Automating 3D Wireless Measurements with Drones

Wireless signals and networks are ubiquitous in today’s world. Though more reliable than ever, wireless networks still struggle with weak coverage, blind spots, and interference. Having a strong understanding of wireless signal propagation is essential for increasing coverage, optimizing performance, and minimizing interference for wireless networks. Extensive studies have been done on the propagation of wireless signals, and many theoretical models have been made to simulate wireless signal propagation. Unfortunately, models of signal propagation are often not accurate in reality, and real- world signal measurements are required for validation. Existing methods for collecting wireless measurements involve human researchers walking to each location of interest and manually collecting measurements, which requires large amounts of time and effort, or placing sensors at each location of interest, which is costly. We propose Drone- Sense: a system for measuring wireless signals using autonomous drones. DroneSense reduces the time and effort required for measurement collection, and is affordable and accessible to all users. This is significant in the field of wireless networking as it provides researchers with an efficient method to quickly analyze wireless coverage and test their wireless propagation models

Customizing Indoor Wireless Coverage via 3D-Fabricated Reflectors

Judicious control of indoor wireless coverage is crucial in built environments. It enhances signal reception, reduces harmful interference, and raises the barrier for malicious attackers. Existing methods are either costly, vulnerable to attacks, or hard to configure. We present a low-cost, secure, and easy-to-configure approach that uses an easily-accessible, 3D-fabricated reflector to customize wireless coverage. With input on coarse-grained environment setting and preferred coverage (e.g., areas with signals to be strengthened or weakened), the system computes an optimized reflector shape tailored to the given environment. The user simply 3D prints the reflector and places it around a Wi-Fi access point to realize the target coverage. We conduct experiments to examine the efficacy and limits of optimized reflectors in different indoor settings. Results show that optimized reflectors coexist with a variety of Wi-Fi APs and correctly weaken or enhance signals in target areas by up to 10 or 6 dB, resulting to throughput changes by up to -63.3% or 55.1%