An energy aware coverage-preserving scheme for wireless sensor networks
Bibliographic record
Abstract
How well a large wireless sensor network can be monitored or tracked while keeping long live is a challenging problem known as the energy aware coverage preserving. Several coverage solutions have been introduced based on node scheduling and quality coverage. Node scheduling based solutions usually rely on global clock synchronization and/or time delays to resolve conflicts when determining what nodes should be turned-off to save energy. If these time delays cannot be calculated accurately blind areas might emerge jeopardizing the network coverage quality. Other challenges to node scheduling based solutions include finding optimal wakeup strategies that avoid waking up more nodes than necessary; and keeping connectivity and coverage of the network while optimizing the number of nodes. This paper extends the coverage calculation method proposed by Tian and Georganas, referred here as C-PNSS scheme, and describes a novel distributed solution based on local information exchange without the uncertainty of self-schedule algorithms. A Decision algorithm and a new node wakeup scheme were devised to overcome existing problems in actual schemes. We implement our optimal coverage-preserving scheme (OCoPS) as an extension of LEACH. A set of simulation experiments was performed to evaluate OCoPS performance when compared to LEACH and C-PNSS schemes. The results indicate that our solution outperforms C-PNSS by over 20% on network lifetime and by over 25% on network lifetime when the coverage rate is higher than 80%. LEACH is outperformed by nearly over five times on network lifetime. The experimental results also show that our coverage scheme based on our extended coverage calculation method effectively limits the on-duty node number when compared to both LEACH and C-PNSS.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".