Stuck-at Fault Analytics of IoT Devices Using Knowledge-based Data Processing Strategy in Smart Grid

Abstract

Smart grid addresses traditional electricity generation issues by integrating ambient intelligence in actions of connected devices and production processing units. The grid infrastructure uses sensory IoT devices such as smart meter that records electric energy consumption and production information into the end units and stores sensor data through semantic technology in the central grid repository. The grid uses sensor data for various analytics such as production analysis of distribution units and health checkup of involved IoT devices and also observes functional profile of IoT equipment that includes service time, remaining lifespan, power consumption along with its functional error percentile. In a typical grid infrastructure, AMI meters process continuous streaming of data with Nand flash memory that stores dataset in the form of charges such as 0 and 1 in memory cell. Although, a flash memory is tested through rigorous testing profile but the grid environment impacts its cell endurance capacity diversely. Thus, a cell gets stuck-at fault before the end of endurance and can not be used to override a new tuple into it. In this paper, we perform a knowledge-based analytics to observe these stuck-at faults by detecting the abnormal variation among stored data tuples and predicts the going-to-be stuck-at cells of AMI meter. The simulation results show that the proposed approach rigorously maintain a knowledge-based track of AMI devices' data production with an average error percentile of 0.06% in scanning blocks and performed prediction analytics according to the scanning percentile functional health and presents a work-flow to balance the load among healthy and unhealthy IoT devices in smart grid.