Green content distribution in Wireless Mesh Networks with infrastructure support
Bibliographic record
Abstract
We consider the problem of energy consumption in sharing a viral file between peers over a wireless community network (e.g., students in campus). Wireless community networks such as Wireless Mesh Networks (WMNs) have been accepted as a new communication approach that enables users to share the network resources and reduce the cost of the Internet access. The common paradigm for sharing content between users in a community network is through the use of a centralized server. Another scheme is to exploit the upload capacity of peers who are interested in the same content (e.g., Peer-to-Peer (P2P) file sharing). In this paper, we consider a content distribution setting in a wireless mesh network wherein a number of infrastructure nodes (mesh routers) support the P2P content sharing and act as caches and participants. We model the performance of this content distribution scheme, and analytically compute the energy that is consumed in the network when a viral P2P object is shared between the peers in a WMN. We compare the energy consumption with the centralized server scheme using both numerical results and detailed simulations. The results shows significant reduction in energy consumption (more than an order of magnitude) when only few replicas of the object is cached at the infrastructure nodes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".