Mobility-Based Generic Infrastructure for Large Scale Sensor Network Architecture
Bibliographic record
Abstract
Building efficient general purpose sensor network infrastructure that could be leveraged by upper layer protocols is still an open research problem. In this paper, mobility is exploited to organize sensor nodes into generic efficient infrastructure. We propose layered infrastructure protocol (LIP) that allows mobile robots to organize the network nodes into co centric circular layers. Once the network is organized, the mobile robots are assigned layers to serve called home service layers where they act as moving probes to access the data and monitor the layers. Access positions are selected dynamically at each layer to provide anchors for the probes to visit in their home service layers. Probes cooperate to perform the application requests by executing a communication plan that is provided by the upper layer applications. The protocol is greatly able to cope with failures and requires only local updates for maintenance. We show that the proposed protocol provides a flexible infrastructure that keeps the nodes proximity and could be leveraged by upper layer protocols. To evaluate the performance of the proposed infrastructure some upper layer applications are implemented and built over the proposed infrastructure. Simulation-based results show the robustness and efficiency of the implemented applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".