Joint Routing and MAC Layer QoS-Aware Protocol for Wireless Sensor Networks
Bibliographic record
Abstract
In this paper, we propose a novel joint routing and medium access control (MAC) protocol with traffic differentiation, based on quality of service (QoS) for wireless sensor networks (WSNs). This is referred to as joint routing and MAC (JRM) protocol. By leveraging the classical layered approach and combining routing and MAC layer functions, the proposed JRM protocol achieves a solution for energy efficiency in WSNs. JRM also ensures low latency for prioritized traffic. There are three major advantages of the proposed protocol. Firstly, the instantaneous network information (e.g., estimated time to destination, and node's unavailability to forward additional packet) is piggy-backed with the control packets acknowledgement and clear- to-send of the MAC frame. Based on the updated network knowledge and the objective of the required performance metrics (e.g., energy and latency), the next hop neighbor is chosen dynamically with reduced control overhead. Secondly, the JRM protocol introduces an approach for finding the constrained shortest path for forwarding packets, which results in load balancing in WSNs. Finally, routers (nodes) in JRM require very little forwarding and routing table information, which is compatible with the resource constraints in WSNs. The efficiency of the proposed protocol is shown through simulation results.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".