Efficient Execution of Complex Context Queries to Enable Near Real-Time Smart IoT Applications

Area of Science:

• Computer Science
• Ubiquitous Computing
• Distributed Systems

Background:

• The rapid evolution of the Internet of Things (IoT) necessitates advanced contextual intelligence for enhanced device interaction, efficiency, and sustainability.
• Context Management Platforms (CMPs) are emerging as key solutions, but generic, scalable platforms for context sharing remain underdeveloped.
• Existing solutions struggle with efficient context management and real-time query processing.

Purpose of the Study:

• To propose and validate a novel Context Query Engine (CQE) as a core component of a pioneering Context-as-a-Service (CoaaS) platform.
• To demonstrate the CQE's capability for efficient, near real-time execution of context queries within the IoT ecosystem.
• To provide a robust and scalable solution for context publishing, consumption, monitoring, and sharing.

Main Methods:

• Development of a novel Context Query Engine (CQE) architecture.
• Integration of CQE into the Context-as-a-Service (CoaaS) platform.
• Extensive experimental evaluation of CQE's performance and scalability, focusing on complex context query execution.

Main Results:

• The proposed CoaaS platform, powered by CQE, demonstrates superior performance in executing complex context queries compared to existing solutions.
• Experimental results validate the efficiency and scalability of the CQE mechanism.
• The advanced query language integrated into CoaaS enhances its suitability for real-world IoT deployments.

Conclusions:

• The Context Query Engine (CQE) offers an efficient and scalable solution for context management in the Internet of Things.
• The Context-as-a-Service (CoaaS) platform, featuring CQE, represents a significant advancement in delivering contextual intelligence for IoT.
• CoaaS is a viable candidate for practical, large-scale IoT deployments requiring sophisticated context-aware services.