A Model for Predictive and Prescriptive Analysis for Internet of Things Edge Devices with Artificial Intelligence

Abstract

In recent years, abundant amounts of data have been accumulated from a huge network of Internet of things (IoT) devices spread around the globe. The collected data is only useful if it creates an action. To forge data actionable, it needs to be broadened with context and creativity. Traditional methods of evaluating structured data and creating action do not contribute to efficiently process the massive amounts of real-time data that stream from IoT devices. The study has shown that most of the IoT gadgets offering cloud storage along with analytics either trade the data or are lost dumped with no use. For instance, consider the trillions of log files that contain metadata, timestamps of a smart bulb which seems useless if used by nobody. But, it is always important to correlate the data with similar data patterns in a different application that helps in forecasting an insight into possible outcomes. Hence, there is a huge scope for improvement in this realm which motivated us to perform experiments and prove the concept with rigid conclusions. This is where AI-based analysis and response become crucial for extracting optimal value from that data. Also the research involved contains sensible prescriptive analysis offering hindsight when one talks about the edge or node devices in the IoT scenario but certainly, it lacks the rigid structure for offering insight and foresight. An in-depth insight at the edge level can be conceived by the existing artificial intelligence building models offered by many IT giants such as AWS Greengrass. Thus, there is an immense need to process the edge device data with enough intelligence and use existing analytics tools to greatly enhance the performance of the cloud and improve overall IoT application in hand by making the cloud requirements less CPU intensive and more economic. In this paper, a model for predictive and prescriptive analysis to improve production capabilities, gain efficiencies, and reduce operating costs by delving into edge computing to produce actionable insight and foresight is demonstrated with the help of a practical experiment.KeywordsEdge computingInternet of thingsAWS GreengrassCloud computingPrescriptive analysisInternet of things (IoT)Artificial intelligenceRaspberry PiMPU6050