Millimeter-wave (mmWave) imaging is increasingly being adopted across the supply chain and security industries to see through occlusions and detect hidden objects. Yet, most industrial mmWave imaging relies on a static, bulky, and expensive infrastructure to achieve precisely measured positions required for synthetic aperture imaging. In contrast, a handheld mmWave scanner can provide a compact, mobile and cost-effective alternative for high-resolution mmWave imaging, but it suffers from severe phase errors and image distortions due to motion inaccuracy.

mmWave radar based sensors are increasingly being deployed for robotics and automobile applications to sense the environment. Unfortunately, specular reflections of smooth and planar surfaces makes mmWave sensing unreliable at detecting objects and vehicles in the real world. In fact, due to the small wavelength of mmWave radars, most surfaces are smooth reflectors for the radar signals. This paper addresses this fundamental limitation of detecting specular reflectors by capturing the energy reflected away from the radar.

Small autonomous underwater vehicles (AUVs) that can perceive and explore the environment at low cost are increasingly being deployed for seafloor mapping and biome sensing. A critical sensing ability that many of the larger submarines possess is to perform directional SONAR using arrays of hydrophones to map the environment. Unfortunately, this ability to identify direction-of-arrival (DoA) remains too inaccurate or even infeasible for the small AUVs due to size, weight, and power (SWaP) constraints. This paper presents a new opportunity in augmenting this critical ability using a single hydrophone.

Accurately identifying materials in real-world environments is critical for applications in robotics, security screening and autonomous systems. While more expensive approaches for material characterization such as spectroscopy or ellipsometry are ill-suited for large scale deployment, RF-based solutions present a new potential low-cost alternative. Prior RF-based material sensing approaches are either too invasive, suffer from lower resolution or are highly specialized in particular materials, presenting a need for a more general-purpose solution.

Past research has widely explored the design and fabrication of resistive matrix-based tactile sensors for creating touch-sensitive devices. However, real-world deployment of resistive tactile sensing systems remains difficult for individuals with limited prior experience in embedded sensing due to challenges of portability, adaptivity, and efficiency. We introduce the WiReSens Toolkit, an accessible, open-source platform to bridge this gap. Central to our approach is adaptive hardware for interfacing with resistive sensors and a web-based GUI that streamlines access to advanced features for building scalable tactile sensing systems, including multi-device programming and wireless visualization across three communication protocols, autocalibration for adaptive sensitivity, and intermittent data transmission for low-power use. We validated the toolkit’s usability through a user study with 11 novice participants, who, on average, configured a tactile sensor with over 95% accuracy in under five minutes, calibrated sensors 10× faster than baseline methods, and showed improved sense-making of tactile data.

Figure 1: UltraPoser enables ubiquitous full-body pose estimation by integrating ultrasound sensing and IMU using commodity wearable devices.In addition to measuring IMU data, a smartphone and smartwatch are used to transmit and receive ultrasound signals.The extracted ultrasound features capture motions from joints without any attached devices and offer drift-free range measurements to complement IMU data for more accurate pose estimation.

A general consumer around the world gets general awareness about the causes of mechanical, electrical or digital problems in their house (or neighborhood) and how to fix them through common life experiences or learning from their peers. However, despite similar level of integration into everyday life, the understanding of wireless technologies lags significantly. This bottleneck leads to consumer dissatisfaction and reduced profits for cellular providers.

A general consumer around the world gets general awareness about the causes of mechanical, electrical or digital problems in their house (or neighborhood) and how to fix them through common life experiences or learning from their peers. However, despite similar level of integration into everyday life, the understanding of wireless technologies lags significantly. This bottleneck leads to consumer dissatisfaction and reduced profits for cellular providers.

The fundamental bottleneck in adapting data-driven wireless solutions to real world is the lack of tools for augmenting good quality data which is environment-specific. Indeed, much of the data required for popular data-driven wireless communication and sensing systems requires domain expertise beyond the reach of an average consumer. This demo presents a radical new vision for generating synthetic data in consumer-specific environments by leveraging the power of modern compute.

Recent years have seen the rapid deployment of low-cost CubeSats in low-Earth orbit, many of which experience significant latency (several hours) from the time information is gathered to the time it is communicated to the ground. This is primarily due to the limited availability of ground infrastructure that is bulky to deploy and expensive to rent. This article explores the opportunity in leveraging the extensive terrestrial LoRa infrastructure as a solution. However, the limited bandwidth and large amount of Doppler on CubeSats precludes these LoRa links to communicate rich satellite Earth images—instead, the CubeSats can at best send short messages. This article details our experience in designing LoRa-based satellite ground infrastructure that requires software-only modifications to receive packets from LoRa-enabled CubeSats recently launched by our team. We present Vista, a communication system that adapts encoding onboard the CubeSat and decoding configuration on commercial LoRa ground stations to allow images to be communicated. We perform a detailed evaluation of Vista by leveraging wireless channel measurements from a recent CubeSat (2021), and show that Vista can achieve 55.55% lower latency in retrieving data with 12.02 dB improvement in packet retrieval in the presence of terrestrial interference. We then evaluate Vista on a case study on land-use classification over images transmitted over the CubeSat link to further demonstrate a 4.56 dB improvement in image PSNR and 1.38× increase in classification accuracy over baseline approaches.

