SmartCity 2015: The first international workshop on smart cities and urban informatics 2015 - program
Bibliographic record
Abstract
Emerging smart cities will leverage information and communication technologies (ICT) to address urban challenges and improve the well-being of citizens. A key development in ICT is the Internet of Things (IoT), which extends the Internet to connect not only computers and smart devices carried by people, but "things" with embedded sensors, actuators, and networking capabilities. IoT will enable many new services and applications such as smart grids, intelligent transportation, e-health, smart homes/buildings/offices/factories, which will be an integral part of the future smart cities. Of particular interest are applications that contribute to the global efforts towards a greener society by reducing energy consumption, shortening travel times, etc. In many applications, IoT will employ embedded radios and wireless machine-to-machine communications to enable ubiquitous connectivity anywhere any time. Many smart devices with embedded radios are battery operated, and in many situations battery replacement may be awkward if not impossible. Regardless of the power source, with deployment of billions of devices anticipated, global power consumption of IoT may become considerable. Therefore in the continued development of IoT for deployment in smart cities, it is inevitable that energy efficiency becomes an important part of the research agenda towards "green IoT for smart cities". In this presentation, we shall give an overview of IoT for smart cities and examine how IoT contributes to a greener society. We shall also highlight some of our recent research results on utilizing the powerful computation capacity of cloud computing to enable green IoT for smart cities. Open problems and future research directions will be discussed.
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 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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.140 | 0.062 |
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".