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Status已发表Published
TitleArtificial Intelligence based QoS optimization for multimedia communication in IoV systems
Creator
Date Issued2019-06-01
Source PublicationFuture Generation Computer Systems
ISSN0167-739X
Volume95Pages:667-680
Abstract

Due to the advancements in multimedia communication in internet of vehicles (IoV) through emerging technologies i.e., WiFi, Bluetooth, and fifth generation (5G) etc. The critical challenge for IoV during multimedia communication in healthcare is the quality of experience (QoE) optimization by managing the mobility of wireless channel between vehicles. Besides, Artificial Intelligence (AI) based approaches have entirely changed the landscape of IoVs, also the portable devices for transmitting multimedia content in IoV system has become very necessary for the end-users in their respective fields. Most of the end users are facing is their annoyed and less satisfactory perspective about the quality they are experiencing i.e., QoE. If the service provisioning is not pleasant then most of the end-users/consumers give-up to continue, and finally market devaluates the overall performance of the devices, company or entire system. So remedy that problem this paper first proposes two novel algorithms named, Power-aware QoE Optimization (PQO) and Buffer-aware QoE Optimization (BQO) and compares their performance with the Baseline. Second proposes multimedia communication mechanism. Third, proposes the QoE optimization framework during multimedia communication in IoV system through portable devices. Besides, experimental results reveal that proposed PQO and BQO algorithms optimizes the QoE at (31%, 33.5%) with improved lifetime of portable devices at (25%, 27%) higher level than the Baseline (25%, 17) accordingly by satisfying the end-users. Hence, it is concluded that our proposed algorithms outperforms the Baseline, so can be considered as potential candidates for the IoV applications during multimedia communication.

KeywordAI Baseline BQOA IoV Multimedia communication Optimization PQOA QoE QoS
DOI10.1016/j.future.2018.12.008
URLView source
Indexed BySCIE
Language英语English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Theory & Methods
WOS IDWOS:000465509600055
Scopus ID2-s2.0-85061065672
Citation statistics
Cited Times:61[WOS]   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Identifierhttp://repository.uic.edu.cn/handle/39GCC9TT/6891
CollectionResearch outside affiliated institution
Corresponding AuthorLuo, Zongwei
Affiliation
1.Sukkur IBA University,Sukkur,Sindh,65200,Pakistan
2.IDA-Computer and Information Science Department,Linkoping University,Linkoping,58183,Sweden
3.Shenzhen Key Laboratory of Computational Intelligence,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
4.Shah Abdul Latif University,Sindh,Khairpur Miras,Pakistan
5.Bahria University,Islamabad,Pakistan
6.National Institute of Telecommunications (Inatel),Brazil
7.Instituto de Telecomunicações,Portugal
8.University of Fortaleza,Brazil
Recommended Citation
GB/T 7714
Sodhro, Ali Hassan,Luo, Zongwei,Sodhro, Gul Hassanet al. Artificial Intelligence based QoS optimization for multimedia communication in IoV systems[J]. Future Generation Computer Systems, 2019, 95: 667-680.
APA Sodhro, Ali Hassan, Luo, Zongwei, Sodhro, Gul Hassan, Muzamal, Muhammad, Rodrigues, Joel J.P.C., & de Albuquerque, Victor Hugo C. (2019). Artificial Intelligence based QoS optimization for multimedia communication in IoV systems. Future Generation Computer Systems, 95, 667-680.
MLA Sodhro, Ali Hassan,et al."Artificial Intelligence based QoS optimization for multimedia communication in IoV systems". Future Generation Computer Systems 95(2019): 667-680.
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