E2DNE: Energy Efficient Dynamic Network Embedding in Virtualized Wireless Sensor Networks
Bibliographic record
Abstract
Efficient utilization of resources is an important challenge in traditional non-virtualized Wireless Sensor Networks (WSNs), as the applications are embedded in the sensors, precluding the sensor nodes from being re-used by other applications. Inefficient utilization of resources results in high deployment and maintenance costs. Virtualization is a promising technology that allows multiple sensing tasks from diverse applications to be executed on the same deployed WSNs concurrently. However, given that the WSN sensors are constrained with limited energy, the allocations of physical and virtual resources to the applications in an efficient manner becomes a challenge, especially for mission-critical, delay-sensitive applications. In this paper, we address the problem of virtual network embedding in virtualized WSNs, aiming at minimizing the overall energy consumption while considering the end-to-end latency and bandwidth consumption as the service level agreement (SLA) constraints. We propose our Dynamic Network Embedding (DNE) heuristic for large-scale problem instants. The results reveal that our proposed heuristic leads to close-to-optimal solutions with satisfactory execution time. Our results indicate that the proposed heuristic achieves up to an 18% optimality gap in energy consumption in the small-scale scenario, and outperforms the existing benchmark by up to 58% in the large-scale scenario.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".