Energy-Efficient Coalition Formation in Sensor Networks: a Game-Theoretic Approach
Bibliographic record
Abstract
The most important challenge in Wireless Sensor Networks (WSNs) is the energy constraint. Numerous solutions have been proposed to alleviate the issue, including clustering. Game theory is an effective decision-making tool that has been shown to be effective in solving complex problems. In this paper, we employ cooperative games and propose a new clustering scheme called Coalitional Game-Theoretic Clustering (CGTC) algorithm for WSNs. The idea is to partition the entire network area into two regions, namely far and vicinity, in order to address the hotspot problem in WSNs, wherein nodes close to the base station (BS) tend to deplete their energy faster due to relaying the traffic load received from farther nodes. Then, coalitional games are utilized to group nodes as coalitions. The main factor in choosing coalition heads is the energy level of nodes so that the most powerful nodes play the role of heads. The Shapley value is adopted as the solution concept to our coalitional games. The results of simulations confirm the effectiveness of CGTC in terms of energy efficiency and improved throughput.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".