FoT-Stream: A Fog platform for data stream analytics in IoT

The Internet of Things (IoT) has developed infrastructures and applications that generate large amounts of data. These data are usually yielded as streams, characterized for being continuous and infinite, and presenting the peculiarity of modifying their behavior over time. Due to the large capacity of storage, data processing, and provisioning of resources, such data are generally processed/analyzed in Cloud Computing environments. Although those environments provide IoT infrastructure with adequate scalability and resource-centric features, the distance between devices and the cloud can impose limitations to achieve low latency in data traffic. In order to maintain scalability, achieve low latency, and reduce data traffic between the IoT devices and the Cloud, the Fog Computing was proposed, providing resource availability at the edge of the network. Although Fog Computing establishes resource availability at the edge of the network, the technologies/techniques currently used for IoT data processing and analysis may not be sufficient to support the continuous and unlimited streams that IoT platforms and applications produce. In addition, data stream applications at Fog must be supported by the computationally limited devices. Therefore, aiming at taking advantage of Fog Computing and resolve the gap of the data stream in IoT environments, this work presents a new platform called FoT-Stream designed to process and analyze, in real-time, data stream from the Internet of Things in Fog. The main advantage of using our approach is the possibility of reducing the amount of data transmitted on the network infrastructure, which allows, as a consequence, to perform an online data modeling, by detecting changes in data behavior, and a reduction of the Internet usage. Our results in a real-world scenario emphasize FoT-Stream considerably reduces the latency and amount of data traffic in IoT environments without affecting the system throughput.