Secure OTA Software Updates for Connected Vehicles Using LoRaWAN and Blockchain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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 it