This paper presents a UWB-IMU fusion approach to obtain location and orientation of an object in 6 degrees of freedom at the room level, without use of optical motion capture systems. When tested with different human movement patterns such as walking, running, jumping, and swirling on a wheeled chair, we obtain less than 10cm of 3D localization error and under 5° of orientation error at the 90thpercentile. We expect our system, called ObjectTrack, to enable spatial audio and interaction for VR/AR applications, enable precision tracking of objects, and for localization of robotic motion systems. ObjectTrack significantly reduces the cost barrier by about 50× compared to popular motion capture systems.

Indoor wireless ranging localization is a promising approach for low-power and high-accuracy localization of wearable devices. A primary challenge in this domain stems from non-line of sight propagation of radio waves. This study tackles a fundamental issue in wireless ranging: the unpredictability of real-time multipath determination, especially in challenging conditions such as when there is no direct line of sight. We achieve this by fusing range measurements with inertial measurements obtained from a low cost Inertial Measurement Unit (IMU). For this purpose, we introduce a novel asymmetric noise model crafted specifically for non-Gaussian multipath disturbances. Additionally, we present a novel Levenberg-Marquardt (LM)-family trust-region adaptation of the iSAM2 fusion algorithm, which is optimized for robust performance for our ranging-IMU fusion problem. We evaluate our solution in a densely occupied real office environment. Our proposed solution can achieve temporally consistent localization with an average absolute accuracy of ∼0.3m in real-world settings. Furthermore, our results indicate that we can achieve comparable accuracy even with infrequent range measurements down to 1Hz.

This paper explores the problem of seeking a wireless ultra-wideband radio-enabled physical package in a cluttered environment using a mobile robot. We enable a robot to navigate (using LiDAR and wheel odometry measurements) to a physical package by continuously calculating the distance from the package (using UWB-based wireless ranging). We assume no explicit support from the surrounding environment where the package is present and, therefore, intend to perform this package-seeking using sensors on the robot and a UWB-wireless radio installed inside the package. We expect such a system to be used for high-value packages or packages with perishable contents. The technical innovations in this paper stem from the robot utilizing its own movements and continuous distance measurements with the UWB inside the package to localize the package and then seek out an approach path for autonomous navigation using LiDAR. We demonstrate our end-to-end robotic system using real-world experiments with varying levels of clutter and navigational complexity.

We investigate the issue of deploying applications on a set of loosely coupled compute devices, connected through a wireless mesh, typical in community networks. Wireless mesh networks experience significant temporal and spatial variations in link bandwidth. When application components, modeled as a directed acyclic graph, need to be scheduled on such a mesh with bandwidth constraints (and variations), the problem of mapping components to specific compute nodes becomes an instance of bin packing with constraints of CPU, memory, and bandwidth limits within the mesh. To make the scheduling tractable, we propose BASS (Bandwidth Aware Scheduling System), and develop heuristics for scheduling, based on the directed graph topology of the application components. We evaluate BASS on an emulated mesh using bandwidth traces collected from an actual wireless testbed - CityLab. Detailed evaluations show that contemporary orchestration frameworks can plug in BASS to provide better end-to-end performance for the applications deployed on the mesh while reducing resource utilization.

This paper proposes a UWB beacon to be installed on trucks carrying unusual loads to warn approaching autonomous vehicles (AVs) of unconventional cargo. We propose several approaches, including one where the AV only receives UWB messages. We plan to use a QR code to provide secure communication between the AV and the cargo. The proposed system generalizes to static and dynamic traffic anomalies.

In this work, we present a secure Internet-of-Things framework enabled by UWB, which allows users to wirelessly interact with any public and home appliances in a more secure, seamless and efficient manner. we envision an ecosystem of appliances and mobile devices equipped with UWB transceivers, where the appliances are capable of imposing location-based access control in precise and configurable zones and ensuring security against eavesdropping and hijacking by malicious attackers. We propose a location-based access control algorithm that is robust against range spoofing. Further, we propose the network and application layer protocols that enable one generic user-end application to access the control interface of any appliances and adapt to context.

