AI, IoT, and Big Data Convergence Accelerates Environmentally Sustainable Smart City Development

Why It Matters

Understanding how these technologies converge is crucial for designers and engineers developing urban solutions. It highlights a shift towards data-driven, interconnected systems that can optimize resource use and environmental performance in urban environments.

Key Finding

Research shows that smart cities focused on environmental sustainability are a fast-growing area, largely due to the combined power of AI, IoT, and Big Data, which has been further spurred by recent global events and digital trends.

Key Findings

• Environmentally sustainable smart cities represent a rapidly growing research trend, with significant acceleration observed in recent years.
• The convergence of AI, IoT, and Big Data technologies is a key enabler for achieving environmental targets within smart cities.
• Digitalization and decarbonization agendas, influenced by factors like COVID-19 and technological advancements, have boosted the development of these sustainable urban models.
• Research priorities within this field have evolved, with specific AI models and environmental sustainability areas receiving varying levels of attention over time.

Research Evidence

Aim: To explore the key research trends, driving factors, and thematic evolution of environmentally sustainable smart cities, focusing on the convergence of AI, IoT, and Big Data technologies.

Method: Bibliometric analysis and evidence synthesis.

Procedure: A comprehensive literature review was conducted, analyzing 2,574 documents from the Web of Science database across three distinct time periods (1991-2015, 2016-2019, and 2020-2021) to identify trends and thematic shifts in research on environmentally sustainable smart cities and their technological underpinnings.

Sample Size: 2574 documents

Context: Urban planning and smart city development, with a focus on environmental sustainability.

Design Principle

Leverage the synergistic capabilities of AI, IoT, and Big Data to drive environmental sustainability in urban systems.

How to Apply

When designing smart city solutions, consider how AI can analyze data from IoT sensors to optimize energy consumption, waste management, and transportation, thereby contributing to environmental sustainability goals.

Limitations

The study relies on existing literature, and the rapid pace of technological advancement may mean some findings are quickly superseded. The focus is on research trends, not necessarily on the practical implementation challenges or successes of specific cities.

Student Guide (IB Design Technology)

Simple Explanation: Smart cities are getting better at being green because they're using AI, the Internet of Things (like sensors), and Big Data together to manage things like energy and waste more efficiently.

Why This Matters: This research shows that using advanced technologies like AI and IoT is key to making cities more environmentally friendly, which is a major goal for many design projects.

Critical Thinking: To what extent can technology alone solve environmental degradation in cities, or are social and policy changes equally, if not more, important?

IA-Ready Paragraph: This research highlights the critical role of converging AI, IoT, and Big Data technologies in advancing environmentally sustainable smart cities. The study's findings indicate a significant trend towards integrating these digital solutions to meet environmental targets, suggesting that future urban design and development should prioritize such technological integration to enhance efficiency and ecological responsibility.

Project Tips

• When researching smart city solutions, look for how different technologies are combined.
• Consider the environmental impact of your design choices and how data can help measure and improve it.

How to Use in IA

• Reference this study when discussing the role of technology in achieving environmental sustainability in your design project.
• Use the findings to justify the integration of specific technologies in your proposed solution.

Examiner Tips

• Demonstrate an understanding of how technological convergence supports sustainability goals.
• Critically evaluate the limitations of relying solely on technology for environmental solutions.

Independent Variable: Convergence of AI, IoT, and Big Data technologies.

Dependent Variable: Progress towards environmentally sustainable smart cities (measured by research trends, thematic evolution, and reported environmental targets).

Controlled Variables: Time periods of analysis (1991-2015, 2016-2019, 2020-2021), data sources (Web of Science).

Strengths

• Comprehensive literature review covering a significant number of documents.
• Analysis across multiple time periods to identify trends and evolution.
• Focus on the synergistic potential of key emerging technologies.

Critical Questions

• How do the specific applications of AI, IoT, and Big Data vary across different urban contexts and sustainability challenges?
• What are the potential ethical and privacy concerns associated with the extensive data collection and AI analysis in smart cities?

Extended Essay Application

• An Extended Essay could investigate a specific application of AI, IoT, or Big Data within a smart city context, analyzing its environmental impact and comparing it to non-technological solutions.
• Another angle could be to explore the diffusion of these technologies in smart cities and the barriers to their adoption for sustainability purposes.

Source

Environmentally sustainable smart cities and their converging AI, IoT, and big data technologies and solutions: an integrated approach to an extensive literature review · Energy Informatics · 2023 · 10.1186/s42162-023-00259-2