A conceptual use-cases mapping framework for IoT-based smart building management
Bibliographic record
Abstract
Smart buildings aim to enhance user satisfaction and optimize operations through efficient facility management, employing IoT technology as a key enabler.IoT relies on sensors to collect building data, process information, and trigger actions via actuators.Despite the proliferation of IoT devices, there's a notable absence of a comprehensive framework for smart building management (SBM) in existing literature.While previous SBM frameworks focused on software, network, or data collection aspects, none address the classification of use cases for IoT devices, which form the backbone of these frameworks.The absence of a framework leads to a lack of standardized descriptions and contextual awareness of use cases, hindering research on SBM and its goal of maximizing beneficial outputs.This study addresses this gap by introducing a multidimensional conceptual framework for mapping potential IoT device use cases within the context of academic buildings.The proposed framework consists of four dimensions: (1) IoT device name and categorization, (2) building components, (3) building smartness dimensions, and (4) smart building management objectives.The study provides a detailed visual and textual representation of the framework, which is validated through four use cases, demonstrating its promising applicability in SBM.Initial observations from the framework implementation indicate its effectiveness in mapping existing sensors and identifying new potential use-cases and providing a tool for understanding and advancing the integration of IoT devices in smart buildings.This framework has the potential to serve as a communication tool for fostering collaboration among different research institutes and universities, contributing to the development of strategic SBM research programs.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".