Big Sensed Data Challenges in the Internet of Things
Bibliographic record
Abstract
Internet of Things (IoT) systems are inherently built on data gathered from heterogeneous sources. In the quest to gather more data for better analytics, many IoT systems are instigating significant challenges. First, the sheer volume and velocity of data generated by IoT systems are burdening our networking infrastructure, especially at the edge. The mobility and intermittent connectivity of edge IoT nodes are further hampering real-time access and reporting of IoT data. As we attempt to synergize IoT systems to leverage resource discovery and remedy some of these challenges, the rising challenges of Quality of Information (QoI) and Quality of Resource (QoR) calibration, render many IoT interoperability attempts far-fetched. We survey a number of challenges in realizing IoT interoperability, and advocate for a uniform view of data management in IoT systems. We delve into three planes that encompass Big Sensed Data (BSD) research directions, presenting a building block for future research efforts in IoT data management.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.004 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".