This paper asks: “Can we detect whether a fragile product, made of porcelain or glass is damaged as it travels along the supply chain, without opening its packaging?” We ask this question in the context of the multi-billion dollar global supply chain industry of fragile products that experience large overheads due to product returns. This paper presents MiLTOn, a novel acoustic and mm-wave based solution for through-box non-invasive product integrity sensing that is sensitive to even minute sub-mm cracks in the object. MiLTOn is inspired by acoustic vibrometry used for instance to monitor cracks in railroads. Unlike traditional vibrometry, MiL-TOn is unique in its ability to sense products non-invasively using an external transducer and microphone, neither of which are in direct physical contact of the object within the box. MiLTOn pro-cesses measurements from the microphone to design a robust and environment-independent product signature that can be used to sense presence of product defects. Our extensive evaluation on a large number of fragile products of diverse materials demonstrates 97% accuracy in identifying product damage.

With increasing numbers of nano-satellites (CubeSats) being launched into space in recent years, monitoring their health and debugging become crucial problems. In traditional systems such as big satellites, time-series telemetry is widely used by users to monitor the state of the satellite from the ground stations. However, today smaller CubeSats do not enjoy the benefits of live telemetry due to low throughput and lack of coverage from ground station infrastructure. In this poster, we conduct a motivation study based on data collected from public satellites in low-earth orbit to demonstrate the potential bottlenecks in obtaining live telemetry data from CubeSats. We then describe the design space of possible solutions and opportunities for researchers to improve time-series telemetry for CubeSats.

LoRa is a popular Low-Power Wide-Area Networking (LP-WAN) technology that allows devices powered by a ten year AA battery to connect to radio infrastructure miles away. One of the most promising features of LoRa is the ability to track the location of radios from a distance, enabling applications ranging from inventory tracking, smart infrastructure monitoring and structural health sensing. Yet, state-of-the-art LoRa localization systems experience errors of several tens or even hundreds of meters in location tracking, owing to the narrow bandwidth and limited battery life of LoRa devices.

Full duplex communication has been well explored in the past decade, with several physical layer designs proposed for effectively doubling the throughput of mobile devices. However, these systems are far from being well-deployed in the Wi-Fi and cellular applications, where most of the research has been done. In this paper, we investigate some of the fundamental pragmatic challenges in deploying full duplex that explain the reluctance from industry players in deploying this feature in commercial end-user devices and base stations at a large scale. Upon doing so, we identify that the problems lie not quite at the radio layer -- but at layers below and above it. At the hardware layer, we find that the power and complexity of IC-implementations of full duplex far exceeds that of other existing technologies that achieve similar throughput gain, such as multi-user MIMO. We further identified how higher-layer cellular and Wi-Fi traffic patterns and MAC protocols are fundamentally ill-suited to the full duplex paradigm. We report empirical analysis from the power consumption of an IC-implementation of a full duplex cancellation circuit, demonstrating that full duplex cancellation circuitry would consume at least 57 percent more power than a MIMO system achieving identical throughput gains. We also show how current traffic patterns reduce the benefits of full duplex to 1.25X instead of the expected 2X.

With the advent of low-power internet-of-things(IoT) technologies, city-scale IoT is expected to bridge everyday objects to the cloud. Connecting objects at that scale requires a wireless technology that provides long battery life and range of communication. Recent successful deployments of low-power wide-area networks (LP-WANs) render them the leading candidate enabling such connectivity. Yet, while LP-WAN clients achieve optimum performance in rural areas, they severely underperform in urban cities due to interference, worse multipath and noise. This significantly lowers their battery lives, range and data rates. This is further exacerbated by the fact that these low-power clients are also highly susceptible to security threats from much more powerful malicious adversaries.

Low-Power Wide-Area Networks (LP-WANs) are seeing wide-spread deployments connecting millions of sensors, each powered by a ten-year AA battery to radio infrastructure, often miles away. By design, iteratively querying all sensors in an LP-WAN may take several hours or even days, given the stringent battery limits of client radios. This precludes obtaining even an approximate real-time view of sensed information across LP-WAN devices over a large area, say in the event of a disaster, fault or simply for diagnostics.This paper presents QuAiL1, a system that provides a coarse aggregate view of sensed data across LP-WAN devices over a wide- area within a time span of just one LP-WAN packet. QuAiL achieves this by coordinating multiple LP-WAN radios to transmit their information synchronously in time and frequency despite their power constraints. We design each client’s transmission so that the base station can retrieve an approximate heatmap of sensed data by exploiting the spatial correlation of this data across clients. We further show how our system can be optimized for statistical and machine learning queries, all while maintaining the security and privacy of sensed data from individual clients. Our deployment over a 3 sq. km. LP-WAN deployment around CMU campus in Pittsburgh demonstrates a 4x faster information retrieval versus the state-of- the-art statistical methods to retrieve the spatial sensor heatmap at a desired resolution.

mm-Wave has emerged as an attractive high-speed wireless communication paradigm owing to the high available bandwidth at mm-wave frequencies. Full-Duplex has the potential to double the available capacity in the mm-wave bands by enabling simultaneous radio transmission and reception. While full-duplex has been extensively studied in sub-6 GHz bands, this paper exposes the unique challenges in porting this capability to mm-wave frequencies.

