Localization is a critical task for underwater robots, yet today's underwater localization systems are limited by their accuracy, scalability, and/or energy consumption (i.e., longevity). We present the design, implementation, and evaluation of EchoBLUE- an accurate, scalable, and low-power localization system for underwater robots.

Underwater backscatter is a promising technology for ultra-low-power underwater networking, but existing systems break down in mobile scenarios. This paper presents EchoRider, the first system to enable reliable underwater backscatter networking under mobility.

We present the design, implementation, and evaluation of 3D-BLUE, an ultra-low-power underwater 3D localization system that can be deployed on compact robots to accurately localize them in shallow underwater environments. 3D-BLUE’s design introduces two core components. First, it adapts a recent ultra-low-power underwater acoustic communication technology (called piezo-electric backscatter) to the underwater robotics localization problem; specifically, it integrates backscatter nodes into the underwater robot and uses them for localizing it. Second, it leverages the physical properties of the backscatter technology to efficiently extract spatio-temporal-spectral features from the backscatter signal; using these features, it devises a particle-filter-based algorithm to localize the corresponding robot accurately in challenging shallow-water environments. We implemented an end-to-end prototype of 3D-BLUE on a BlueROV2 robot and custombuilt backscatter localization system, and evaluated it in dozens of experimental trials in a pool. Our results demonstrate that 3D-BLUE can localize the robot with an accuracy of around 0.25m at close range and an accuracy of around 1.4m at a range of 10m. This high localization accuracy opens important commercial, naval, and environmental applications in challenging shallow-water environments such as shores, rivers, pools, and narrow waterways.

This paper investigates how an airborne node can eavesdrop on the underwater acoustic communication between submerged nodes. Conventionally, such eavesdropping has been assumed impossible as acoustic signals do not cross the water-air boundary. Here, we demonstrate that underwater acoustic communications signals can be picked up and (under certain conditions) decoded using an airborne mmWave radar due to the minute vibrations induced by the communication signals on the water surface. We implemented and evaluated a proof-of-concept prototype of our method and tested it in controlled (pool) and uncontrolled environments (lake). Our results demonstrate that an airborne device can identify the modulation and bitrate of acoustic transmissions from an uncooperative underwater transmitter (victim), and even decode the transmitted symbols. Unlike conventional over-the-air communications, our results indicate that the secrecy of underwater links varies depending on the modulation type and provide insights into the underlying reasons behind these differences. We also highlight the theoretical limitations of such a threat model, and how these results may have a significant impact on the stealthiness of underwater communications, with particular concern to submarine warfare, underwater operations (e.g., oil & gas, search & rescue, mining), and conservation of endangered species. Finally, our investigation uncovers countermeasures that can be used to improve or restore the stealthiness of underwater acoustic communications against such threats.

This study examines how an airborne device can intercept underwater acoustic signals exchanged between submerged nodes. It challenges the conventional belief that acoustic communications under the water are safe against eavesdropping since acoustics do not cross the water-air boundary. We show that an airborne mmWave radar can detect and decode underwater acoustic signals by picking up minute surface vibrations induced by these signals. The proof-of-concept was tested in controlled (pool) and uncontrolled (lake) environments, proving that an airborne adversary can identify modulation type, bitrate, and decode symbols from an uncooperative underwater transmitter using its radar sensing capabilities. We demonstrate that the secrecy of underwater links depends on modulation type, providing insights into countermeasures to enhance the security of underwater acoustic communications.

The ability to assess the quality of fruit and vegetables at scale can revolutionize the agriculture sector and significantly reduce food waste. In this paper, we present AgriTera, a novel solution for accurate non-invasive, and contract-free fruit ripeness sensing via sub-terahertz wireless signals. The key idea is that sugar and water concentrations in fruit (that are associated with fruit ripening) leave unique non-uniform footprints in the wide band spectrum of the reflected signal off of the fruits. AgriTera utilizes the sub-THz bands for its wide bandwidth, sensitivity to water, mm-scale penetration depth, and non-ionizing features that offer high-resolution inferences from the peel as well as the pulp underneath the peel. We develop a chemometric model that translates the reflection spectra to well-known ripeness metrics, namely Dry Matter and Brix. We conduct extensive over-the-air experiments with commercially available sub-THz transceivers. We compare our results with ground truth values captured by a specialized quality sensor and a vision-based scheme that infers ripeness based on changes in the appearance of the fruit. We demonstrate that AgriTera can accurately estimate Brix and Dry Matter in three different types of fruit with an average Normalized RMSE value of 0.55%, an error that yields a negligible impact on taste and is imperceivable by the consumer.

