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Smart Cloud Computing Technologies and Applications
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Smart Cloud Computing Technologies and Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 11822

Special Issue Editors

Dr. Meikang Qiu

E-Mail Website
Guest Editor
Department of Computer Science, Texas A&M University-Commerce, Commerce, TX 75428, USA
Interests: machine learning; big data; cyber security; cloud computing; IoT; CPS; smart computing; embedded systems
Dr. Cheng Zhang

E-Mail Website
Guest Editor
School of Fundamental Science and Engineering, Waseda University, Tokyo 169-8555, Japan
Interests: collaborative robot; IoT; machine learning; network economics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart cloud computing technology has developed rapidly recently with the acceleration of Internet-of-things (IoT) technology and artificial intelligence (AI) technology. We have entered an era in which cloud-based systems are given "smart" properties that can meet substantial demand in multiple fields, from tele-health to e-learning, and from vehicular systems to mobile applications. In this Special Issue, we aim to collect recent academic achievements in novel techniques of the most advanced smart cloud computing aligned with other novel technologies, such as IoT, AI, and big data technologies.

Dr. Meikang Qiu
Dr. Cheng Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Internet of things
  • machine learning-based smart cloud computing
  • big data technologies and applications
  • cyber threat intelligence
  • D2D communication
  • sensor network security

Published Papers (3 papers)

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Research

21 pages, 3520 KiB  
Article
Accelerating Faceting Wide-Field Imaging Algorithm with FPGA for SKA Radio Telescope as a Vast Sensor Array
by Yuefeng Song, Yongxin Zhu, Tianhao Nan, Junjie Hou, Sen Du and Shijin Song
Sensors 2020, 20(15), 4070; https://0-doi-org.brum.beds.ac.uk/10.3390/s20154070 - 22 Jul 2020
Cited by 2 | Viewed by 1976
Abstract
The SKA (Square Kilometer Array) radio telescope will become the most sensitive telescope by correlating a huge number of antenna nodes to form a vast array of sensors in a region over one hundred kilometers. Faceting, the wide-field imaging algorithm, is a novel [...] Read more.
The SKA (Square Kilometer Array) radio telescope will become the most sensitive telescope by correlating a huge number of antenna nodes to form a vast array of sensors in a region over one hundred kilometers. Faceting, the wide-field imaging algorithm, is a novel approach towards solving image construction from sensing data where earth surface curves cannot be ignored. However, the traditional processor of cloud computing, even if the most sophisticated supercomputer is used, cannot meet the extremely high computation performance requirement. In this paper, we propose the design and implementation of high-efficiency FPGA (Field Programmable Gate Array) -based hardware acceleration of the key algorithm, faceting in SKA by focusing on phase rotation and gridding, which are the most time-consuming phases in the faceting algorithm. Through the analysis of algorithm behavior and bottleneck, we design and optimize the memory architecture and computing logic of the FPGA-based accelerator. The simulation and tests on FPGA are done to confirm the acceleration result of our design and it is shown that the acceleration performance we achieved on phase rotation is 20× the result of the previous work. We then further designed and optimized an efficient microstructure of loop unrolling and pipeline for the gridding accelerator, and the designed system simulation was done to confirm the performance of our structure. The result shows that the acceleration ratio is 5.48 compared to the result tested on software in gridding parts. Hence, our approach enables efficient acceleration of the faceting algorithm on FPGAs with high performance to meet the computational constraints of SKA as a representative vast sensor array. Full article
(This article belongs to the Special Issue Smart Cloud Computing Technologies and Applications)
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18 pages, 3230 KiB  
Article
SASC: Secure and Authentication-Based Sensor Cloud Architecture for Intelligent Internet of Things
by Khalid Haseeb, Ahmad Almogren, Ikram Ud Din, Naveed Islam and Ayman Altameem
Sensors 2020, 20(9), 2468; https://0-doi-org.brum.beds.ac.uk/10.3390/s20092468 - 27 Apr 2020
Cited by 53 | Viewed by 4785
Abstract
Nowadays, the integration of Wireless Sensor Networks (WSN) and the Internet of Things (IoT) provides a great concern for the research community for enabling advanced services. An IoT network may comprise a large number of heterogeneous smart devices for gathering and forwarding huge [...] Read more.
Nowadays, the integration of Wireless Sensor Networks (WSN) and the Internet of Things (IoT) provides a great concern for the research community for enabling advanced services. An IoT network may comprise a large number of heterogeneous smart devices for gathering and forwarding huge data. Such diverse networks raise several research questions, such as processing, storage, and management of massive data. Furthermore, IoT devices have restricted constraints and expose to a variety of malicious network attacks. This paper presents a Secure Sensor Cloud Architecture (SASC) for IoT applications to improve network scalability with efficient data processing and security. The proposed architecture comprises two main phases. Firstly, network nodes are grouped using unsupervised machine learning and exploit weighted-based centroid vectors for the development of intelligent systems. Secondly, the proposed architecture makes the use of sensor-cloud infrastructure for boundless storage and consistent service delivery. Furthermore, the sensor-cloud infrastructure is protected against malicious nodes by using a mathematically unbreakable one-time pad (OTP) encryption scheme to provide data security. To evaluate the performance of the proposed architecture, different simulation experiments are conducted using Network Simulator (NS3). It has been observed through experimental results that the proposed architecture outperforms other state-of-the-art approaches in terms of network lifetime, packet drop ratio, energy consumption, and transmission overhead. Full article
(This article belongs to the Special Issue Smart Cloud Computing Technologies and Applications)
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15 pages, 2441 KiB  
Article
An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks
by Jin Wang, Yu Gao, Kai Wang, Arun Kumar Sangaiah and Se-Jung Lim
Sensors 2019, 19(11), 2579; https://0-doi-org.brum.beds.ac.uk/10.3390/s19112579 - 06 Jun 2019
Cited by 134 | Viewed by 4675
Abstract
A wireless sensor network (WSN) is an essential component of the Internet of Things (IoTs) for information exchange and communication between ubiquitous smart objects. Clustering techniques are widely applied to improve network performance during the routing phase for WSN. However, existing clustering methods [...] Read more.
A wireless sensor network (WSN) is an essential component of the Internet of Things (IoTs) for information exchange and communication between ubiquitous smart objects. Clustering techniques are widely applied to improve network performance during the routing phase for WSN. However, existing clustering methods still have some drawbacks such as uneven distribution of cluster heads (CH) and unbalanced energy consumption. Recently, much attention has been paid to intelligent clustering methods based on machine learning to solve the above issues. In this paper, an affinity propagation-based self-adaptive (APSA) clustering method is presented. The advantage of K-medoids, which is a traditional machine learning algorithm, is combined with the affinity propagation (AP) method to achieve more reasonable clustering performance. AP is firstly utilized to determine the number of CHs and to search for the optimal initial cluster centers for K-medoids. Then the modified K-medoids is utilized to form the topology of the network by iteration. The presented method effectively avoids the weakness of the traditional K-medoids in aspects of the homogeneous clustering and convergence rate. Simulation results show that the proposed algorithm outperforms some latest work such as the unequal cluster-based routing scheme for multi-level heterogeneous WSN (UCR-H), the low-energy adaptive clustering hierarchy using affinity propagation (LEACH-AP) algorithm, and the energy degree distance unequal clustering (EDDUCA) algorithm. Full article
(This article belongs to the Special Issue Smart Cloud Computing Technologies and Applications)
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