Feedback based real-time MAC (RT-MAC) protocol for data packet streaming in wireless sensor networks
Bibliographic record
Abstract
Wireless sensor networks (WSNs) are generally used for event driven monitoring or periodic reporting. Once a triggering event happens, it needs to be reported in real-time as a continuous stream for some duration. In order to address such communication requirements, this thesis introduces a soft Real-Time MAC (RT-MAC) protocol for real-time data packet streaming in wireless sensor networks. RT-MAC eliminates contention for a wireless medium by introducing a feedback control packet, called Clear Channel (CC). As a result, RT-MAC has a consistent and predictable data transmission pattern that provides end-to-end delay guarantees. Additionally, RT-MAC has a lower end-to-end delay than other real-time WSN MAC protocols for two reasons: (1) it maximizes spatial channel reuse by avoiding the false blocking problem caused by request-to-send (RTS) and clear-to-send (CTS) exchanges in wireless MAC protocols (2) it reduces contention duration of control packets to facilitate faster data packet transfer. Thus, RT-MAC facilitates periodic data packet deliveries as well as alarming event reporting. RT-MAC operates both with and without duty cycle mode (sleep/wakeup schedule for sensor nodes). Duty cycle mode of RT-MAC is useful in situations where energy conservation is one of the goals along with real-time requirements. RT-MAC is well suited for multi-hop communication with a large number of hops. RT-MAC protocol supports single-stream communication between a randomly selected source and sink node pair as well as multi-stream communication among different source and sink node pairs. This thesis provides the lower and upper end-to-end delay bounds for data packets transfer in normal mode of operation of RT-MAC protocol. We used state diagram analysis to show the in-depth functioning of RT-MAC protocol. This thesis also presents Markov analysis of RT-MAC that shows the behavior of the protocol in fault scenarios. Extensive simulation results are also presented in this thesis. These results show significant improvement in delay, packet throughput performance, and uniformity in packet transmission pattern at a cost of a very small increase in energy consumption as compared to other real-time MAC protocols such as VTS and general purpose MAC protocols such as S-MAC and T-MAC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 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".