RL-ASL: A Dynamic Listening Optimization for TSCH Networks Using Reinforcement Learning
Authors: F. Fernando Jurado-Lasso, J. F. Jurado
Organizations: independent researcher, based in Cali, Colombia · Department of Basic Science, Faculty of Engineering and Administration, Universidad Nacional de Colombia Sede Palmira, Palmira 763531, Colombia
Time Slotted Channel Hopping (TSCH) is a widely adopted Media Access Control (MAC) protocol within the IEEE 802.15.4e standard, designed to provide reliable and energy-efficient communication in Industrial Internet of Things (IIoT) networks. However, state-of-the-art TSCH schedulers rely on static slot allocations, resulting in idle listening and unnecessary power consumption under dynamic traffic conditions. This paper introduces RL-ASL, a reinforcement learning-driven adaptive listening framework that dynamically decides whether to activate or skip a scheduled listening slot based on real-time network conditions. By integrating learning-based slot skipping with standard TSCH scheduling, RL-ASL reduces idle listening while preserving synchronization and delivery reliability. Experimental results on the FIT IoT-LAB testbed and Cooja network simulator show that RL-ASL achieves up to 46% lower power consumption than baseline scheduling protocols, while maintaining near-perfect reliability and reducing average latency by up to 96% compared to PRIL-M. Its link-based variant, RL-ASL-LB, further improves delay performance under high contention with similar energy efficiency. Importantly, RL-ASL performs inference on constrained motes with negligible overhead, as model training is fully performed offline. Overall, RL-ASL provides a practical, scalable, and energy-aware scheduling mechanism for next-generation low-power IIoT networks.
Figures & tables
Fig. 1: Network model for a TSCH network with five nodes.
Fig. 2: Experimental network topologies from Strasbourg IoT-LAB site: (a) Simple 5-node topology used for training and validation, (b) Larger star topology, and (c) Deployment of the star topology in the FIT IoT-LAB.
1 [respect-blocks] \RowStyle Pattern
Jittered
Transmission intervals per node ID
High Traffic
✓
All nodes: 13 s
\Block 4-1 Heterogeneous Traffic
\Block 4-1✓
IDs 3, 11, 15, 19: 17 s
IDs 4, 12, 16, 20: 30 s
IDs 5, 6, 13, 17, 21: 50 s
IDs 4, 18, 22: 73 s
Sparse Traffic
✓
Alternating IDs: 60 or 73 s
TABLE I: Traffic generation patterns and per-node Tx intervals.
Fig. 3: Convergence of average reward during Q-learning training in the relay topology for different learning rates α .
1 [respect-blocks] \RowStyle Rskip
Latency [ms]
PDR [%]
RDC [%]
0.25
202.37
99.91
1.35
0.5
196.38
99.97
1.32
0.75
198.70
99.97
1.33
TABLE III: Sensitivity of RL-ASL to the reward parameter Rskip in the simple topology.
Fig. 4: Comparison of PDR across different traffic patterns for the simple topology : (a-d) PDR under high, heterogeneous, sparse, and periodic traffic patterns.
Fig. 5: Latency distributions across different traffic patterns for the simple topology : (a-d) Latency under high, heterogeneous, sparse, and periodic traffic patterns.
Fig. 6: Power consumption breakdown across different traffic patterns for the simple topology : (a-d) Power consumption under high, heterogeneous, sparse, and periodic traffic patterns.
Fig. 7: Trade-off comparison across different traffic patterns for the simple topology : (a-d) Overall performance under high, heterogeneous, sparse, and periodic traffic patterns. The metrics are normalized for comparison.
Fig. 8: Comparison of PDR across different traffic patterns for the Star topology : (a-d) PDR under high, heterogeneous, sparse, and periodic traffic patterns.
Fig. 9: Latency distributions across different traffic patterns for the Star topology : (a-d) Latency under high, heterogeneous, sparse, and periodic traffic patterns.
Fig. 10: Power consumption across different traffic patterns for the Star topology : (a-d) Power consumption under high, heterogeneous, sparse, and periodic traffic patterns.
Fig. 11: Trade-off comparison across different traffic patterns for the Star topology : (a-d) Overall performance under high, heterogeneous, sparse, and periodic traffic patterns. The metrics are normalized for comparison.
