Editorial: Special Issue "Edge and Fog Computing for Internet of Things Systems"

Area of Science:

• Computer Science
• Network Engineering

Background:

• Massive data generation from Internet of Things (IoT) sensing devices presents significant challenges.
• Real-time data processing and low latency are critical requirements for many IoT applications.
• Traditional cloud computing models face limitations in handling the scale and immediacy demanded by IoT.

Discussion:

• Edge and fog computing architectures offer a decentralized approach to process IoT data closer to its source.
• These distributed computing paradigms alleviate the processing burden on cloud platforms by performing computation at the network edge.
• Implementing edge and fog computing is vital for addressing the unique challenges posed by large-scale IoT deployments.

Key Insights:

• Edge and fog computing enable reduced latency for time-sensitive IoT applications.
• These architectures enhance IoT system scalability by distributing data processing tasks.
• Decentralized processing improves the responsiveness of IoT systems to security threats.

Outlook:

• Further research into optimizing resource allocation and task scheduling in heterogeneous edge-fog environments is recommended.
• The integration of artificial intelligence (AI) and machine learning (ML) at the edge will unlock new capabilities for intelligent IoT systems.
• Standardization efforts for edge and fog computing protocols will accelerate the adoption and interoperability of IoT solutions.