Review of Data Visualization Techniques in IoT Data

Data visualization is considered the most efficient solution in IoT. It visualizes the endless stream of information collected by the respective smart systems to exponentially increase its value due to the meaningful insights it provides. Notably, numerous tools and techniques visualize the meaningful information obtained. It is, however, prudent that the analysis of this information considers the possibilities of anomalies within the data. The proper mechanisms must be implemented to detect these anomalies to ensure that the visualized information is accurate and reliable. This research discussion delves into reviewing current literature on the different tools and techniques that could detect anomalies in IoT. The review would reveal that the machine learning and deep learning approaches are the most-used approaches for anomaly detection. Several images would emerge regarding the visualization tools, including Grafana, Tableau, Orange, Power Bi tool, Kibabana, and Plotly. Tableau was identified as the popular and leading software used by numerous entities for visualizing the data in meaningful insights leading to powerful decisions. Some of its salient features include offering Api's and sophisticated developer tools such that different companies could use to meet specific needs, it has a less learning curve and has the ability to handle with extensive amounts of data generated in the network. Notably, the fusion of Big Data and IoT will continue to develop and advance both the existing and new visualization tools. These developments will arise because the IoT networks will continue to obtain data from various sources as the number of smart devices increases tremendously.