SM@RMFFOG: sensor mining at resource management framework of fog computing

Due to the increasing use of sensors/devices in smart cities, IoT/cloud data centers must provide adequate computing resources. Efficient resource management is of the biggest challenges in distributed computing. This research proposes a solution to use the activity log of sensors to extract their activity patterns. These patterns contribute to the resource management to predict future resource requirements and act accordingly. In this framework, called sensor mining at resource management framework, the pattern extraction can be performed by the sensor mining algorithm at cloud-only data centers (SM@RMFCLOUD) or cloud/fog servers (SM@RMFFOG). Experiments apply both CityPulse and ideal datasets to evaluate the presented frameworks. The sensor mining in both cloud/fog and cloud-only frameworks improves throughput, response time, and execution delay without increasing costs, energy, and bandwidth consumption compared to the frameworks with no sensor mining.