Bibliographic record
Abstract
The indoor inventory system is gaining more research attention and commercial value with the development of IoT. In this thesis, we presented the design of a MAC protocol that allows synchronized transmission of location and sensing data in a wireless positioning and sensor network for an indoor inventory system. The network supports real-life industrial applications and provides a highly specific positioning method. In the network, mobile sensing tags are connected to smart readers that performs localization of tags and gathers sensing data from the tags. The readers are connected to the back-end cloud. The proposed MAC serves multiple classes of mobile tags with different priorities and latency requirements. These tags transmit critical, position and sensing data with different QoS requirements. The proposed MAC is a hybrid MAC that offers contention-based period for tag discovery and scheduled period for the transmission of sensing data with guaranteed latency. We conducted simulation to evaluate the performance of different methods of discovery process and their impact on latency assurance. We also developed a queuing model to analyze the relationship between parameters, acquiring parameters through experiment, and calculation of boundary values. Simulation using MatLabTM software suggests that the joining period in design can increase the transmission success rate of high priority messages at the cost of a slight increment in the delay of low priority messages. Preliminary analysis suggests that by adaptively allocating the channel resources of the network to three types of tags, service efficiency can be improved. This result also guides the direction for further improvement. We explored the performance of two options considered currently, which is selecting the discovery process according to modulo result of unique 16-bit tag ID and random select of an available discovery process. In the current environment where each tag does not have any information about other tags inside the network, the two methods have the same effect on avoiding collisions that could happen in a single discovery cycle. The proposed MAC layer protocol can provide the best service when the available discovery process in the discovery cycle is for initialization and resetting. For an emergency, the joining period designs can still ensure a success rate for critical messages to be over 90%. Hence, the simulation results indicate the joining period method is able to improve MAC-layer performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".