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Applied Sciences
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Applications of Mobile Computing in Wireless Networks
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Applications of Mobile Computing in Wireless Networks

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (25 August 2022) | Viewed by 7590

Special Issue Editors

Prof. Dr. Zhiping Cai

E-Mail Website
Guest Editor
Networking Engineering Department, College of Computer, National University of Defense Technology, Changsha, China
Interests: wireless mesh networks; virtualization technology; computer networking; network communication
Prof. Dr. Yejun He

E-Mail Website
Guest Editor
College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
Interests: wireless communications; signal processing; information and communication technology
Dr. Ruoyu Su

E-Mail Website
Guest Editor
School of Internet Things, Nanjing University of Posts and Telecommunications, Nanjing, China
Interests: wireless communication; communication and Internet of Things

Special Issue Information

Dear Colleagues,

Mobile computing releases the limitations of conventional computing resource and platforms by wireless communication infrastructures and powerful end devices. Users can rapidly obtain efficient and accurate information with a low cost under the general framework of mobile computing with wireless networks. Recent developments in next-generation wireless networks, the Internet of Things, and artificial intelligence, have boosted interest in highly efficient communications and mobile computing for a wide range of applications in civilian, commercial, and military areas. For example, the self-driving technique depends on the wireless network with high latency sensitivity and rapid computing to guarantee the safety of roads and drivers, whereas telemedicine requires ultra-high throughput to enhance users’ experiences. Therefore, a large amount of network services as well as highly dynamic resource requirements have presented many formidable technical challenges, such as scalability, quality of service, reliability and security, and energy efficiency, which have stimulated a considerable amount of research activities in this broad area in recent years.

This Special Issue presents and highlights advances in network architectures, protocols, and algorithms for mobile computing in wireless networks. The applications of interest include but are not limited to mobile edge computing, ubiquitous Internet of Things, wireless sensor networks, cloud computing, emerging multimedia applications, artificial intelligence for mobile computing, and wireless networks.

Prof. Dr. Zhiping Cai
Prof. Dr. Yejun He
Dr. Ruoyu Su
Guest Editors

Manuscript Submission Information

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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. Applied Sciences 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 2400 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

  • mobile computing
  • wireless sensor networks
  • wireless communications
  • ubiquitous Internet of Things
  • cloud computing
  • mobile edge computing
  • learning algorithms

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Published Papers (3 papers)

