Improving Energy Efficiency and QoS of LPWANs for IoT Using Q-Learning Based Data Routing
Bibliographic record
Abstract
Recent proliferation of Internet of Things (IoT) demands large scale connectivity among smart IoT devices over a vast geographical area. However, limited radio range and lack of scalability of conventional wireless sensor networks do not allow a wide area connectivity among IoT devices. To address these challenges, Low-Power Wide-Area Networks (LPWANs) are emerging to provide long-range communication capability with low-power consumption of the end devices. Nevertheless, given the demand in delivering an increasingly large volume of data generated by IoT devices, the direct data transmission model is not suitable due to its poor network lifetime. Therefore, in this work, a multi-hop data routing method is proposed for LPWANs. Since multi-hop data transmission faces several challenges such as increased data latency, higher interference, and reduced data throughput (i.e., poor bandwidth utilization), we propose a reinforcement learning method to address those challenges. The proposed method updates the Q-matrix of the network at varying discrete time instants and selects relay devices in such a way that maximizes the cumulative reward value between selected device-gateway pairs. The applicability and effectiveness of the proposed method are illustrated over both simulated LPWAN testbed and real field data sets. The obtained results clearly demonstrate the improved network performance in terms of energy efficiency and QoS of the proposed method as compared to various existing methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".