Abstract:
The implementation of IoT devices in agriculture has transformed smart farming, enabling precise real-time monitoring
and management of agricultural activities. However, traditional smart farming applications that rely on centralized cloud servers face
significant challenges, including increased latency and network congestion, which hinder the timely processing of critical data. To
address these issues, this research proposes a fog computing-based solution tailored for smart farming, focusing on optimized latency
and energy management. We introduce a clustering algorithm to enhance the communication and collaboration between fog nodes
and their corresponding Fog Controller Nodes (FCNs), ensuring efficient data processing within the fog layer. Additionally, an
energy-aware algorithm is presented to improve the FCN's awareness of each fog node’s energy profile, allowing for adaptive power
management strategies that optimize energy consumption. An optimal module placement algorithm is also proposed, prioritizing
tasks based on their latency sensitivity and urgency, which ensures efficient resource utilization and timely responses to critical
agricultural needs. The proposed approaches have been implemented and tested using the iFogSim simulator, demonstrating
significant improvements in latency, network usage, and energy consumption compared to FCMSF and Agrifog models. This
comprehensive evaluation underscores the potential of fog computing in revolutionizing smart farming by addressing key challenges
and enhancing overall system efficiency.
