An Energy Aware Intelligent Fault Detection Scheme for IoT-Enabled WSNs

Recent years have seen an explosion in the demand for the Internet of Things (IoT). IoT makes any physical object smart by providing sensing capability. The physical objects are embedded with sensors that form a network of sensors. However, sensors are vulnerable to faults due to energy depletion, software failures, and hardware failures. The existing fault detection schemes put huge computational overhead on the battery-limited and low computational capacity sensor nodes that are subject to the premature death of the sensor nodes. Also, they suffer from poor fault detection accuracy and huge false alarm rate that significantly reduce the overall performance of the networks. In this paper, an energy-aware intelligent fault detection scheme is proposed for IoT-enabled wireless sensor networks that significantly improve fault detection accuracy and reduce false alarm rate. A novel 3-Tier hard fault detection mechanism is used for detecting hardware unit faults of the sensor nodes. Furthermore, an optimized deep learning mechanism is used for various soft fault detection that prevents premature death of sensor nodes. The paper mathematically analyses the proposed scheme in terms of energy consumption, time complexity, and message complexity. Extensive simulations show the enhanced performance of the proposed scheme compared with the state-of-the-art algorithms in terms of fault detection accuracy, false alarm rate, false-positive rate, energy consumption, and network lifetime.