Joint Optimization of Data Value and Age of Information in Multi-cluster System with Mixed Data

LUO Jia; CHEN Qianbin; TANG Lun

doi:10.11999/JEIT230023

Volume 46 Issue 1

Jan. 2024

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(1): 308-316

LUO Jia, CHEN Qianbin, TANG Lun. Joint Optimization of Data Value and Age of Information in Multi-cluster System with Mixed Data[J]. Journal of Electronics & Information Technology, 2024, 46(1): 308-316. doi: 10.11999/JEIT230023

Citation:

LUO Jia, CHEN Qianbin, TANG Lun. Joint Optimization of Data Value and Age of Information in Multi-cluster System with Mixed Data[J]. Journal of Electronics & Information Technology, 2024, 46(1): 308-316. doi: 10.11999/JEIT230023

Citation:

PDF( 999 KB)

Joint Optimization of Data Value and Age of Information in Multi-cluster System with Mixed Data

doi: 10.11999/JEIT230023

LUO Jia^{1, 2
,
,},
CHEN Qianbin²,
TANG Lun²

1.
School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Key Laboratory of Mobile Communication Technology, School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62071078), The Chongqing Municipal Natural Science Foundation (cstc2021jcyj-bsh0175), The Sichuan Science and Technology Program (2021YFQ0053)

Received Date: 2023-01-16
Rev Recd Date: 2023-04-18

Available Online: 2023-04-26

Publish Date: 2024-01-17

Abstract

Abstract

Age of Information (AoI) is an emerging time-related indicator in the industry. It is often used to evaluate the freshness of received data. Considering a multi-cluster live streaming system with mixed video data and environmental data, a scheduling policy is formulated to jointly optimize the system data value and AoI. To overcome the problem that the effective solution to the optimization problem is difficult to achieve due to the action space being too large, the scheduling policy of the optimization problem is decomposed into two interrelated internal layer and external layer policies. The external layer policy utilizes deep reinforcement learning for channel allocation between clusters. The internal layer policy implements the link selection in the cluster on the basis of the constructed virtual queue. The two-layer policy embeds the internal layer policy of each cluster into the external layer policy for training. Simulation results show that compared with the existing scheduling policy, the proposed scheduling policy can increase the time-averaged data value of received data and reduce the time-averaged AoI.
- Age of Information (AoI),
- Data value,
- Live streaming system,
- Deep reinforcement learning,
- Scheduling policy

FullText(HTML)

References(15)

References

[1]	KAUL S, YATES R, and GRUTESER M. Real-time status: How often should one update?[C]. Proceedings of the IEEE INFOCOM, Orlando, USA, 2012: 2731–2735.
[2]	ZHANG Shuhang, ZHANG Hongliang, HAN Zhu, et al. Age of information in a cellular Internet of UAVs: Sensing and communication trade-off design[J]. IEEE Transactions on Wireless Communications, 2020, 19(10): 6578–6592. doi: 10.1109/TWC.2020.3004162
[3]	HU Huimin, XIONG Ke, QU Gang, et al. AoI-minimal trajectory planning and data collection in UAV-assisted wireless powered IoT networks[J]. IEEE Internet of Things Journal, 2021, 8(2): 1211–1223. doi: 10.1109/JIOT.2020.3012835
[4]	TANG Haoyue, WANG Jintao, SONG Linqi, et al. Minimizing age of information with power constraints: Multi-user opportunistic scheduling in multi-state time-varying channels[J]. IEEE Journal on Selected Areas in Communications, 2020, 38(5): 854–868. doi: 10.1109/JSAC.2020.2980911
[5]	XIE Xin, WANG Heng, YU Lei, et al. Online algorithms for optimizing age of information in the IoT systems with multi-slot status delivery[J]. IEEE Wireless Communications Letters, 2021, 10(5): 971–975. doi: 10.1109/LWC.2021.3052569
[6]	UITTO M and HEIKKINEN A. Evaluating 5G uplink performance in low latency video streaming[C]. Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), Grenoble, France, 2022: 393–398.
[7]	ZHANG Zhilong, ZENG Minyin, CHEN Mingzhe et al. Joint user grouping, version selection, and bandwidth allocation for live video multicasting[J]. IEEE Transactions on Communications, 2022, 70(1): 350–365. doi: 10.1109/TCOMM.2021.3115480
[8]	LIU Junquan, ZHANG Weizhan, HUANG Shouqin, et al. QoE-driven HAS live video channel placement in the media cloud[J]. IEEE Transactions on Multimedia, 2021, 23: 1530–1541. doi: 10.1109/TMM.2020.2999176
[9]	WEI Bo, SONG Hang, and KATTO J. High-QoE DASH live streaming using reinforcement learning[C]. IEEE/ACM 29th International Symposium on Quality of Service (IWQOS), Tokyo, Japan, 2021: 1–2.
[10]	MA Xiaoteng, LI Qing, ZOU Longhao, et al. QAVA: QoE-aware adaptive video bitrate aggregation for HTTP live streaming based on smart edge computing[J]. IEEE Transactions on Broadcasting, 2022, 68(3): 661–676.
[11]	LIU Dongzhu, ZHU Guangxu, ZENG Qunsong, et al. Wireless data acquisition for edge learning: Data-importance aware retransmission[J]. IEEE Transactions on Wireless Communications, 2021, 20(1): 406–420. doi: 10.1109/TWC.2020.3024980
[12]	LIU Dongzhu, ZHU Guangxu, ZHANG Jun, et al. Data-importance aware user scheduling for communication-efficient edge machine learning[J]. IEEE Transactions on Cognitive Communications and Networking, 2021, 7(1): 265–278. doi: 10.1109/TCCN.2020.2999606
[13]	YATES R D. Lazy is timely: Status updates by an energy harvesting source[C]. IEEE International Symposium on Information Theory (ISIT), Hong Kong, China, 2015: 3008–3012.
[14]	ZHOU Zhenyu, YU Haijun, MUMTAZ S, et al. Power control optimization for large-scale multi-antenna systems[J]. IEEE Transactions on Wireless Communications, 2020, 19(11): 7339–7352. doi: 10.1109/TWC.2020.3010701
[15]	DU Jianbo, CHENG Wenjie, LU Guangyue, et al. Resource pricing and allocation in MEC enabled blockchain systems: An A3C deep reinforcement learning approach[J]. IEEE Transactions on Network Science and Engineering, 2022, 9(1): 33–44. doi: 10.1109/TNSE.2021.3068340