Characterization and Efficient Management of Big Data in IoT-Driven Smart City Development

Area of Science:

• Computer Science
• Urban Planning
• Information Technology

Background:

• Smart cities integrate Information and Communication Technologies (ICT) to improve citizens' quality of life through context-aware services.
• Key challenges include optimizing resource use, ensuring uninterrupted service delivery, minimizing costs, and reducing consumption.
• Existing methods struggle with the volume and complexity of big data generated by the Internet of Things (IoT).

Purpose of the Study:

• To propose a data-centric approach for conceptualizing IoT 'things' from a service-oriented perspective.
• To investigate efficient methods for identifying, integrating, and managing big data in smart city contexts.
• To address the limitations of current techniques in handling IoT-generated big data complexities.

Main Methods:

• Conceptualizing IoT devices as service-oriented entities.
• Developing a data-centric framework for big data management.
• Investigating efficient data identification, integration, and management strategies.
• Exploring scalable query processing and reasoning techniques.

Main Results:

• The proposed data-centric approach offers improved management of complex IoT data.
• Enhanced support for efficient and scalable query processing and reasoning.
• Potential to better address resource optimization and service delivery challenges in smart cities.

Conclusions:

• A data-centric approach is crucial for effectively managing big data in smart city initiatives.
• This framework provides a foundation for developing more efficient and scalable smart city applications.
• Further research can build upon this approach to enhance urban technological integration and citizen services.