Design and Evaluation of Context-Aware and Location-Based Service Discovery Protocols for Vehicular Networks
Bibliographic record
Abstract
The increasing number of potential applications related to intelligent transportation systems (ITSs) have attracted researchers to the area of vehicular networks (VNs). Two main classes of applications have lately gained popularity, i.e., security and safety, and traffic information and service location applications. However, several open research challenges are delaying the efficient and widespread deployment and management of such applications in VNs. One of these challenges comprises how vehicles and service providers could discover each other in VNs, which are well known for their large scale and high mobility. Most service discovery strategies available present high overhead and poor performance in a VN environment. Existing context-aware and location-based service discovery protocols (LocVSDPs) are either designed without considering the particularities of VNs or are not scalable with the increase in network density and the number of requests. In this paper, we propose a new context-aware and LocVSDP (EB-LocVSDP) for VNs and its variant (Naive-LocVSDP). Our protocols offer a scalable framework for the discovery of time-sensitive and location-based services in VNs. They rely on a cluster-based infrastructure. Furthermore, LocVSDPs are integrated into the network layer and use channel diversity to improve service discovery efficiency. We discuss the implementation of our protocols and techniques, report on performance evaluation experiments, and offer a comparison against an existing location-based discovery protocol [the Vehicular Information Transfer Protocol (VITP)]. Our simulation results indicate that our proposed LocVSDPs show a gain of 20% in terms of success rate. LocVSDPs use at least 90% less bandwidth than VITP, and their average response time is at least 10% lower than VITP for successful query transactions.
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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 | 0.001 | 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 | 0.000 | 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".