Multi-Class Imbalanced Data Handling with Concept Drift in Fog Computing: A Taxonomy, Review, and Future Directions

A network of actual physical objects or “IoT components” linked to the internet and equipped with sensors, electronics, software, and network connectivity is known as the Internet of Things (IoT). This ability of the IoT components to gather and share data is made possible by this network connectivity. Many IoT devices are currently operating, which generate a lot of data. When these IoT devices started collecting data, the cloud was the only place to analyze, filter, pre-process, and aggregate it. However, when it comes to IoT, the cloud has restrictions regarding latency and a more centralized method of distributing programs. A new form of computing called Fog computing has been proposed to address the shortcomings of current cloud computing. In an IoT context, sensors regularly communicate signal information, and edge devices process the data obtained from these sensors using Fog computing. The sensors’ internal or external problems, security breaches, or the integration of heterogeneous equipment contribute to the imbalanced data, i.e., comparatively speaking, one class has more instances than the other. As a result of this data, the pattern extraction is imbalanced . Recent attempts have concentrated heavily on binary-class imbalanced concerns with exactly two classes. However, the classification of multi-class imbalanced data is an issue that needs to be fixed in Fog computing, even if it is widespread in other fields, including text categorization, human activity detection, and medical diagnosis. The study intends to deal with this problem. It presents a systematic, thorough, and in-depth comparative analysis of several binary-class and multi-class imbalanced data handling strategies for batch and streaming data in IoT networks and Fog computing. There are five major objectives in this study. First, reviewing the Fog computing concept. Second, outlining the optimization metric used in Fog computing. Third, focusing on binary and multi-class batch data handling for IoT networks and Fog computing. Fourth, reviewing and comparing the current imbalanced data handling methodologies for multi-class data streams. Fifth, explaining how to cope with the concept drift, including novel and recurring classes, targeted optimization measures, and evaluation tools. Finally, the best performance metrics and tools for concept drift, binary-class (batch and stream) data, and multi-class (batch and stream) data are highlighted.