Prediction-based Schemes for Coexistence in Personal Wireless Networks
Bibliographic record
Abstract
The wide spread of sensor networks enforces the need to provide a new technology that supports the emerging characteristics of sensors. This has geared IEEE 802.15.4 standards committee toward working on the physical and data link layers in cooperation with ZigBee Alliance that has worked on the network and application layers. The new cooperation, named ZigBee, considers sensors characteristics in the full design. Numerous home applications are guiding the need for communication protocols supporting the new emergent characteristics of Wireless Sensor Networks (WSN), which demand ultra low rate, low power consumption and low cost. ZigBee operates in the license-free 2.4 GHz Industrial, Scientific and Medical (ISM) band that is used by many other standards such as, Bluetooth IEEE 802.15.1, and WLAN IEEE 802.11b. This paper proposes two smart schemes that enable the communication in environments with ZigBee and WLAN devices in order to achieve higher delivery ratios while maintaining acceptable end-to-end delay values. The proposed schemes carryout essential predictions to achieve fairness among ZigBee and WLAN transmissions, therefore, attain better load balancing among devices of different networks. Simulation results show that the proposed smart and opportunistic schemes increase the packet delivery ratio of ZigBee devices for different scenarios. In addition, the proposed schemes enhance the performance of WLAN devices by increasing the packet delivery ratio, while maintaining an acceptable level of end to end delay values.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".