Improved data-driven root cause analysis in fog computing environment

Internet of Things (IoT) and cloud computing are used in many real-time smart applications such as smart health-care, smart traffic, smart city, and smart industries. Fog computing has been introduced as an intermediate layer to reduce communication delay between cloud and IoT Devices. To improve performance of these smart applications, a predictive maintenance system needs to adopt anomaly detection and root cause analysis model that helps to resolve anomalies and avoid such anomalies in future. The state-of-art work on data-driven root cause analysis suffers from scalability, accuracy, and interpretability. In this paper, a multi-agent-based improved data-driven root cause analysis technique is introduced to identify anomalies and its root cause. Multiple agents are used to perform various operations like data collection, anomaly detection, and root cause analysis. The deep learning model LSTM autoencoder is used to find the anomalies, and a game theory approach called SHAP algorithm is used to find the root cause of the anomaly. The experiment is carried out in Google Colab with Keras Python library to evaluate the model. The evaluation result shows the improvement in accuracy and interpretability, as compared to state-of-the-art works.