Secure OTA Software Updates for Connected Vehicles Using LoRaWAN and Blockchain
Bibliographic record
Abstract
Over-the-Air (OTA) software updates are becoming essential for connected vehicles, allowing remote updates that eliminate the need for physical access to each vehicle. This significantly reduces downtime and operational costs while keeping vehicular software up-to-date against emerging threats and technological advancements. However, this conventional approach is susceptible to various challenges, including security vulnerabilities, limited connections, and potential network unreliability, which may impede the effective deployment of updates. To address these issues, we investigate the integration of LoRaWAN with blockchain technology and the InterPlanetary File System (IPFS) for the connected vehicles. LoRaWAN offers long-range communication with end-to-end encryption, while blockchain provides transparency and IPFS offers efficient’ distributed storage. This dual-layered security approach also benefits from LoRaWAN's coverage and low power usage, and blockchain's tamper-proof nature. Furthermore, this paper assesses the feasibility of LoRaWAN through simulations, to determine the ideal conditions for effective OTA execution utilizing LoRaWAN technology in the connected vehicles. Our findings indicate that this approach is viable for updates targeting performance enhancements and a wide range of applications.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".