Replication schemes for peer-to-peer content in wireless mesh networks with infrastructure support
Bibliographic record
Abstract
Many mobile devices e.g., smart phones, PDAs, portable computers and wireless routers e.g., WiFi access points nowadays are equipped with ad hoc transmission mode. In a dense environment such as a college/office campus, this creates the possibility of forming a wireless mesh network WMN in which mobile users communicate with each other through multiple wireless hops. This allows mobile users to exchange share files over the free access WMN rather than a carrier frequency such as 3G and WiMax. We consider a peer-to-peer P2P content sharing setting in a WMN, wherein the mesh network operator over-provision a number of mesh routers in the network with additional storage capacity and P2P-aware devices that are programmed to cache and store P2P content. Those mesh routers act as caches and participants in P2P content sharing. The aim of this setting is to both reduce the cost of communications between peers within the WMN i.e., reduce bandwidth and energy that P2P traffic consumes in the network, and enhance the performance of P2P content sharing i.e., reduce the average P2P content download delay. Our main contribution in this paper is an optimum P2P content replication strategy at the participating mesh routers. In particular, we determine the optimum number of replicas for every P2P file such that the average access cost of all files in the network is minimized. We propose a centralized algorithm that enables the participating mesh routers to implement the optimal strategy. We further propose a distributed low cost algorithm for P2P content replication at the participating mesh routers, and show that the distributed algorithm mimics the optimal strategy very well. The analytical and simulation results show that our replication strategy significantly reduces the average overall cost of accessing P2P files in the WMN as compared with other commonly used replication strategies. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".