Artificial Intelligence based QoS optimization for multimedia communication in IoV systems

Sodhro, Ali Hassan1,2; Luo, Zongwei3; Sodhro, Gul Hassan4; Muzamal, Muhammad5; Rodrigues, Joel J. P. C.6,7; de Albuquerque, Victor Hugo C.8

2019-06

10.1016/j.future.2018.12.008

Future Generation Computer Systems-The International Journal of eScience   影响因子和分区

0167-739X

1872-7115

Due to the advancements in multimedia communication in internet of vehicles (boy) through emerging technologies i.e., WiFi, Bluetooth, and fifth generation (5G) etc. The critical challenge for boy during multimedia communication in healthcare is the quality of experience (QoE) optimization by managing the mobility of wireless channel between vehicles. Besides, Artificial Intelligence (Al) 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 boy 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 boy applications during multimedia communication. (C) 2019 Elsevier B.V. All rights reserved.

Al QoE Multimedia communication IoV QoS Optimization PQOA BQOA Baseline

SCI ; EI

英语

通讯

SUSTech Startup Fund, China[Y01236215] ; SUSTech Startup Fund, China[Y01236115]

Computer Science

Computer Science, Theory & Methods

WOS:000465509600055

ELSEVIER SCIENCE BV

20190606478234

5G mobile communication systems ; Artificial intelligence ; Multimedia systems ; Optimization ; Portable equipment ; Power management ; Vehicle to vehicle communications ; Wi-Fi

Energy Utilization:525.3 ; Telecommunication; Radar, Radio and Television:716 ; Computer Software, Data Handling and Applications:723 ; Optimization Techniques:921.5

Web of Science

1.Sukkur IBA Univ, Elect Engn Dept, Sindh 65200, Pakistan
2.Linkoping Univ, IDA Comp & Informat Sci Dept, S-58183 Linkoping, Sweden
3.Southern Univ Sci & Technol, Shenzhen Key Lab Computat Intelligence, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China
4.Shah Abdul Latif Univ, Khairpur Miras, Sindh, Pakistan
5.Bahria Univ, Islamabad, Pakistan
6.Natl Inst Telecommun Inatel, Santa Rita Do Sapucai, MG, Brazil
7.Inst Telecomunicacoes, Aveiro, Portugal
8.Univ Fortaleza, Fortaleza, Ceara, Brazil

Sodhro, Ali Hassan,Luo, Zongwei,Sodhro, Gul Hassan,et al. Artificial Intelligence based QoS optimization for multimedia communication in IoV systems[J]. Future Generation Computer Systems-The International Journal of eScience,2019,95:667-680.

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-The International Journal of eScience,95,667-680.

Sodhro, Ali Hassan,et al."Artificial Intelligence based QoS optimization for multimedia communication in IoV systems".Future Generation Computer Systems-The International Journal of eScience 95(2019):667-680.