Advancing Data-Driven Decision-Making in Smart Cities through Big Data Analytics: A Comprehensive Review of Existing Literature
Bibliographic record
Abstract
Governments and cities are increasingly launching smart city (SC) schemes to address the challenges posed by rapid urbanization and population growth in municipalities. Smart cities utilize data from various sources within a metropolis to enhance urban development, promote qualitative lifestyles, and focus on economic and environmental sustainability. Big data analytics (BDA) plays a crucial role in collecting and analyzing vast amounts of data from SC infrastructures, enabling effective management and implementation of smart city initiatives. BDA helps explore data collected through Internet of Things (IoT) devices and sensors, identifying trends, and making appropriate changes, ultimately making smart cities more efficient, sustainable, and beneficial for their inhabitants. However, big data in SC also presents potential risks and challenges related to urban security and the well-being of residents. The literature review examines various research approaches, techniques, algorithms, and architectures proposed to address the challenges of handling big data in smart cities. Urbanization's growing trend is causing challenges in managing basic amenities and resources in urban areas, necessitating innovative solutions to ensure efficient functioning and improved quality of life for citizens. Previous research has highlighted the significance of big data analytics in driving smart city decision-making, yet many smart city big data initiatives have faced difficulties in implementation. To overcome these challenges, researchers have explored techniques like artificial intelligence, machine learning, data mining, and deep learning, as well as architectures encompassing layers of instrumentation, middleware, and application for end-users. Additionally, researchers have emphasized the importance of selecting appropriate sensors for efficient data collection and explored low-cost smart traffic systems to improve urban traffic management. Overall, this review synthesizes insights from nine scholarly papers, shedding light on approaches to handling big data challenges in smart cities.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".