Vehicular Delay Tolerant Networking for Fleet Management Applications
Bibliographic record
Abstract
The objective of this thesis is the study and implementation of a Vehicular Delay Tolerant Network (VDTN) system for a fleet of vehicles, and the evaluation of its data carrying potential. The implementation relies on commodity hardware and communication using "WiFi" (IEEE 802.11) transceivers. We also detail the steps necessary for the accurate simulation of realistic, daily routines, of vehicular fleets serving an urban road network. We use as our example a fleet of service vehicles operating in the city of Lethbridge, Alberta. We analyze the dynamics of encounters among fleet vehicles throughout a typical working day, and introduce a Markovian model capturing the encounter distance dynamics. We can then translate the encounter distance dynamics to, corresponding, communication throughput dynamics. We perform data collection of IEEE 802.11 point-to-point throughput vs. distance measurements, which, in conjunction with the Markovian model, allows to derive the expected data carrying volume introduced by the VDTN. The results demonstrate that the data carrying capacity of the VDTN exceeds what is needed by typical vehicle monitoring applications. The surplus capacity can be used for delivering value-added services, such as data collection from external wireless sensor networks.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".