Resource-Aware Cooperative Caching on Mobile Ad-hoc Peer to Peer Networks
Bibliographic record
Abstract
With the emergence of wireless devices, service delivery for ad-hoc networks has started to attract a lot of attention recently. Ad-hoc networks provide an attractive solution for networking in the situations where network infrastructure or service subscription is not available. We believe that overlay networks, particularly peer-to-peer (P2P) systems, is a good abstraction for application design and deployment over ad-hoc networks. The principal benefit of this approach is that application states are only maintained by the nodes involved in the application execution and all other nodes only perform networking related functions. On the other hand, data access applications in Ad-hoc networks suffer from restricted resources. In this thesis, we explore how to use Cooperative Caching to improve data access efficiency in Ad-hoc network. We propose a Resource-Aware Cooperative Caching P2P system (RACC) for data access applications in Ad-hoc networks. The objective is to improve data availability by considering energy of each node, demand and supply of network. We evaluated and compared the performance of RACC with Simple Cache, CachePath and CacheData schemes. Our simulation results show that RACC improves the lay of query as well as energy usage of the network as compared to Simple Cache, CachePath and CacheData.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| 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".