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18 pages, 3636 KiB  
Article
A Blockchain-Based Trust Model for Uploading Illegal Data Identification
by Jieren Cheng, Yuanshen Li, Yuming Yuan, Bo Zhang and Xinbin Xu
Appl. Sci. 2022, 12(19), 9657; https://doi.org/10.3390/app12199657 - 26 Sep 2022
Cited by 3 | Viewed by 1739
Abstract
Malicious users can upload illegal data to the blockchain to spread it, resulting in serious threats due to the tamper-proof characteristics of the blockchain. However, the existing methods for uploading illegal data identification cannot select trust nodes and ensure the credibility of the [...] Read more.
Malicious users can upload illegal data to the blockchain to spread it, resulting in serious threats due to the tamper-proof characteristics of the blockchain. However, the existing methods for uploading illegal data identification cannot select trust nodes and ensure the credibility of the identification results, leading to a decrease in the credibility of the methods. To solve the problem, this paper proposes a blockchain-based trust model for uploading illegal data identification. The trust model mainly has the following two core modules: Reputation-based random selection algorithm (RBRSA) and incentive mechanism. By assigning reputation attributes to nodes, the proposed RBRSA will select nodes according to reputation values. RBRSA favors the nodes with high reputation value to ensure the randomness and credibility of the identification nodes. The incentive mechanism is designed to ensure the credibility of the identification results through the credibility analysis of the model based on game theory and Nash equilibrium. Identification nodes that identify illegal data correctly will obtain incentives. In order to obtain a higher income, the identification nodes must identify illegal data correctly. Credibility analysis and comparative experiments show that the probability of selecting credible nodes by RBRSA is up to 23% higher than the random selection algorithm. The probability of selecting the nodes with a reputation value of 20 by RBRSA is 27% lower than the random selection algorithm; that is, the probability that RBRSA selects untrusted nodes is lower. Therefore, the nodes selected by RBRSA have superior credibility compared with other methods. In terms of the effect of the incentive mechanism, the incentive mechanism can encourage nodes to identify data credibly and improve the credibility of identification results. All in all, the trusted model has higher credibility than other methods. Full article
(This article belongs to the Special Issue Applications of Mobile Computing in Wireless Networks)
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16 pages, 1160 KiB  
Article
A Polynomial Inversion-Based Fast Time-Delay Estimation Method for Wideband Large-Scale Antenna Systems
by Xiaowei Liu, Guangliang Ren, Xiaoman Yin, Bo Zhang and Yu Wang
Appl. Sci. 2022, 12(7), 3378; https://doi.org/10.3390/app12073378 - 26 Mar 2022
Cited by 2 | Viewed by 1948
Abstract
This paper proposes a new fast time-delay estimation (TDE) method based on polynomial inversion which addresses the challenges arising from the requirements for high-precision, low-computational-complexity synchronization error estimation in wideband large-scale antenna systems (LSASs). In this work, we use the convex parabolic extreme [...] Read more.
This paper proposes a new fast time-delay estimation (TDE) method based on polynomial inversion which addresses the challenges arising from the requirements for high-precision, low-computational-complexity synchronization error estimation in wideband large-scale antenna systems (LSASs). In this work, we use the convex parabolic extreme point equation as the timing error detector (TED), and develop a polynomial inversion-based function to characterize the one-to-one mapping relationship between true time delay (TTD) and TED estimates using the least square (LS) method, then obtain the time-delay difference with a high accuracy and high computational efficiency. The results of the performance analysis indicate that the Mean Square Error (MSE) of the proposed algorithm is less than 1 dB away from the Cramer–Rao lower bound (CRLB), which is produced in this paper, while adding a few multipliers compared to the convex parabolic interpolation method. Finally, a further example illustrates that the proposed algorithm can achieve a synchronization error of less than 5 ps between channels based on the NI PXI broadband multichannel acquisition platform. Full article
(This article belongs to the Special Issue Applications of Mobile Computing in Wireless Networks)
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15 pages, 644 KiB  
Article
Scenario-Based Configuration Refinement for High-Load Cellular Networks: An Operator View
by Ruoyu Su, Meinan Zhang, Fei Ding, Guilong Hu and Qi Qi
Appl. Sci. 2022, 12(3), 1483; https://doi.org/10.3390/app12031483 - 29 Jan 2022
Cited by 2 | Viewed by 2012
Abstract
With the rapid growth of users and sustained network demands powered by different industries, the quality of service (QoS) of the cellular network is affected by network traffic and computing loads. The current solutions of QoS improvement in academia focus on the fundamental [...] Read more.
With the rapid growth of users and sustained network demands powered by different industries, the quality of service (QoS) of the cellular network is affected by network traffic and computing loads. The current solutions of QoS improvement in academia focus on the fundamental algorithms within the physical and medium access control (MAC) layer. However, traffic features of various scenarios extracted from field data are rarely addressed for practical network configuration refinement. In this paper, we identify significant indicators of high traffic load cells according to the field data provided by telecommunication operators. Then, we propose the analysis flow of high traffic load cells with basic principles of network configuration refinement for QoS improvement. To demonstrate the proposed analysis flow and the refinement principles, we consider three typical scenarios of high traffic load cells, including high population density, emergency, and high-speed mobility. For each scenario, we discuss traffic features with field data. The corresponding performance evaluation demonstrates that the proposed principle can significantly enhance the network performance and user experience in terms of access success rate, downlink data rate, and number of high traffic load cells. Full article
(This article belongs to the Special Issue Applications of Mobile Computing in Wireless Networks)
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