MQoSR: A Multiobjective QoS Routing Protocol for Wireless Sensor Networks
Why this work is in the frame
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Bibliographic record
Abstract
With the growing demand for quality-of-service (QoS) aware routing protocol in wireless networks, QoS-based routing has emerged as an interesting research topic. Quality of service guarantee in wireless sensor networks (WSNs) is difficult and more challenging due to the fact that the available resources of sensors and the various applications running over these networks have different constraints in their nature and requirements. In this paper, we present a heuristic neighbor selection mechanism in WSNs that uses the geographic routing mechanism combined with the QoS requirements to provide multiobjective QoS routing (MQoSR) for different application requirements. The problem of providing QoS routing is formulated as link, and path-based metrics. The link-based metrics are partitioned in terms of reliability, delay, distance to sink, and energy, and the path-based metrics are presented in terms of end-to-end delay, reliability of data transmission, and network lifetime. The simulation results demonstrate that MQoSR protocol is able to achieve the delay requirements, and due to optimum path selection process, the achieved data delivery ratio is always above the required one. MQoSR protocol outperforms the existing model in the literature remarkably in terms of reliable data transmission, time data delivery, and routing overhead and underlines the importance of energy-efficient solution to enhance network lifetime.
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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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 it