1 [respect-blocks] \RowStyle Protocol
Simple [mW]
LT [days]
Star [mW]
LT [days]
Orchestra
0.996
99
1.008
98
Orchestra-LB
1.419
70
1.453
68
PRIL-M
0.529
185
0.751
131
RL-ASL
0.561
174
0.647
152
RL-ASL-LB
0.789
124
0.856
114
TABLE IV: Average Power and Estimated Lifetime (3 V, 220 mAh Battery)
1 [respect-blocks] \RowStyle Protocol
PDR%
Radio Duty Cycle [%]
Latency [ms]
RL-ASL
93.1
2.8
186.97
RPL Baseline
96.7
3.4
184.70
TABLE V: Impact of Moderate Mobility on RL-ASL Performance
Next-generation IoT applications increasingly span across autonomous administrative entities, necessitating silo-cooperative scheduling to leverage diverse computational resources while preserving data privacy. However, realizing efficient cooperation faces significant challenges arising from infrastructure heterogeneity, Non-IID workload shifts, and the inherent risks of adversarial environments. Existing approaches, relying predominantly on centralized coordination or independent learning, fail to address the incompatibility of state-action spaces across heterogeneous silos and lack robustness against malicious attacks. This paper proposes DeFRiS, a Decentralized Federated Reinforcement Learning framework for robust and scalable Silo-cooperative IoT application scheduling. DeFRiS integrates three synergistic innovations: (i) an action-space-agnostic policy utilizing candidate resource scoring to enable seamless knowledge transfer across heterogeneous silos; (ii) a silo-optimized local learning mechanism combining Generalized Advantage Estimation (GAE) with clipped policy updates to resolve sparse delayed reward challenges; and (iii) a Dual-Track Non-IID robust decentralized aggregation protocol leveraging gradient fingerprints for similarity-aware knowledge transfer and anomaly detection, and gradient tracking for optimization momentum. Extensive experiments on a distributed testbed with 20 heterogeneous silos and realistic IoT workloads demonstrate that DeFRiS significantly outperforms state-of-the-art baselines, reducing average response time by 6.4% and energy consumption by 7.2%, while lowering tail latency risk (CVaR0.95) by 10.4% and achieving near-zero deadline violations. Furthermore, DeFRiS achieves over 3 times better performance retention as the system scales and over 8 times better stability in adversarial environments compared to the best-performing baseline.
Zhiyu Wang, Mohammad Goudarzi, Mingming Gong +1
The Quantum Cloud Computing and Distributed Systems (qCLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Australia · The Faculty of Information Technology, Monash University, Australia · School of Mathematics and Statistics, The University of Melbourne, Australia
The rapid development of the Internet of Everything (IoE) is accelerating the adoption of intelligent applications. However, the massive number of connected devices generates diverse and heterogeneous tasks, which pose increasing challenges for dynamic resource scheduling in IoE environments. Using their superior semantic understanding and reasoning capabilities, Large Artificial Intelligence Models (LAIMs) demonstrate significant potential to handle complex scheduling scenarios and improve resource utilization efficiency. This paper investigates a task-oriented LAIM-driven resource scheduling mechanism, which constructs a multidimensional scheduling decision model by integrating task semantics, network states, and constraint conditions. Furthermore, a task-oriented prompt generation method is designed to establish a deep association between task requirements and network state. In the proposed resource allocation scheme, an external evaluation and feedback module is incorporated to conduct real-time feasibility verification and performance evaluation of scheduling strategies, thus enhancing the robustness and adaptability of scheduling. Simulation results demonstrate that the proposed Large Language Model (LLM)-driven network architecture and resource allocation scheme achieve significant improvements in convergence speed, processing latency, and energy consumption, effectively enhancing IoE task responsiveness and resource utilization.
Haijun Zhang, Zhuojun Duan, Zijun Wu +2
Hebei Key Laboratory of Space-Air-Ground Intelligent Communication and Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services, University of Science and Technology Beijing, Beijing, China
We study the allocation of reconfigurable intelligent surfaces (RISs) in a multi-cell wireless network, where base stations compete for control of shared RIS units deployed at the cell edges. These RISs, provided by an independent operator, are dynamically leased to the highest bidder using a simultaneously ascending auction format. Each base station estimates the utility of acquiring additional RISs based on macroscopic channel parameters, enabling a scalable and low-overhead allocation mechanism. To optimize the bidding behavior, we integrate deep reinforcement learning (DRL) agents that learn to maximize performance while adhering to budget constraints. Through simulations in clustered cell-edge environments, we demonstrate that reinforcement learning (RL)-based bidding significantly outperforms heuristic strategies, achieving optimal trade-offs between cost and spectral efficiency. Furthermore, we introduce a tunable parameter that governs the bidding aggressiveness of RL agents, enabling a flexible control of the trade-off between network performance and expenditure. Our results highlight the potential of combining auction-based allocation with adaptive RL mechanisms for efficient and fair utilization of RISs in next-generation wireless networks.
Martin Mark Zan, Stefan Schwarz
Institute of Telecommunications, TU Wien, Vienna, Austria