ABSRP- A Service Discovery Approach for Vehicular Ad Hoc Networks
Bibliographic record
Abstract
A vehicular ad hoc network (VANET) is a network of intelligent vehicles that communicate with other vehicles in the network. The main objective of VANET is to provide comfort and safety for passengers. In addition, various transaction based services, such as information about gas prices, restaurant menu, and discount sale, can be provided to drivers. In order to make these services available, there is a need for an efficient service discovery protocol. In this paper, we propose a new protocol called Address Based Service Resolution Protocol (ABSRP) to discover services in vehicular ad-hoc networks. As most of the transaction based services are provided by roadside units, we exploit their presence to perform service discovery. We utilize the unique address assigned to each service provider in order to discover a route to that service provider. Our technique proactively distributes the service provider's address along with its servicing capabilities to other roadside units within a particular area. Each roadside unit will then utilize this information to service the request placed by the vehicles. If the service provider (destination node) is not reachable over the vehicular network, we propose to use a backbone network to service requests. Our approach is independent of the network layer routing protocol. We have evaluated the performance of our approach by using the Qualnet simulation tool.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".