Improved Reinforcement Learning for Reliable Routing in Medical Wireless Sensor Networks
Bibliographic record
Abstract
The medical field is one of the various evolving areas of wireless sensor network (WSN) applications.WSN is a self-creating entity that requires no pre-infrastructure support for data exchange, and this special characteristic of WSN is used for monitoring vital parameters of patients in hospitals.However, the latency and packet loss issues in WSN are critical to the monitoring of sensitive vital parameters.To develop reliable data exchange for WSN, a low-risk reliable routing (LRRR) approach is proposed.The LRRR method proposes an updated reward metric in reinforcement learning for optimal clustering and head selection in the WSN interface.In addition to the existing interface unit in WSN, a decision unit for measuring the packet forwarding factor is proposed.The proposed monitoring factor improves the existing reward metric with reference to the packet forwarding conditions in the network.An updated reward factor improves the reliability of packet exchange by monitoring the energy and forwarding condition of a node in the network.Performance of WSN communication using the LRRR method in vital parameter monitoring observed an improvement in network throughput of 30% and network life time by 13 msec.A decrease in the end-to-end (E2E) delay is observed for 5 sec.compared to existing cluster-based routing approaches in WSN.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".