Smart city platforms on multitier software-defined infrastructure cloud computing
Bibliographic record
Abstract
We identify requirements and then propose a multi-layer architecture for smart city platforms (SCPs) based on an infrastructure layer operating as a multi-tier computing cloud using software-defined infrastructure (SDI), a platform layer providing data dissemination, and a Business-Intelligence-as-a-Service (BIaaS) layer offering analytics services to support smart city applications through open APIs. The multitier cloud enables the end-to-end orchestration of resources from sensor/actuator to smart edge and to core massive datacenter. The SDI provides the necessary heterogeneous resources, including computing, storage, GPUs, programmable hardware, sensors and networking, that are all managed jointly within a single domain or across multiple domains through federation. The data dissemination services allow individuals, organizations, and public agencies to share real-time or historical data within a domain or across domains. The analytics services provide intelligence by acting on data in support of decision-making. Public and private application providers use these services to create smart city applications. We discuss our experience implementing and operating a Canada-wide multitier SDI cloud and a Greater Toronto Area (GTA) platform for smart transportation, and we present extensions of these platforms to handle smart city applications across multiple domains. Throughout we describe how the proposed smart city platform supports extensibility and smartness, and how it has the desired attributes of replicability, scalability and interoperability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".