Emergent Communication Protocol Learning for Task Offloading in Industrial Internet of Things
Mostafa, Salwa; Mota, Mateus P.; Valcarce, Alvaro; Bennis, Mehdi (2024-02-26)
IEEE
26.02.2024
S. Mostafa, M. P. Mota, A. Valcarce and M. Bennis, "Emergent Communication Protocol Learning for Task Offloading in Industrial Internet of Things," GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia, 2023, pp. 7055-7060, doi: 10.1109/GLOBECOM54140.2023.10437954.
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© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202403192310
https://urn.fi/URN:NBN:fi:oulu-202403192310
Tiivistelmä
Abstract
In this paper, we leverage a multi-agent reinforcement learning (MARL) framework to jointly learn a computation of-floading decision and multichannel access policy with corresponding signaling. Specifically, the base station and industrial Internet of Things mobile devices are reinforcement learning agents that need to cooperate to execute their computation tasks within a deadline constraint. We adopt an emergent communication protocol learning framework to solve this problem. The numerical results illustrate the effectiveness of emergent communication in improving the channel access success rate and the number of successfully computed tasks compared to contention-based, contention-free, and no-communication approaches. Moreover, the proposed task offloading policy outperforms remote and local computation baselines.
In this paper, we leverage a multi-agent reinforcement learning (MARL) framework to jointly learn a computation of-floading decision and multichannel access policy with corresponding signaling. Specifically, the base station and industrial Internet of Things mobile devices are reinforcement learning agents that need to cooperate to execute their computation tasks within a deadline constraint. We adopt an emergent communication protocol learning framework to solve this problem. The numerical results illustrate the effectiveness of emergent communication in improving the channel access success rate and the number of successfully computed tasks compared to contention-based, contention-free, and no-communication approaches. Moreover, the proposed task offloading policy outperforms remote and local computation baselines.
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