A Resilient P2P Architecture for Mobile Resource Sharing
Bibliographic record
Abstract
Peer-to-peer (P2P) systems present a unique medium for resource sharing among cliques of participants (peers) in a distributed and self-organized manner. With the advent of mobile users and the increasing power of mobile devices, the spectrum of P2P capabilities should scale. Peers establish transient or persistent relationships with other peers based on mutual interest. Communicating peers may use intermediary peers to forward communication messages, if a direct link is beyond their communication range. A critical design parameter is establishing a resilient communication topology, yet reduce the overhead of control messages required to instill and maintain it. This rises as a significant hindrance in mobile environments, which pose additional challenges on P2P networks due to the heterogeneity of nodes, limited resources, dynamic contexts in addition to the inherited wireless network stringencies. Thus far, efforts in establishing P2P networks via super peers (SPs) have been capped by considering a subset of peer properties to evaluate their candidacy. This paper presents RobP2P, a robust architecture to construct mobile P2P networks and efficiently maintain network state. RobP2P introduces a SP selection protocol based on a dynamic score function that takes into account peers’ capabilities and context, such as location and quality of connectivity. The paper also presents an agile utility function through which SPs can delegate monitoring responsibilities to comparably powerful and stable peers to ensure self-healing topology maintenance. We present an elaborate performance evaluation of RobP2P implemented on Network Simulator NS-3. Our results illustrate the efficiency of RobP2P, its resilience to failures, and the improvements in lowering overhead traffic while reliably maintaining the consistency of network state.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".