Wireless communication remains the most energy-intensive component of embedded systems, often limiting their deployment in power-constrained or hard-to-reach environments. While backscatter communication offers energy efficiency by offloading carrier generation, it continues to face practical limitations, hindering widespread adoption. In this paper, we propose going beyond backscatter architecture that re-imagines both the transmitter and receiver using ultra-low-power components, such as tunnel diodes. Our approach eliminates the dependence on external emitters for transmission and moves beyond envelope detectors on the receiver side, resulting in significantly improved sensitivity and communication range.

Wireless connectivity challenges hinder large-scale deployment of embedded systems. We introduce AudioCast to address two critical issues: spectrum scarcity-induced contention and high power consumption of radio transmitters. The decline of FM-broadcast stations and the ubiquity of FM-receivers motivate the design of Audio-Cast. By leveraging the negative differential resistance of tunnel diodes—which occurs at low power—AudioCast rethinks the conventional transmitter design. Combined with their self-modulation capability, tunnel diode oscillators enable frequency modulated transmissions while consuming under 200 μW. The transmitter achieves upto 130 m range in line-of-sight and tens of meters in non-line-of-sight environments.

Tracking movements in the environment of macroscopic objects enables numerous applications, from monitoring vital signs through body movements to inferring hand gestures. However, current systems overwhelmingly rely on contact-based sensors or energy-consuming radio frequency mechanisms that necessitate complex radio transceivers for receptions. We present ongoing research on a novel low-power sensor that leverages the unique characteristics of tunnel diodes. This sensor can detect minute changes in its vicinity and communicate these changes over radio waves, all while consuming under 150 microwatts of power consumption. Notably, the transmitted radio waves are processed using low-cost, off-the-shelf radio transceivers, resulting in low cost and power consumption. The sensor’s functionality stems from the sensitivity of the resonant frequency of the tunnel diode oscillators to changes in their electromagnetic surroundings. Our early work exhibits its potential for detecting a person’s breathing patterns, and hand gestures.

Significant efforts to address the energy challenges of embedded systems have resulted in the design of new low-power transmitter architectures. These transmitters utilize backscatter or tunnel diode-based mechanisms to enable low-power transmissions by delegating energy-intensive tasks to external infrastructure. However, their widespread adoption is hindered by the need for specialized deployment setups, such as the precise placement of carrier-emitting devices. To overcome this, we are developing GateHaul-a low-cost gateway architecture that provides the required carrier signal, coordinates with other gateways, collects sensor data, and backhauls the information. Notably, GateHaul can backhaul sensor data without conventional networks utilizing emerging opportunistic backhaul networks. We demonstrate an early prototype of GateHaul that collects information from backscatter and conventional devices and backhauls the collected information using Apple's FindMy network.

Current air pollution monitoring systems are bulky and expensive resulting in a very sparse deployment. In addition, the data from these monitoring stations may not be easily accessible. This paper focuses on studying the dense deployment based air pollution monitoring using IoT enabled low-cost sensor nodes. For this, total nine low-cost IoT nodes monitoring particulate matter (PM), which is one of the most dominant pollutants, are deployed in a small educational campus in Indian city of Hyderabad. Out of these, eight IoT nodes were developed at IIIT-H while one was bought off the shelf. A web based dashboard website is developed to easily monitor the real-time PM values. The data is collected from these nodes for more than five months. Different analyses such as correlation and spatial interpolation are done on the data to understand efficacy of dense deployment in better understanding the spatial variability and time-dependent changes to the local pollution indicators.