Evaluation of IoT Measurement Solutions from a Metrology Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To professionally plan and manage the development and evolution of the Internet of Things (IoT), researchers have proposed several IoT performance measurement solutions. IoT performance measurement solutions can be very valuable for managing the development and evolution of IoT systems, as they provide insights into performance issues, resource optimization, predictive maintenance, security, reliability, and user experience. However, there are several issues that can impact the accuracy and reliability of IoT performance measurements, including lack of standardization, complexity of IoT systems, scalability, data privacy, and security. While previous studies proposed several IoT measurement solutions in the literature, they did not evaluate any individual one to figure out their respective measurement strengths and weaknesses. This study provides a novel scheme for the evaluation of proposed IoT measurement solutions using a metrology-coverage evaluation based on evaluation theory, metrology principles, and software measurement best practices. This evaluation approach was employed for 12 IoT measure categories and 158 IoT measurement solutions identified in a Systematic Literature Review (SLR) from 2010 to 2021. The metrology coverage of these IoT measurement solutions was analyzed from four perspectives: across IoT categories, within each study, improvement over time, and implications for IoT practitioners and researchers. The criteria in this metrology-coverage evaluation allowed for the identification of strengths and weaknesses in the theoretical and empirical definitions of the proposed IoT measurement solutions. We found that the metrological coverage varies significantly across IoT measurement solution categories and did not show improvement over the 2010–2021 timeframe. Detailed findings can help practitioners understand the limitations of the proposed measurement solutions and choose those with stronger designs. These evaluation results can also be used by researchers to improve current IoT measurement solution designs and suggest new solutions with a stronger metrology base.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it