Processing Complex Events in Fog-Based Internet of Things Systems for Smart Agriculture

Area of Science:

• Computer Science
• Distributed Systems
• Internet of Things (IoT)

Background:

• The proliferation of Internet of Things (IoT) services necessitates enhanced data processing and storage capabilities.
• Edge computing and its embodiment, Fog computing, offer distributed processing closer to data sources, reducing reliance on centralized Cloud resources.
• Fog computing provides an intermediate processing tier, decreasing end-to-end latency and core network traffic, crucial for time-sensitive IoT applications.

Purpose of the Study:

• To propose and evaluate a multi-tier Complex Event Processing (CEP) approach integrating sensor nodes, Fog, and Cloud.
• To leverage Fog computing, CEP, and data fusion for efficient knowledge generation and decision support in IoT systems.
• To demonstrate significant reductions in message transmission time and data traffic within IoT networks.

Main Methods:

• Implementation of a multi-tier architecture encompassing sensor nodes, Fog nodes, and Cloud infrastructure.
• Integration of Complex Event Processing (CEP) and data fusion techniques across the tiers.
• Experimental evaluation of message delivery time and data traffic reduction.

Main Results:

• Achieved 98% accuracy in information processing for fast decision-making.
• Demonstrated a 77% reduction in the average message sending time across the network.
• Successfully reduced overall data traffic by 82%.

Conclusions:

• The proposed multi-tier CEP approach effectively enhances decision-making speed in IoT systems.
• Fog computing, combined with CEP and data fusion, significantly optimizes network performance by reducing latency and traffic.
• This architecture provides a scalable and efficient solution for managing the demands of modern IoT applications.