Fair and Low Complexity Node Selection in Energy Harvesting Wireless Sensor Networks
Bibliographic record
Abstract
The use of energy harvesting in wireless sensor networks is an emerging wireless communication technology with a wide range of applications. Maximizing the number of samples collected by the sensor nodes and transmitted to the sink is a key element in order to minimize uncertainties for those applications. This work considers energy harvesting sensor nodes that are transmitting to a nonenergy harvesting sink. Using a zero-forcing (ZF) receiver, the sink selects the largest possible set of transmitting sensor nodes to maximize the received quantity of information while the selected transmissions should satisfy a given quality of service defined by signal-to-noise ratio and certain fairness constraint. The maximization problem is formulated as an integer nonlinear program and it is proved to be NP-hard. Thus, two low complexity and efficient heuristic algorithms are proposed to solve this problem. Two other variants are also proposed in order to improve the system fairness. We demonstrate via simulations in a node selection context that the proposed algorithms which consider the energy state of the system better exploit the full system resources compared to state-of-the-art algorithms which only consider channel conditions. Interestingly, simulation results show that the performance of the proposed algorithms varies as a function of the energy availability. Hence, they are adapted to the energy harvesting context.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".