Data Capacity Improvement of Wireless Sensor Networks Using Non-Uniform Sensor Distribution
Bibliographic record
Abstract
Energy conservation is an important design consideration for battery powered wireless sensor networks (WSNET). Energy constraint in WSNETs limits the total amount of sensed data (data capacity) received by sinks. In the commonly used static model of sensor networks with uniformly distributed homogenous sensors with a stationary sink, sensors close to the sink drain their energy much faster than sensors far away from the sink due to the unevenly distributed forwarding workloads among sensors. A major issue, which has not been adequately addressed so far, is the question of how sensor deployment governs the data capacity, and how to improve data capacity of WSNETs. In our previous work, we provided a simple analytical model to address this issue for one specific type of WSNETs. In this paper, we extend our previous work to address this issue for general WSNETs. In the extended static models, for large networks, we find that after the lifetime of a sensor network is over, there is a great amount of energy left unused, which can be up to 90% of the total initial energy. Thus, the static models with uniformly distributed homogenous sensors cannot effectively utilize their energy. This energy waste implies that the potential data capacity is much larger than the capacity achieved in these static models. To increase the total data capacity, we propose a non-uniform sensor distribution strategy. Simulation results show that, for large, dense WSNETs, the non-uniform sensor distribution strategy can increase the total data capacity by an order of magnitude.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".