Compositional Kalman Filters for Navigational Data Streams In IoT Systems

Abstract

The Internet of Things (IoT) technology is undergoing expansion into different aspects of our life, changing the way businesses operate and bringing in efficiency and reliability of digital controls on various levels. Processing large amount of data from connected sensor networks becomes a challenging task. Specific part of it related to fleet management requires processing of the data on boards of vehicles equipped with multiple electronic devices and sensors for maintenance and operation of such vehicles. Herewith the efficiency of various configurations of employing Kalman filter algorithm for on-the-fly pre-processing of the sensory network originated data streams in IoT systems is investigated. Contextual grouping of the data streams for pre-processing by specialized Kalman filter units is found to be able to satisfy the logistics of IoT system operations. It is demonstrated that interconnection of the elementary Kalman filters into an organized network, the compositional Kalman filter, allows to take advantage of the redundancy of data streams to accomplish IoT pre-processing of the raw data. This includes intermittent data imputation, missing data replacement, lost data recovery, as well as error events detection and correction. Architectures are proposed and tested for the interaction of elementary Kalman filters in detection of GPS outage events and their compensation via data replacement procedure, as well as GPS offset occurrence detection and its compensation via data correction routine. Demonstrated is the efficiency of the suggested compositional designs of elementary Kalman filter networks for the purpose of data pre-processing in IoT systems.