Wireless sensors have enabled a number of key applications. Due to their energy constraints, wireless sensors today communicate occasional short samples or pre-determined summary statistics of the data they collect. This means that computing every additional statistic at high fidelity incurs additional communication and energy overhead. This paper presents Joltik, a framework enabling general, future-proof, and energy-efficient analytics for low power wireless sensors. Joltik is general in that it summarizes sensed data from low-power devices without making assumptions on which specific statistical metric(s) are desired at the cloud and is future-proof, meaning it supports new, unforeseen metrics. Joltik is built upon recent theoretical advances in universal sketching, which can enable a Joltik sensor node to report a compact summary of observed data to enable a large class of statistical summaries. We address key system design and implementation challenges with respect to communication, memory, and computation bottlenecks that arise in practically realizing the potential benefits of universal sketching in the low-power regime. We present a proof-of-concept testbed evaluation of Joltik in LoRaWAN NUCLEO-L476RG boards and sensors. Across a range of realistic datasets, Joltik provides up to a 24.6× reduction in energy cost compared to transmitting raw data and outperforms many natural alternatives (e.g., sub-sampling, custom sketches, compressed sensing, and lossy compression) in terms of energy-accuracy trade-offs.

Network Function Virtualization has demonstrated how general purpose infrastructure in the cloud can match the performance of specialized hardware while providing greater flexibility and scalability. Yet, specialized hardware remains the preferred implementation choice for the wireless physical layer. This is primarily due to the tight latency requirements and high bandwidth demanded by operations at the wireless PHY, as opposed to the network-layer. This poster argues that in the era of low-power Internet of Things, virtualization of physical layer functions deserves a revisit. We specifically study low-power Internet of Things technologies that present a ripe opportunity for cloudification of wireless network functions, with their lower bandwidths and lax latency constraints. We present our vision of how physical layer function virtualization should be structured to maximize benefits and performance. We present a feasibility study on the latency and scalability limits of such virtualization for various physical layer functions that could leverage such an architecture.

At its peak, the Internet-of-Things will largely be composed of low-power devices with wireless radios attached. Yet, secure authentication of these devices amidst adversaries with much higher power and computational capability remains a challenge, even for advanced cryptographic and wireless security protocols. For instance, a high-power software radio could simply replay chunks of signals from a low-power device to emulate it. This paper presents a deep-learning classifier that learns hardware imperfections of low-power radios that are challenging to emulate, even for high- power adversaries. We build an LSTM framework, specifically sensitive to signal imperfections that persist over long durations. Experimental results from a testbed of 30 low-power nodes demonstrate high resilience to advanced software radio adversaries.

Low-Power Wide-Area Networks (LPWANs) are an emerging wireless platform which can support battery-powered devices lasting 10-years while communicating at low data-rates to gateways several kilometers away. Not all such devices will experience the promised 10 year battery life despite the high density of LPWAN gateways expected in cities. Transmission from devices located deep within buildings or in remote neighborhoods will suffer severe attenuation forcing the use of slow data-rates to reach even the closest gateway, thus resulting in battery drain. This paper presents Charm, a system that enhances both the battery life of client devices and the coverage of LPWANs in large urban deployments. Charm allows multiple LoRaWAN gateways to pool their received signals in the cloud, coherently combining them to detect weak signals that are not decodable at any individual gateway. Through a novel hardware and software design at the gateway, Charm carefully detects which chunks of the received signal need to be sent to the cloud, thereby saving uplink bandwidth. We present a scalable solution to decoding weak transmissions at city-scale by identifying the set of gateways whose signals need to be coherently combined over time. In evaluations over a test network and from simulations using traces from a large LoRaWAN deployment in Pittsburgh, Pennsylvania, Charm demonstrates a gain of up to 3x in range and 4x in client battery-life.

Future urban spaces are expected to see Unmanned Aerial Vehicles (UAVs) deployed for wide-ranging applications including product delivery, imaging and search-and-rescue. Yet today's UAVs rely critically on inertial sensors and GPS to remain stable in-flight, meaning they struggle to penetrate urban canyons where GPS is unavailable. This poster presents LoRaTilt, a system that maintains stability of a UAV using a Low-Power Wide-Area Network (LP-WAN) transmitter. We mount a light-weight and ten-year battery-powered LP-WAN transmitter on a UAV and process its signals from base stations up to hundreds of meter away. We demonstrate how our system estimates and corrects for UAV drift in GPS-denied settings with minimal impact on the cost and flight life of a UAV. Our proof-of-concept experiments show a promising median accuracy of 1.2326 mm and 0.0164 radians in estimating the displacement and orientation of a drone with a LoRa LP-WAN radio.