Data management for the internet of things: design primitives and solution.

Area of Science:

• Computer Science
• Data Science
• Networking

Background:

• The Internet of Things (IoT) involves interconnected smart objects generating vast data streams.
• Current hardware and communication technologies for IoT are advancing, but data management solutions lag behind.
• Traditional databases struggle with the scale and complexity of global IoT data.

Purpose of the Study:

• To survey existing data management solutions for the Internet of Things (IoT).
• To identify key design primitives essential for effective IoT data management.
• To propose a novel data management framework for IoT environments.

Main Methods:

• Literature review and survey of current IoT data management approaches.
• Analysis of distinctive design primitives required for IoT data management.
• Development of a federated, data- and sources-centric framework proposal.

Main Results:

• Existing IoT data management solutions address only partial aspects, often focusing on sensor networks.
• Key design primitives for IoT data management include scalability, heterogeneity, and real-time processing.
• A federated framework is proposed to integrate diverse IoT data and sources.

Conclusions:

• There is a critical need for mature data management solutions for the Internet of Things.
• The proposed federated framework offers a promising approach to address IoT data complexity.
• This framework serves as a foundation for comprehensive IoT data management systems.