An Edge Computing-Based Complex Event Processing Technique for Sensor-Based Systems
Bibliographic record
Abstract
Complex Event Processing (CEP) on sensor-based systems often uses a mobile gateway agent to forward raw sensor data streams to a remote back-end server.Complex events that are triggered by multiple raw events are then detected at the back-end server.This approach relies on a persistent network connection between the back-end server and the mobile device.This thesis proposes an edge computing-based mobile CEP technique in which CEP is performed on the mobile edge device using an embedded CEP engine and the detected complex events are sent to the back-end server for further processing.A proof-of-concept prototype for this system has been built using a Siddhi CEP engine and a WSO 2 server.A thorough performance analysis is performed for comparing the proposed system with the back-end server-based system.The proposed system can handle intermittent network disconnections and leads to reduced user cost and energy consumption for the mobile device."No problem can be solved from the same level of consciousness that created it" -Albert EinsteinThe work presented in this dissertation would not have been realized without the assistance of many individuals.I take this opportunity to express my sincere gratitude to everyone who helped me during this journey.Foremost, I would like to thank my supervisors Dr. Shikharesh Majumdar and Dr. Marc St-Hilaire for their continuous guidance, motivation, and feedback throughout the process.Secondly, I would like to thank my parents for providing me with monetary support and moralistic encouragement
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".