Keyword(s):  
Cloud Computing ◽  
State Of The Art ◽  
High Volume ◽  
Data Partitioning ◽  
The State ◽  
Computing Systems ◽  
Indexing System ◽  
Indexing Method ◽  
Concurrent User ◽  
Art Research

The sheer volume of contents generated by today’s Internet services is stored in the cloud. The effective indexing method is important to provide the content to users on demand. The indexing method associating the user-generated metadata with the content is vulnerable to the inaccuracy caused by the low quality of the metadata. While the content-based indexing does not depend on the error-prone metadata, the state-of-the-art research focuses on developing descriptive features and misses the system-oriented considerations when incorporating these features into the practical cloud computing systems. We propose an Update-Efficient and Parallel-Friendly content-based indexing system, called Partitioned Hash Forest (PHF). The PHF system incorporates the state-of-the-art content-based indexing models and multiple system-oriented optimizations. PHF contains an approximate content-based index and leverages the hierarchical memory system to support the high volume of updates. Additionally, the content-aware data partitioning and lock-free concurrency management module enable the parallel processing of the concurrent user requests. We evaluate PHF in terms of indexing accuracy and system efficiency by comparing it with the state-of-the-art content-based indexing algorithm and its variances. We achieve the significantly better accuracy with less resource consumption, around 37% faster in update processing and up to 2.5[Formula: see text] throughput speedup in a multi-core platform comparing to other parallel-friendly designs.

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