Recording 3D movements of a user's hand, robotic arms, or an object, even in a small confined space, has several applications in AR/VR, robotics, movement science, and 3D modeling and rendering. Existing camera-based tracking systems, though extremely accurate, are quite expensive and suffer from issues of occlusion and face difficulties when operating in extremely dark or extremely bright environments. We contend that trading-off a bit of accuracy while reducing costs and enabling more flexible operating environment might be worth exploring. This paper presents UTrack3D, a table-top setup that tracks the movements of an object in 3D space using embedded low-cost ultra-wideband (UWB) radios. The core idea is to continuously track the changes in phase as captured from UWB signal's channel impulse response (CIR) derived from the UWB messages received at a set of dual-antenna UWB receivers. Each of our custom dual-antenna receivers captures the UWB signal from two corners of a cuboid allowing us to perform relative phase measurements. The main challenges in the solution are caused by a location-dependant large variation in the signal amplitudes and corruption of the CIR due to multipath. UTrack3D tackles these challenges via a signal processing pipeline fusing a forward localization process which tracks the object's location using UWB CIR phase, and a posterior location check process, which validates the estimated location. UTrack3D is implemented on commercial-off-the-shelf (COTS) UWB chips, and provides a 90th percentile accuracy of 9 mm in a table-top 3D region (1.5m × 0.8m × 0.8m). We evaluate the effects of additional UWB receivers, effect of different movement speeds, and effect of small-scale signal blocking using different materials. We expect UTrack3D to allow researchers a rich new environment for further advancing UWB-based 3D tracking.

This paper presents an ultra-wideband (UWB) based two-factor authentication (2FA) platform, called UWB-Auth, designed as carriable or wearable devices. UWB-Auth eliminates various social engineering attacks, including phishing attack, 2FA-fatigue attack, co-located attack etc., on existing 2FA solutions like Duo and reveals simple and fast user interaction. The key innovation of UWB-Auth is a novel combination of location authentication via UWB, checking whether a legitimate token is in the vicinity of the login device with centimeter-level accuracy, followed by an abstraction layer allowing different knowledge-based or biometric-based authentication, ensuring the user's identity and intent to login. Moreover, UWB-Auth reverses the sequence in which the two factors are verified, providing robust defences against data breach. We develop 3 UWB-Auth prototypes: a key-chain token, a smartwatch with commercial knowledge/biometric factor, and a smartring with customized knowledge/biometric authentication algorithm to demonstrate the effectiveness of UWB-Auth. Overall, UWB-Auth completes the whole authentication process in 4 seconds, and completely rejects malicious requests when the token is 20cm or 10^\circ outside a small valid physical area near the login device. Even when a malicious entity gains physical access to the token, UWB-Auth stops attack attempts via knowledge and biometric authentication.

This paper presents UnSpoof, a UWB localization system that can detect and localize distance-spoofing tags with several collaborative passively-receiving anchors. We propose novel formulations that enable passively-receiving anchors to deduce their time-of-arrival (ToA) and time-difference-of-arrival (TDoA) just by overhearing standard two-way ranging (TWR) messages between the tag and one active anchor. Our ToA formulation can be used to precisely localize an honest tag, and to detect a distance-spoofing tag that falsely reports its timestamps. Additionally, our TDoA formulation enables spoof-free localization, which can be used to track down and apprehend a malicious tag Our experimental evaluation shows 30cm $75^{th}$ percentile error for ToA-based honest tag localization, and sub-meter error for TDoA-based localization for spoofing tags. We demonstrate successful detection of distance reduction and enlargement attacks inside the anchors’ convex hull, and graceful degradation outside.

While aging adults frequently prefer to "age in place", their children can worry about their well-being, especially when they live at a distance. Many in-home systems are designed to monitor the real-time status of seniors at home and provide information to their adult children. However, we observed that the needs and concerns of both sides in the information sharing process are often not aligned. In this research, we examined the design of a system that mitigates the privacy needs of aging adults in light of the information desires of adult children. We apply an iterative process to design and evaluate a visualization of indoor location data and compare its benefits to displaying raw video from cameras. We elaborate on the tradeoffs surrounding privacy and awareness made by older adults and their children, and synthesize design criteria for designing a visualization system to manage these tensions and tradeoffs.

