Immersive Visual Analytics of Wi-Fi Signal Propagation and Network Health

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e are immersed in waves of information. This information is typically transmitted as radio waves in many protocols and frequencies, such as WiFi, Bluetooth, and Near-Field Communications (NFC). It carries vital information such as health data, private messages, and financial records. There is a critical need for systematic and comprehensive visualization techniques to facilitate seamless, resilient, and secure transmission of these signals. Traditional visualization techniques are not enough because of the scale of these datasets. In this dissertation, we present three novel contributions that leverage advances in volume rendering and virtual reality (VR): (a) an outdoor volume-rendering visualization system that facilitates large-scale visualization of radio waves over a college campus through real-time programmable customization for analysis purposes, (b) an indoor, building-scale visualization system that enables data to be collected and analyzed without occluding the user's view of the environment, and (c) a systematic user study with 32 participants which shows that users perform analysis tasks well with our novel visualizations.

In our outdoor system, we present the Programmable Transfer Function. Programmable Transfer Functions offer the user a way to replace the traditional transfer function paradigm with a more flexible and less memory-demanding alternative. Our work on indoor WiFi visualization is called WaveRider. WaveRider is our contribution to indoor-modeled WiFi visualization using a virtual environment. WaveRider was designed with the help of expert signal engineers we interviewed to determine the needs of the visualization and who we used to evaluate the application. These works provide a solid starting point for signal visualization as our networks transition to more complex models.

Indoor and outdoor visualizations are not the only dichotomy in the realm of signal visualization. We are also interested in visualizations of modeled data compared to visualization of data samples. We have also explored designs for multiple sample-based visualizations and conducted a formal evaluation where we compare these to our previous model-based approach. This analysis has shown that visualizing the data without modeling improves user confidence in their analyses. In the future, we hope to explore how these sample-based methods allow more routers to be visualized at once.