K-Means Based on Resource Clustering for Smart Farming Problem in Fog Computing

Fog computing extends the traditional centralized cloud-computing model to the edge of the network to provide cloud services such as computation and networking distributed closer to the end device. For Smart Farming applications, Fog computing can enable real-time analysis of crop and environmental behavior to improve the production of the agricultural sector. However, being at the edge results in limited physical underlying infrastructure thus lessens fog nodes (Fog node) resources compare to the cloud resulting in resource overconsumption problem and network performance downgrade if not managed. Therefore, to address resource enhancement problem. In this paper, we propose a K-Means based clustering algorithm for a Smart Farming application, compared network performance with first come first serve (FCFS) algorithms. Simulation results demonstrate that K-Means outperforms FCFS by 1.80 % on energy consumption, 1.2 % lesser on network usage, and 27 % lesser on end-to-end delay.