A Novel Hierarchical Two-Tier Node Deployment Strategy for Sustainable Wireless Sensor Networks
Bibliographic record
Abstract
Wireless sensor networks (WSNs) have been widely adopted to fulfil the imperative requirement of real-time monitoring and/or long-term surveillance of the field-of-interest. However, due to the limited battery capacity, energy is the most critical constraint for improving the sustainability of a WSN. Hence, conserving energy and extending battery life are important in designing a sustainable WSN. Fortunately, the emerging energy harvest techniques provide us with a semi-permanent energy resource to power WSNs. In this article, we introduce a novel energy-aware hierarchical two-tier (HTT) energy harvesting-aided WSNs deployment scenario. More precisely, we consider two types of nodes in the system: one is the regular battery-powered sensor node (RSN), and the other is the energy harvesting-aided data relaying node (EHN). The objective is to use only RSNs to monitor FoI, while EHNs focus on collecting the sensed data from RSNs and forwarding the gathered data to the data sink. The minimum number of EHNs is deployed based on a newly designed probability density function to minimize the energy consumption of RSNs. This, in turn, extends the lifetime of the deployed WSN. The simulation results indicate that the proposed scheme outperforms some well-known techniques in the network lifetime, while enhancing the total throughput.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".