Revisiting Scalability of Distributed Wireless Networks: A Multi-Hop Communication Perspective
Bibliographic record
Abstract
Large-scale distributed wireless networks provide infrastructure-free and cost-effective connectivity, supporting applications from disaster recovery to global digital inclusion. However, multi-hop communication introduces scalability challenges, as point-to-point (P2P) capacity decreases with the number of intermediate relays (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hop count</i>). The growth rate of the expected <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hop count</i> with network expansion is primarily governed by the underlying interaction patterns among network users. Thus, this study focuses on the critical role of multi-hop communication and user interaction probability, which empirical evidence shows it decays as a power law with geographic distance. We present a comprehensive analysis of network scalability, from capacity estimation to empirical evaluation of real-world interaction patterns. The capacity estimation problem is decomposed using a novel analytical methodology, along with symmetric topology selection and geometric partitioning, to overcome the complexities inherent in previous models. The estimated P2P capacity bounds, derived from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">expected</i> hop count, surpass previous benchmarks. Specifically, when the power-law exponent exceeds a critical threshold, the expected hop count remains stable and P2P capacity is sustained; otherwise, the hop count grows and capacity declines as the network scales. Accordingly, an analytical method is devised to relate real-world interaction patterns to the power-law exponent, quantified by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">contact distribution</i>. The analysis of multiple empirical datasets confirms that the exponent falls within a range that naturally supports <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scalability</i>. Consequently, multi-hop communication does not fundamentally hinder the wide-scale deployment of distributed wireless networks. This capacity-based analysis provides a clear perspective on scalability under realistic interaction patterns and underscores the promising future of such networks, as well as their potential for widespread deployment.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".