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Wireless Multicast Rate Control Adaptive to Application Goodput and Loss Requirements

Published: 13 May 2024 Publication History

Abstract

Modern IoT/edge applications require one-to-many wireless communication (e.g., multi-drone coordination, data sharing among vehicles, synchronized IoT light shows). Due to the constantly varying wireless medium, thus reception quality, the sender must adjust its transmitting rate on a per-frame basis to meet the goodput and loss requirements of multiple receivers. Deciding an optimal rate within tens of milliseconds from nearly a hundred or more choices and continuing to chase that moving target is extremely challenging. Existing wireless technologies have little support for multicast rate control: most works are designed for unicast, where one receiver sends explicit per frame feedback, which is infeasible to scale to multiple receivers; a few works for multicast have rigid structures and high overhead unsuitable for IoT/edge; and most designs are based on a common implicit assumption: higher rates incur more losses. In this paper, we conduct systematic experiments and find that only a small fraction of data rates are practically useful, and higher rates can incur similar or even lower losses, thus cutting the data rate table size by 3.8X, making it manageable to select the optimal rate within a short duration. We further design an application-adaptive multicast rate control feedback protocol (r-DACK) with two policies enabling receivers to specify their desired loss rate, or loss rate and goodput requirements. r-DACK enables most receivers to meet their goodput/loss requirements while not being "bogged down" by some stragglers with bad reception quality. We build a prototype leveraging 802.11ac radio hardware and show that r-DACK can meet various goodput (15-50Mbps) and loss rate (10-50%) requirements successfully, both indoors and outdoors.

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  • (2024)Boosting Collaborative Vehicular Perception on the Edge with Vehicle-to-Vehicle CommunicationProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699328(141-154)Online publication date: 4-Nov-2024

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              cover image Guide Proceedings
              2024 IEEE/ACM Ninth International Conference on Internet-of-Things Design and Implementation (IoTDI)
              May 2024
              234 pages

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              IEEE Press

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              Published: 13 May 2024

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              • (2024)Boosting Collaborative Vehicular Perception on the Edge with Vehicle-to-Vehicle CommunicationProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699328(141-154)Online publication date: 4-Nov-2024

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