Enhanced QoS for Real-time Multimedia Delivery over the Wireless Link using RFID Technology
Bibliographic record
Abstract
Currently, there is an increasing demand for real-time multimedia applications running over the Internet. In a wired network, if congestion occurs, the quality of real-time multimedia transmission degrades severely. Rate-based adaptation schemes are being used to alleviate congestion. Packet loss incurred is used as the main indicator of network congestion. However, when running multimedia applications over wireless/mobile networks, packet loss can be attributed to different causes other than congestion. Packet loss can be due to the low bandwidth of the wireless link or the frequent interruptions in service due to mobility, handoff, or signal propagation effects, e.g. obstruction, attenuation. In this paper, we present the design of our sensor guided wireless adaptation scheme (SGWAS) that works in a micromobility domain and that infers the main reason of packet loss incurred by the mobile node in the cell. Consequently, it takes the appropriate action to improve the QoS of the transmission. Determining the reason of packet loss relies on information obtained from wireless sensors, specifically RFlD devices, to detect the location of the mobile node within the cell. On one hand, if packet loss is due to the low bandwidth of the wireless link, which can cause local wireless link congestion, then local transmission rate adaptation is applied in the cell. On the other hand, if packet loss is due to mobility or signal propagation effects, then other appropriate actions are taken. We conducted some simulation experiments to verify that we can determine the location of the mobile node when it is in the handoff region. The results demonstrate that SGWAS identifies the reason of packet loss when the mobile node is in the handoff region. Rate adaptation should not be performed in this case because packet loss is not due to congestion
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".