Can we build a battery-free underwater GPS? While underwater localization is a long-studied problem, in this paper, we seek to bring it to battery-free underwater networks. These recently-introduced networks communicate by simply backscattering (i.e., reflecting) acoustic signals. While such backscatter-based communication enables them to operate at net-zero power, it also introduces new and unique challenges for underwater localization.

We present the design, implementation, and evaluation of U2B, a technology that enables ultra-wideband backscatter in underwater environments. At the core of U2B's design is a novel metamaterialinspired transducer for underwater backscatter, and algorithms that enable self-interference cancellation and FDMA-based medium access control.

In this paper, we present our preliminary results towards a novel framework for non intrusive monitoring of home appliances using wireless signals such as those of Wi-Fi. The framework relies on the fact that the state - ON or OFF - of some home appliances causes detectable changes in the characteristics of the wireless channel. For proof of concept demonstrations, we select a number of home appliances whose operation is either characterized by moving parts or by generation of radio frequency interference (RFI) - properties which we show can be detected by analysing reflected radio frequency energy in the wireless channel. Using software-defined radios with a WiFi-like physical layer, we develop a prototype system for the detection of the following home appliances: (a) Ceiling Fan, (b) Pedestal Fan, (c) Washing Machine, and (d) Microwave Oven. We identify relevant features on the measured Channel State Information (CSI) that depict the environmental changes due to state of different home appliances. These features are used by a subsequent machine learning stage to classify the state and type of the home appliance. By achieving an average validation accuracy of 90.11% using a Support Vector Machine (SVM) classifier, our results indicate that the proposed framework has the potential for a cheap, easy to install, and an ubiquitous alternative to conventional non-intrusive load monitoring (NILM).

Many vehicular applications such as navigation, collision avoidance, and location-based services envisioned under intelligent transportation system (ITS) framework require position information with certain accuracy and reliability. Global navigation satellite system (GNSS) are widely deployed for absolute and relative positioning in a wide range of ITS applications due to their global coverage but these systems have limited accuracy and availability to meet the requirement of many safety-critical applications. This limitation has led to the development of cooperative positioning (CP) methods in a vehicular ad-hoc network. CP methods are based on the assumption that the inter-vehicular ranges are available through some radio ranging method, such as received signal strength, time of arrival, or time difference of arrival. However, the feasibility and accuracy of these radio ranging methods is limited due to the constraints of communication medium and rapidly changing vehicular environment. In this paper, we propose a generalized version of a theoretical framework for the measurement and analysis of inter-vehicular ranges by exchanging GNSS observables between vehicles. More specifically, we investigate the utilization of GNSS observables in the following four categories for range measurement: 1) absolute positions of the vehicles provided by GNSS receivers; 2) raw code pseudoranges; 3) single difference of the raw code pseudoranges; and 4) double difference of the raw code pseudoranges. The developed theoretical framework compares the accuracy of inter-vehicular range measurement using these four observables. This framework is further validated by actual field trials in different mobile environments using two vehicles equipped with GNSS receivers.

Accurate inter-vehicle range estimation is a critical requirement in Cooperative Positioning (CP) methods. CP methods are preferred over other positioning techniques in location-aware intelligent transportation applications due to increased availability and accuracy of positioning information. In this paper, we propose a new statistical Finite Impulse Response (FIR) filtering framework for estimating the accuracy of inter-vehicle ranges using Global Navigation Satellite System (GNSS) observables. FIR filters have been proven to be better state estimators than traditional Kalman filters in terms of robustness and sensitivity to unknown noise characteristics. However, they are affected by the choice of selecting an optimal value for window size which is usually unknown and involves an intricate procedure to find it. Here, we propose rather a simpler approach to deal with this problem where an ensemble of a priori particles, generated from a Gaussian density function, is used to deal with the window size problem. Only one particle which is closest to current measurement is retained and used in the update step of the filter. The proposed filtering architecture is tested and its enhanced performance is validated using actual field trials data sets.