LFDDRA-IoT: Lightweight Faulty Data Detection and Recovery Approach for Internet of Things

Abstract

IoT data is prone to different kinds of failures (hardware, software, and communication failures). Fault detection and recovery are challenging problems due to sensing devices’ limitations and the deployment field’s nature. Furthermore, timely and accurate detection of faulty data and recovery is highly significant to IoT applications to ensure operational stability and execution efficiency. This paper presents a faulty data detection and recovery approach based on dynamic interval-valued evidence and Kalman filter to accomplish this objective. The proposed approach is edge-based and requires no training to perform faulty data detection and recovery. The simulation results reveal that the proposed approach is efficient and effective in fault detection and recovery.