Covert radio-frequency exfiltration using short, low-duty-cycle transmissions highlights the need for continuous, low-latency spectrum monitoring capable of capturing fleeting emissions. Modern RF environments increasingly feature bursty, opportunistic signals that appear only for milliseconds, making them difficult for traditional receivers to detect. Conventional wideband sensing architectures depend on high-speed ADCs, local oscillators (LO), and mixer chains that draw significant power, while swept-LO and sequential scanning approaches suffer from limited real-time visibility. Parallel filter-bank designs improve instantaneous coverage but remain bulky and energy-intensive. We present SpecSentry, a low-power spectrum-monitoring architecture that maintains wideband visibility while reducing analog complexity by more than an order of magnitude. SpecSentry implements a "mark-fold-capture-detect" workflow in which the RF front end passively imprints frequency-dependent codes onto any active signal and folds the result into a compact representation sampled at rates more than 20× below the Nyquist rate. A two-stage neural regression pipeline analyzes this compressed snapshot to infer key signal characteristics, achieving median detection errors of 0.3 MHz for the bandwidth and 7.89 MHz for the center-frequency of the transmission over a 400 MHz band. This hybrid analog-ML design enables persistent, wideband, ultra-low-power monitoring suited for mobile, unattended, and maintenance-free deployments where detecting ephemeral transmissions is essential.

This demonstration presents LiTEfoot, an ultra-low power localization system leveraging ambient cellular signals. To address the limitations of traditional GPS-based tracking systems in terms of power consumption and latency, LiTEfoot employs a non-linear transformation of the cellular spectrum to achieve efficient self-localization. Our design uses a simple envelope detector to realize spectrum folding, enabling the identification of multiple active base stations. The LiTEfoot prototype shows a median localization error of 22 meters in urban areas and 50 meters in rural areas, consuming only 40 μJoules of energy per localization update.

In this paper, we introduce a low-power wide-area cellular localization system, called LiTEfoot. The core architecture of the radio carefully applies non-linear transform of the entire cellular spectrum to obtain a systematic superimposition of the synchronization signals at the baseband. The system develops methods to simultaneously identify all the base stations that are active at any cellular band from the transformed signal. The radio front end uses a simple envelop detector to realize the non-linear transformation. We build on this low-power radio to implement a self-localization system leveraging ambient 4G-LTE signals. We show that the core system can also be extended to other cellular technologies like 5G-NR and NB-IoT. The prototype achieves a median localization error of 22 meters in urban areas and 50 meters in rural areas. It can sense a 3GHz wideband LTE spectrum in 10ms using non-linear intermodulation while consuming 0.9 mJ of energy for a PCB-based implementation and 40 μJ for CMOS simulation. In other words, LiTEfoot tags can last for 11 years on a coin cell while continuously estimating location every 5 seconds. We believe that LiTEfoot will have widespread implications in city-scale asset tracking and other location-based services. The radio architecture can be useful beyond low-power self-localization and can find application in synchronization and communication on battery-less platforms.