DISTRIBUTED ENERGY-EFFICIENT ALGORITHMS FOR COVERAGE PROBLEM IN ADJUSTABLE SENSING RANGES WIRELESS SENSOR NETWORKS
Bibliographic record
Abstract
Due to wide range of applications of Wireless Sensor Network (WSN), lots of effort has been dedicated to solve its various issues. Among those issues, coverage is one of the most fundamental ones of which a WSN has to watch over the environment such as a forest (area coverage) or set of subjects such as collection of precious renaissance paintings (target of point coverage) and collect environment parameters and maybe, further monitor the environment. With variable sensing range, the difficulties to cover a continuous space (where number of points is infinity) in the area coverage problem becomes somewhat harder than covering limited number of discrete points in the target coverage problem. Very few papers have paid effort for the former problem. In this paper, we consider the area coverage problem for WSN where sensors can arbitrarily change their sensing ranges under some upper bound. We first improve the work in [1] so that the boundary effect is ruled out and the monitored area can be completely covered at all cases. Next, we extend that improved algorithm by introducing two distributed scheduling algorithms which are trade-off in term of network lifetime and algorithms efficiency. The major objective of each of our 3 proposed algorithms in this paper is to balance energy consumption and to maximize network lifetime. Our proposed algorithm efficiency is shown by algorithms complexity analysis and extensive simulation. In compared with the work in [1], our proposed algorithms are not only better in providing coverage quality, they could also greatly lengthen network lifetime and greatly reduce the unnecessary coverage redundancy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".