Survey of Internet of Things (IoT) Infrastructures for Building Energy Systems
Bibliographic record
Abstract
This paper discusses a revolutionary system of interrelated computing devices and technologies known as the Internet of Things (IoT), with the objective of improving energy efficiency for both residential and commercial buildings. A thorough review of a profusion of empirical surveys and studies was carried out to fully comprehend the use of IoT to improve energy efficiency in buildings. The present study also examines methods by which these technologies can be further enhanced to provide solutions that are more requisite. Most of the studies reviewed discuss the applicability of various aspects of IoT to a specific application, and were invariably concerned with the problem of efficient heating systems in buildings. Some aspects of the core elements of IoT, such as the hardware and software required for control are analyzed in all the studies reviewed. Although the kind of application in each study is different, similar patterns are discernible in their design factors. Some common design factors include: the choice of sensors and actuators and their powering methods; control strategies for gathering data and actuating devices; collection of historical data to predict future energy use; and interaction approaches between IoT elements. Results showed that certain variables are application-specific and must all be factored into the decisions. Finally, from the studies surveyed, some areas of future research were recommended.
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.
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 | 0.000 | 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 | 0.000 | 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 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".