A New Block-Based Reinforcement Learning Approach for Distributed Resource Allocation in Clustered IoT Networks
Bibliographic record
Abstract
Resource allocation and spectrum management are two major challenges in the massive scale deployment of Internet of Things (IoT) and Machine-to-Machine (M2M) communication. Furthermore, the large number of devices per unit area in IoT networks also leads to congestion, network overload, and deterioration of the Signal to Noise Ratio (SNR). To address these problems, efficient resource allocation play a pivotal role in optimizing the throughput, delay, and power management of IoT networks. To this end, most of the existing resource allocation mechanisms are centralized and do not gracefully support the heterogeneous and dynamic IoT networks. Therefore, distributed and Machine Learning (ML)-based approaches are essential. However, distributed resource allocation techniques also have scalability problem with large number of devices whereas the ML-based approaches are currently scarce in the literature. In this paper, we propose a new distributed block-based Q-learning algorithm for slot scheduling in the smart devices and Machine Type Communication Devices (MTCDs) participating in clustered IoT networks. We furthermore, propose various reward schemes for the evolution of Q-values in the proposed scheme and, discuss and evaluate their effect on the distributed model. Our goal is to avoid inter- and intra-cluster interference, and to improve the Signal to Interference Ratio (SIR) by employing frequency diversity in a multi-channel system. Through extensive simulations, we analyze the effects of the distributed slot-assignment (with respect to varying SIR) on the convergence rate and the convergence probability. Our theoretical analysis and simulations validate the effectiveness of our proposed method where, (i) a suitable slot with acceptable SIR levels is allocated to each MTCD, and (ii) IoT network can efficiently converge to a collision-free transmission causing minimum intra-cluster interference. The network convergence is achieved through each MTCD's learning ability during the distributed slot allocation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".