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    Deadline-Aware TDMA Scheduling for Multihop Networks Using Reinforcement Learning

    Chilukuri, Shanti and Piao, Guangyuan and Lugones, Diego and Pesch, Dirk (2021) Deadline-Aware TDMA Scheduling for Multihop Networks Using Reinforcement Learning. 2021 IFIP Networking Conference, IFIP Networking 2021. pp. 1-9.

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    Time division multiple access (TDMA) is the medium access control strategy of choice for multihop networks with deterministic delay guarantee requirements. As such, many Internet of Things applications use protocols based on time division multiple access. Optimal slot assignment in such networks is NP-hard when there are strict deadline requirements and is generally done using heuristics that give suboptimal transmission schedules in linear time. However, existing heuristics make a scheduling decision at each time slot based on the same criterion without considering its effect on subsequent network states or scheduling actions. Here, we first identify a set of node features that capture the information necessary for network state representation to aid building schedules using Reinforcement Learning (RL). We then propose three different centralized approaches to RL-based TDMA scheduling that vary in training and network representation methods. Using RL allows applying diverse criteria at different time slots while considering the effect of a scheduling action on meeting the scheduling objective for the entire TDMA frame, resulting in better schedules. We compare the three proposed schemes in terms of how well they meet the scheduling objectives and their applicability to networks with memory and time constraints. One of the schemes proposed is RLSchedule, which is particularly suited to constrained networks. Simulation results for a variety of network scenarios show that RLSchedule reduces the percentage of packets missing deadlines by up to 60% compared to the best available baseline heuristic.

    Item Type: Article
    Keywords: Deadline-Aware; TDMA; Scheduling; Multihop Networks; Reinforcement Learning;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15632
    Identification Number:
    Depositing User: Guangyuan Piao
    Date Deposited: 08 Mar 2022 11:52
    Journal or Publication Title: 2021 IFIP Networking Conference, IFIP Networking 2021
    Publisher: IEEE
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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