A Lifetime-Aware Centralized Routing Protocol for Wireless Sensor Networks using Reinforcement Learning
Bibliographic record
Abstract
This paper presents the design of a Lifetime-Aware Centralized Q-routing Protocol (LACQRP) for Wireless Sensor Network (WSN) to maximize the network lifetime. This is achieved by implementing Q-learning on the sink of the WSN, which also acts as a controller that has global knowledge of the network topology as enabled by Software-Defined WSN (SDWSN). The controller generates all possible distance-based minimum spanning trees (MSTs), which form the set of routing tables (RTs). The maximization of the network lifetime is achieved by the controller learning the routing table that minimizes the maximum of the sensor nodes’ consumption energies using Reinforcement Learning (RL). The simulation results show that the LACQRP learns the best RT that maximizes the network lifetime and has a better network lifetime performance when compared with recent distributed RL routing protocols for lifetime optimization, which are Reinforcement Learning-Based Routing (RLBR) and Reinforcement Learning for Lifetime Optimization (R2LTO).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".