Centipede: Leveraging the Distributed Camera Crowd for Cooperative Video Data Storage
Bibliographic record
Abstract
Surveillance cameras have been extensively used in smart cities and high security zones. However, with the exploding deployment of smart cameras, the rapid growth of cloud workloads from vision-based IoT applications are becoming a huge burden for all cloud service providers. Some researchers have proposed mechanisms, such as compression and deduplication to reduce the video traffic size, but these methods cannot offset the enormous growth of data volume. Most of the surveillance video data do not need to be proceeded in real time. By making use of the IoT camera’s onboard resources to store the data, the cloud workloads can be fundamentally reduced. However, recent incidents have posed a new, powerful geo-range attack, where the attacker may compromise a group of surveillance cameras located within an area. Existing simple onboard solutions cannot offer secure defense against such geo-range attacks. To tackle the problem, we developCentipede, a cooperative video data storage system that distributes video content across geographically dispersed surveillance cameras. It generates secure copies for the video content and enhances data security by judiciously distributing erasure-coded video blocks across optimally-chosen surveillance cameras. In this article, we implementCentipedeand evaluate its performance.Centipedeis the first solution that can fundamentally reduce the cloud workload and defend against geo-range attacks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".