Heterogeneous Wireless and Visible Light Communication for the Internet of Things

Connecting sensor, control, and edge devices to the Internet in a reliable and robust way is critical to the success of many big data and IoT applications. Wireless technologies enable such connectivity but have come under increasing challenge due to the proliferation of devices and increase in data requirements. Devices with wireless connectivity compete with each other in the limited spectrum resources, causing spectrum crunch and interference, which signi cantly hampers the IoT vision. In this dissertation, we study how serious the problem of interference is in wireless networks for IoT, and then propose two solutions to solve this problem. Our goal is to connect IoT devices to the Internet with reliability, robustness, and adaptiveness using edge computing algorithms and methodologies in a practical manner.

One solution is to leverage the wireless interference across various IoT devices. We transformed the interference into a communication channel between these devices and evaluated its feasibility in practical environments. The communication channel was established based on the spectrum sharing by various wireless devices that are using different wireless technologies, such as WiFi, Zigbee, or Bluetooth. In this work, we have achieved one-way communication from WiFi devices to Zigbee devices. We have demonstrated the feasibility to send control signals utilizing the interference. This validates that interference utilization can be a practical solution to solve the spectrum-crunch problem.

The other solution is to avoid interference by exploring new spectrum resources that can provide wireless connectivity. We adopt visible light as the communication medium since it is ubiquitous and free from wireless interference. Existing embedded LED-to-LED communication is considered a promising technique for IoT connectivity. However, low-cost embedded visible light communication (VLC) has been largely restricted by its reliability, robustness, and speed. In this work, we propose adaptive ambient light cancellation to improve the robustness of embedded VLC, we also design, implement, and open-source a novel embedded VLC platform with a 6-7x performance gain compared to state-of-the-art.