IoTCrawler: Challenges and Solutions for Searching the Internet of Things

Area of Science:

• Computer Science
• Data Science
• Internet of Things

Background:

• The proliferation of Internet of Things (IoT) devices generates vast amounts of data.
• Existing IoT data sources are often fragmented and lack interoperability, hindering effective data utilization.
• Mechanisms for searching and integrating these diverse data sources are crucial for leveraging IoT data.

Purpose of the Study:

• To introduce the IoTCrawler framework, a novel system designed for searching and integrating IoT data sources.
• To address the challenges of data fragmentation and interoperability in the IoT landscape.
• To provide a domain-independent, layered solution for crawling, indexing, and searching IoT data.

Main Methods:

• Development of a system of systems architecture connecting existing IoT solutions.
• Implementation of a layered approach for crawling, indexing, and searching IoT data.
• Incorporation of features for privacy, security, adaptivity, and reliability.

Main Results:

• The IoTCrawler framework demonstrates effective crawling, indexing, and searching of IoT data sources.
• The framework successfully addresses key requirements for IoT data searching and integration.
• Extensive evaluation and real-world use cases validate the framework's applicability and performance.

Conclusions:

• IoTCrawler provides a robust and adaptable solution for overcoming IoT data fragmentation.
• The framework enhances interoperability and facilitates the leveraging of diverse IoT data for improved services.
• IoTCrawler is a valuable tool for researchers and practitioners working with large-scale IoT data ecosystems.