Design Guidelines for Maximizing Lifetime and Avoiding Energy Holes in Sensor Networks with Uniform Distribution and Uniform Reporting
Bibliographic record
Abstract
Abstract — This paper investigates theoretical aspects of the uneven energy depletion phenomenon recently noticed in sink-based wireless sensor networks. We consider uniformly distributed sensors, each sending roughly the same number of reports toward the closest sink. We assume an energy consumption model governed by the relation E = dα +c where d, (d ≤ tx), is the transmission distance, α ≥ 2 is the power attenuation, c is a technology-dependent positive constant, and tx is the maximum transmission range of sensors. Our results are multifold. First, we show that for α> 2, all sensors whose distance to the sink is min{tx, ( 2c 1 α−2) α} should transmit directly to the sink. Interestingly, this limit does not depend on the size of the network, expressed as the largest distance R from a sensor to the closest sink. Next, we prove that in order to minimize the total amount of energy spent on routing along a path originating at a sensor in a corona and ending at the sink, all the coronas must have the same width, equal to the above expression. This choice, however, leads to uneven energy depletion and to the creation of energy holes. We show that for α>2 the uneven energy depletion can be prevented by judicious system design, resulting in balanced energy expenditure across the network. We describe an iterative process for determining the sizes of coronas. Their optimal sizes (and corresponding transmission radii) and the number of coronas depend on R. As expected, the width of coronas in energy-balanced sensor network increases. Finally, we show that for α =2, the uneven energy depletion phenomenon is intrinsic to the system and no routing strategy can avoid the creation of an energy hole around the sink. I.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".