Mobility-Based Strategies for Energy Restoration in Wireless Sensor Networks
Bibliographic record
Abstract
Energy management has become one of the main hurdles in the quest for autonomous and reliable Wireless Sensor Networks (WSN). This paper examines the emerging problem of increasing network availability by recharging, replacing or redeploying depleted sensors with the help of mobile entities. When mobility becomes a sensor's attribute and service stations are static, we propose passive vs. pro-active approaches to energy redistribution and restoration. In particular, for pro-active approaches, we study the mobility strategies and underlying topologies that guarantee a successful sensor recharge. The experimental results so far show that taking our novel pro-active approach to energy redistribution and network fatigue outperforms passive strategies. The proposed closest-first swapping-based mobility strategy provides the best overall performance among all the pro-active approaches studied and the proposed Compass Directed Unit Graph provides an efficient and flexible underlying topology to achieve energy equilibrium.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".