Formation and inter-piconet communications in cognitive personal area networks
Bibliographic record
Abstract
This dissertation presents a new approach for achieving group rendezvous with a coordinator node towards forming a Cognitive Personal Area Network (CPAN) by an arbitrary number of nodes. We propose a protocol for the time to form CPAN in which the nodes join the coordinator simultaneously instead of sequentially. Specifically, we develop an analytical model and derive the distribution of time to form CPAN under the considerations of random arrivals of nodes and their random times to rendezvous with coordinator. We also investigate the CPAN formation time by considering the random activity of primary user (PU). Besides operating in a CPAN, the nodes may have traffic destined to the nodes of other CPAN. In this dissertation, we also propose a bridging protocol in which a shared (bridge) node routes the inter-CPAN traffic between two CPANs. As the bridge node shares its time between two CPANs, the bridge traffic gets priority over that of ordinary nodes in both CPANs. We consider a single, unidirectional bridge because the traffic in the opposite direction can easily be accommodated by having another bridge node. We develop an analytical model based on probabilistic modeling and queueing theory to evaluate the performance of the bridging protocol. We validate the network performance by analyzing the waiting time of local and non-local packets and how the node or bridge transmission is affected by the collision with primary source activity. Finally, we propose a low-overhead two-way bridging scheme for two-hop CPANs, which is more realistic and can be used a basis for routing inter-CPAN traffic in a multihop network. In this advance bringing protocol, the bridge switches between the CPANs without any predefined arrangement, which resulted in simplified bridge scheduling and increased fairness for all nodes. We also analyze its performance through probabilistic analysis and renewal theory. We show that the CPANs are indeed decoupled in terms of synchronization, however the performance of both local and non-local traffic in either CPAN depends on the traffic intensity in both CPANs as well as on the portion of traffic targeting non-local destinations
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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 | 0.000 | 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 | 0.003 |
| 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".