Integrated Cognitive Symbiotic Computing and Ambient Backscatter Communication Network
Bibliographic record
Abstract
Ambient backscatter communication (AmBC) possesses signal reception and energy-harvesting capabilities, allowing providing wireless cognition through simple energy detection. In typical applications like industrial Internet of Things (IoT), cognitive AmBC (CAmBC) networks are required to offer passive communication, edge computing, and cognition capabilities. However, passive communication relies on the environment and has limited computing power, creating interdependencies among spectrum sensing, networking, and computational cognition. Moreover, the heterogeneous evaluation metrics for communication and computation make unified planning and management challenging. Therefore, this paper proposes the integrated cognitive symbiotic computing-AmBC (CSC-AmBC) based on symbiotic communication and cognitive radio. CSC-AmBC integrates AmBC communication and computational cognition capabilities in a task-oriented manner, sharing proximity and AmBC computing and communication (ACC) resources among primary and secondary tasks. Meta-Link with Tokens and two cognitive ACC reuse models is used to facilitate integration and enhance task execution efficiency, which introduces Places to accommodate the heterogeneous and variable ACC resources. Additionally, the task execution gain metric is introduced to evaluate the multi-task ACC resource utilization. Numerical results validate the cognition networking and the advantage of the proposed task execution gain of CSC-AmBC.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".