TY - GEN
T1 - Revealing Much while Saying Less
T2 - 38th IEEE Conference on Computer Communications, INFOCOM 2020
AU - Jiang, Zhiyuan
AU - Cao, Zixu
AU - Fu, Siyu
AU - Peng, Fei
AU - Cao, Shan
AU - Zhang, Shunqing
AU - Xu, Shugong
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - Wireless communications for status update are becoming increasingly important, especially for machine-type control applications. Existing work has been mainly focused on Age of Information (AoI) optimizations. In this paper, a status-aware predictive wireless interface design, networking and implementation are presented which aim to minimize the status recovery error of a wireless networked system by leveraging online status model predictions. Two critical issues of predictive status update are addressed: practicality and usefulness. Link-level experiments on a Software-Defined-Radio (SDR) testbed are conducted and test results show that the proposed design can significantly reduce the number of wireless transmissions while maintaining a low status recovery error. A Status-aware Multi-Agent Reinforcement learning neTworking solution (SMART) is proposed to dynamically and autonomously control the transmit decisions of devices in an ad hoc network based on their individual statuses. System-level simulations of a multi dense platooning scenario are carried out on a road traffic simulator. Results show that the proposed schemes can greatly improve the platooning control performance in terms of the minimum safe distance between successive vehicles, in comparison with the AoI-optimized status-unaware and communication latency-optimized schemes - this demonstrates the usefulness of our proposed status update schemes in a real-world application.
AB - Wireless communications for status update are becoming increasingly important, especially for machine-type control applications. Existing work has been mainly focused on Age of Information (AoI) optimizations. In this paper, a status-aware predictive wireless interface design, networking and implementation are presented which aim to minimize the status recovery error of a wireless networked system by leveraging online status model predictions. Two critical issues of predictive status update are addressed: practicality and usefulness. Link-level experiments on a Software-Defined-Radio (SDR) testbed are conducted and test results show that the proposed design can significantly reduce the number of wireless transmissions while maintaining a low status recovery error. A Status-aware Multi-Agent Reinforcement learning neTworking solution (SMART) is proposed to dynamically and autonomously control the transmit decisions of devices in an ad hoc network based on their individual statuses. System-level simulations of a multi dense platooning scenario are carried out on a road traffic simulator. Results show that the proposed schemes can greatly improve the platooning control performance in terms of the minimum safe distance between successive vehicles, in comparison with the AoI-optimized status-unaware and communication latency-optimized schemes - this demonstrates the usefulness of our proposed status update schemes in a real-world application.
UR - http://www.scopus.com/inward/record.url?scp=85090296240&partnerID=8YFLogxK
U2 - 10.1109/INFOCOM41043.2020.9155225
DO - 10.1109/INFOCOM41043.2020.9155225
M3 - Conference Proceeding
AN - SCOPUS:85090296240
T3 - Proceedings - IEEE INFOCOM
SP - 1419
EP - 1428
BT - INFOCOM 2020 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 July 2020 through 9 July 2020
ER -