Prolonging Network Lifetime via Nodal Energy Balancing in Heterogeneous Wireless Sensor Networks
Bibliographic record
Abstract
Practical implementation of balanced data routing algorithms in WSNs is challenging because of the heterogeneity among nodes inherited from the physical world in forms of different amount of nodal traffic, residual energy, data transmission rate and bandwidth. As the main concern in sensor networks is preserving nodes' energy, such algorithms should balance energy depletion among nodes by carefully considering the impact of aforementioned heterogeneities to prolong the network lifetime. In this paper, a distributed energy balanced algorithm for data gathering and routing is proposed aiming to construct energy balanced routing trees in a network that contains heterogenous nodes. For this purpose, a game theoretical approach in which nodes can be selfish or cooperative players based on their roles in the network. Utility functions use local information of nodes and they are defined in a way that, while each node in selfish mode tries to achieve the most individual benefit, it implicitly helps to construct a balanced tree for the entire network. Evaluation and simulation results show noticeable improvement in generating more energy balanced routing trees, resulting longer network lifetimes compared to similar work in the literature.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".