Data Management for Enhanced Resource Utilization in Internet of Things Systems
Bibliographic record
Abstract
Internet of Things (IoT) systems are driven by numerous Wireless Sensor Networks (WSNs) at the sensing layer supporting various applications.Due to the high volume, complexity, and velocity of IoT data and the limited resource of IoT devices, it is crucial to develop efficient data management to fulfill the required Quality-of-Service (QoS).However, managing IoT traffic based on QoS metrics is insufficient, especially when the required QoS is not attainable even after optimally utilizing all available resources.In this thesis, data analytics is used to develop metrics and methodologies for identifying the quality of IoT data in terms of predictability and contained information.The Quality-of-Information (QoI) of the collected data must be optimized while being subjected to the limited resources to ensure that IoT applications run successfully.Therefore, the QoI is exploited in data management, reduction, and forwarding.Firstly, advanced algorithms are developed for performing Dual-Prediction (DP) to reduce data based on its predictability.The proposed algorithms are based on Deep Neural Networks, which are able to outperform existing techniques in terms of reduction ratio while maintaining the same error levels in recovery.Secondly, information-oriented data reduction and forwarding are developed to maximize the Information-Content (IC) of collected data.The proposed IC metric measures data redundancy and recoverability.The developed schemes aim to improve data delivery by ensuring that all delivered data is important for running applications successfully.Throughout the writing of this dissertation, I have received a great deal of support and assistance.First and foremost, I would like to thank my supervisor, Professor Mohamed Ibnkahla, whose expertise was invaluable in formulating the research questions and methodology.Your insightful feedback and advice pushed me to sharpen my thinking and brought my work to a higher level.Thank you for all resources and time spent in developing the quality
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".