Enhancements to IEEE 802.15.4 MAC Protocol to Support Vehicle-to-Roadside Communications in VANETs
Bibliographic record
Abstract
The Internet of Things (IoT) paradigm and its applications have been gaining popularity recently. The Intelligent Transportation System (ITS) is a major area of IoT applications. With ITS the transportation infrastructure is supported with advanced networking and computing technologies to better manage traffics on the roads. The Vehicular Ad-Hoc Network (VANET) stands out as an important technology under the ITS. The VANET technology supports different architectures for data communication, namely, vehicle-to-vehicle (V2V), vehicle-to-road-side (V2R), vehicle-to-infrastructure (V2I), and infrastructure-toinfrastructure (I2I). In V2R communication, data flow between vehicles and roadside units (RSUs) to convey important information about the road traffic and emergency situations. This data should be transferred with high probability of successful delivery. Also, the sensitivity of this data requires reducing the end-to-end communication delay. The IEEE 802.15.4 standard is one of the important candidate standards that supports the V2R communications. In this paper, we propose the Dynamic Window Algorithm (DWA); a backoff algorithm that targets improving the performance of V2R communications in terms of throughput and delay. This is attained by proposing changes to the operation of the standard Binary Exponent Backoff (BEB) algorithm (in IEEE 802.15.4 MAC). A Java-based simulation tool has been developed to simulate both BEB and DWA algorithms and conduct a comparison study between them. Our results show that in clusters of 20 nodes, the performance in terms of throughput and delay is improved by 32% and 88%, respectively, with DWA.
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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) | 0.001 | 0.010 |
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; both teacher heads agree on what is shown here.
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".