An Accurate and Energy-Efficient Anomaly Detection in Edge-Cloud Networks
Bibliographic record
Abstract
With the wide-adoption of edge-cloud networks in various domains, anomaly detection is a regularly performed task to guarantee the health of the Internet of Things (IoT) applications. Traditionally, sensory data are gathered at the network edge and completely routed to the cloud, where computational-heavy algorithms are mostly adopted to determine the locations of certain anomaly. Considering the occurrence infrequency of anomalies, this strategy may route relatively large-volume of sensory data, which reflects a healthy situation indeed, to the cloud. To reduce these anomaly-irrelevant sensory data transmitted in the network, this paper proposes an accurate and energy-efficient anomaly detection mechanism in three-Tier IoT-Edge-Cloud (3T-IEC) networks. Specifically, after gathering sensory data provided by IoT nodes in certain edge network, the edge node applies the Marching Squares algorithm to generate isopleths, where an isopleth may capture the boundary of anomaly. A filtering mechanism is conducted at the edge tier, such that anomaly-relevant sensory data are routed to the cloud. Thereafter, the boundary of anomaly is obtained, and the locations of candidate boundary nodes are derived by adopting the Kriging spatial interpolation algorithm at the cloud tier. These locations are traversed by mobile sensing nodes at edge networks, and their sensory data are gathered to refine the boundary. Extensive experiments are conducted, where an air quality hazardous gas dataset acquired from Towards Data Science is applied. Evaluation results show that our technique outperforms the state-of-the-art ones in boundary accuracy and energy-efficiency.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".