An Overview of Fog Data Analytics for IoT Applications

Area of Science:

• Computer Science
• Data Science
• Network Engineering

Background:

• Cloud computing faces challenges with the increasing volume and sensitivity of data generated by the Internet of Things (IoT).
• Fog computing emerges as a viable architecture to manage and analyze large-scale IoT data, particularly for time-sensitive and security-critical applications.
• Existing big data analytics advancements often overlook the specific requirements of fog data analytics.

Purpose of the Study:

• To explore research challenges in fog data analytics for IoT networks.
• To propose solutions for next-generation fog data analytics.
• To compare the performance of fog computing against cloud computing for IoT data processing.

Main Methods:

• Literature review of research challenges in fog data analytics.
• Exploration of solutions for heterogeneity, security, accessibility, and resource sharing in fog environments.
• Experimental analysis comparing fog computing and cloud computing architectures for IoT data.

Main Results:

• Fog computing demonstrates superior performance over traditional cloud computing in network utilization.
• Fog computing significantly reduces latency compared to cloud-based solutions for IoT data processing.
• Identified key challenges include heterogeneity, security, accessibility, and network communication overhead.

Conclusions:

• Fog computing is an effective architecture for addressing the limitations of cloud computing in IoT big data analytics.
• Further research is needed to advance fog data analytics for more proficient knowledge extraction and smart decision-making.
• Future trends in fog data analytics and IoT networks warrant continued investigation.