Machine To Machine Overlay Network Over Random Access Channel Of LTE For Smart City
Bibliographic record
Abstract
The purpose of this dissertation work is to investigate the perspective of the application areas of machine to machine (M2M) communications specifically to build smart city. To have ubiquitous coverage over the city, we consider cellular infrastructure. We propose a M2M overlay network over the physical random access channel (PRACH) of LTE. Based on this architecture, we conduct a case study in a vehicular context and proposed a non-priority CSMA/CA based vehicular M2M (VM2M) overlay networks. The overlay architecture is implemented using a dedicated subset of preambles at the physical layer, and a carrier sense multiple access with collision avoidance (CSMA/CA) mechanism similar to the one used in IEEE 802.15.4 at the medium access control (MAC) layer. We evaluate the performance and interaction of regular human to human (H2H) traffic and VM2M traffic, in particular, the impact of RACH resource configuration and preamble format (PF) in large cells. We have shown that the proposed LTE based VM2M architecture is better suited for smart city scenarios with higher vehicular speed and larger distances without sacrificing performance of H2H traffic. To transmit priority messages with high data rate and more reliably, we also propose a novel priority based CSMA/CA machine to machine (PM2M) overlay network over LTE. The PM2M overlay‘s MAC is more sophisticated with priorities. The architecture is same in the physical layer with dedicated preambles and in MAC layer priority based CSMA/CA is used, which is similar to the one of IEEE 802.15.6. Finally, We propose an analytical model to evaluate the impact of error in the MAC layer of overlay networks during sensing the medium. We develop a 3 dimensional Discrete Time Markov chain (DTMCs) in order to model the backoff procedure of CSMA/CA mechanism with backoff error. We investigated the capacity of PM2M networks for with and without considering backoff error. We have shown that overlay network allows fair coexistence of PM2M and H2H traffic. To validate our scheme we compare the proposed approach with a reference approach. By performance evaluation, we have shown that PM2M overlay out performs compared with reference approach.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".