The need for generating radio signals with a high frequency stability and very low phase noise is a demanding requirement in communication systems. In low-power designs, such high-frequency signal generation is often the main source of power consumption, which necessitates low-power alternatives. Although tunnel diodes can generate high-frequency signals at low power, the generated signal is frequency unstable with high phase noise and unwanted harmonics. State-of-the-art address these limitations through injection locking, which requires an external signal generator that significantly increases the overall system power consumption. We present SharpPeak, a low-power long-range transmitter design that achieves high frequency stability using tunnel diodes without relying on external injection signals. This design lowers both system power and transmitter complexity, while improving frequency stability, phase noise, and frequency drifts. SharpPeak achieves high (370 kbps) data rates and long (1 km) communication range with under 175 μW power consumption at the RF front-end, advancing the state-of-the-art in energy-efficient communication systems. We believe that this work is a significant advancement in the development of a new generation of low-power communication systems.

Analog backscatter communication enables ultra-low-power and low-cost wireless connectivity, making it a promising alternative to traditional RFID systems. Although analog backscatter systems offer simplicity, they are by design less capable compared to their digital backscatter counterparts. A key drawback of existing analog backscatter systems is their single-tag operation, which poses limitations for applications that require multiple tags to function simultaneously. In this paper, we propose a novel low-cost analog backscatter system that allows multiple tags to sense the channel concurrently, thereby enabling signal strength-based localization and tracking of ambient RF sources. The ability to localize RF sources is essential for interference management, security, and optimization of wireless networks, particularly in dynamic environments. We evaluate our system in indoor settings, and demonstrate localization accuracy of RF sources with an average error of 20 cm in a 3m range, without requiring calibration. Our system is also capable of tracking RF sources with an average speed up to 0.6m/s with an average power consumption of 526µW, making it a promising solution for real-time and energy-constrained applications.

Analog backscatter enables sensing and communication at lower power consumption than digital backscatter. In analog tags, a sensor typically changes the resistance or capacitance value that then translates into a frequency change that is backscattered on top of a carrier. However, to read the sensor data with high resolution, a powerful receiver is required. The resolution of sensor data is limited by the FFT size and computation at the receiver. This limits the use of low-cost radio receivers, like RTL-SDR, that are gaining popularity in the RF community. We propose to use higher order harmonic frequencies, that are inherently generated by square wave backscattering at no extra cost, to derive sensor data using such low-cost radio receivers. We present experimental results that demonstrate the viability of our approach.

New battery-free sensor tags that interoperate with unmodified standard IoT devices and protocols can extend a sensor network's capabilities in a scalable and cost-effective manner. The tags achieve battery-free operation through backscatterrelated techniques, while the standard IoT devices avoid additional dedicated infrastructure by providing the unmodulated carrier that tags need to communicate. However, this approach requires coordination between devices transmitting, receiving and generating carrier, adds extra latency and energy consumption to already constrained devices, and increases interference and contention in the shared spectrum. We present a scheduling mechanism that optimizes the use of carrier generators, minimizing any disruptions to the regular nodes. We employ time slots to coordinate the unmodulated carrier while minimizing latency, energy consumption and overhead radio emissions. We propose an efficient scheduling algorithm that parallelizes communications with battery-free tags when possible and shares carriers among multiple tags concurrently. In our evaluation we demonstrate the feasibility and reliability of our approach in testbed experiments. We find that we can significantly reduce the excess latency and energy consumption caused by the addition of sensor tags when compared to sequential interrogation. We show that the gains tend to improve with the network size and that our solution is close to optimal on average.

New battery-free sensor tags that interoperate with unmodified standard IoT devices can extend a sensor network’s capabilities in a scalable and cost-effective manner. The tags achieve battery-free operation through backscatter-related techniques, while the standard IoT devices can provide the necessary unmodulated carrier, avoiding additional dedicated infrastructure. However, this approach presents multiple challenges: It requires coordination between nodes transmitting, receiving and generating carrier, adds extra latency and energy consumption to already constrained devices, and increases interference and contention in shared spectrum. We present TagAlong, a medium access mechanism for interoperable sensor tags that, besides coordinating, optimizes the use of carrier generators, minimizing the disruption caused to the operation of the regular nodes. We accomplish this by parallelizing communications with battery-free tags when possible, sharing carriers for multiple tags concurrently and synchronizing communications with tags that share carrier generators. We demonstrate the feasibility of TagAlong in a testbed deployment. In our evaluation we find that it can reduce the duration of the tags’ schedule by 60% while improving the energy and spectrum usage by 30% when compared to sequential interrogation with no difference in reliability.

Radio Tomographic Imaging (RTI) enables device free localization of physical objects by using signal attenuation in wireless networks. In this paper, we explore how existing RTI methods can be used in WiFi networks to do tomographic imaging. Moreover we analyze and evaluate the properties that affect the accuracy of WiFi tomographic imaging process.