On-demand video contributes a large fraction of the data traffic on mobile networks. This share is expected to increase even more drastically in the coming years. While the cellular infrastructure is continuously evolving to keep pace with this increasing demand, it is necessary to ensure that sufficient bandwidth is reserved for other latency-sensitive realtime applications like video conferencing and multiplayer video games. A tangible approach involves reducing on-demand video load on cellular networks, especially from users on the move. We see an opportunity for cellular load reduction using edge nodes based on two observations: (1) video streaming is mostly a download-only operation with sequential data access; and (2) short-range mmWave links can deliver an extremely high throughput for nearby recipients of data. The knowledge of the user's planned travel route creates opportunities for prescient prefetching and delivering the content as the vehicle passes through just in time, using mmWave devices on en route edge nodes. ClairvoyantEdge is a novel networked system infrastructure that leverages inter-edge node communication and the knowledge of users' trajectories to plan and deliver buffered video segments to the vehicles passing by. To evaluate ClairvoyantEdge, we built a comprehensive end-to-end emulation-based workflow that incorporates in situ field measurements of mmWave links into our own homegrown emulation framework. With a minuscule 0.12% coverage of a 46km2geographical area employing 20 edge nodes distributed in that area providing short-range mmWave access to passing vehicles, we achieve an average reduction of up to 21% in cellular bandwidth usage for video downloads, using a real-world workload comprising 758 vehicles. Our results validate the promise of ClairvoyantEdge for incorporation in future edge infrastructure evolution.

Wireless ranging, where two or more wireless devices determine their relative distance by exchanging messages, is a fundamental primitive in short-range distance measurements and localization. These methods are used by fine-time measurement in WiFi, and ultra-wideband (UWB) radios which is seeing an uptick in the smartphone market. Since ranging depends on accurate timestamps, the clock drifts between devices is an important consideration that affects ranging precision. Several applications have been proposed in the short-range localization context with their own ranging protocols and almost all of them perform clock-drift analysis to assess the quality of the protocol and ranging formulation. Although this is standard practice, there lacks a way to quantify and compare the extent of clock-drift introduced measurement errors. In this work, we introduce a metric based on numerical simulations that enables direct comparisons across schemes, and demonstrate how it helps analyze distance measurement errors.

Enabling reliable indoor localization can facilitate several new applications akin to how outdoor localization systems, such as GPS, have facilitated. Currently, a few key hurdles remain that prevent indoor localization from reaching the same stature. These hurdles include complicated deployment, tight time synchronization requirements from time difference of arrival protocols, and a lack of mechanism to allow a pan-building seamless solution. This work explores ways in which these key hurdles can be overcome to enable a more pervasive use of indoor localization. We propose a novel passive ranging scheme where clients overhear ongoing two-way ranging wireless communication between a few infrastructure nodes, and compute their own relative location without transmitting any signals (preserving user privacy). Our approach of performing two-way ranging between infrastructure nodes removes a crucial timing requirement in traditional time-difference-of-arrival methods thereby relaxing the synchronization requirements imposed by previous techniques. We use ultra-wideband wireless (UWB) radios that can easily penetrate building materials so that spanning an entire floor of a large building with just a few infrastructure nodes is possible. We build working prototypes, including the necessary hardware, and demonstrate the plug-and-play nature of our proposed solution. Our evaluation in three indoor spaces shows 1–2 meter-level localization accuracy with areas as large as 2241 m2. We expect our explorations to re-trigger interest in novel applications for indoor spaces based on fine-grained indoor location knowledge.

This demonstration presents the Location-Specific Public Broadcast system, in which localization and wireless broadcasts are combined to deliver a scalable, privacy preserving, and generic solution to location-based services. Other interactive location-based systems either preload information on the user devices, which are usually bulky, difficult to update and have to be custom-made for each venue, or fetch information from cloud based on location, which sacrifices user privacy. In our system, a wireless access point continuously broadcasts information tagged by locations of interest, and the mobile devices performing passive localization select and display the information pertinent to themselves. In this case, the location-specific information is stored only on the WiFi AP, and the phone app would be ultra lightweight with only the location calculation and information filtering functionalities, which can be used in any space. We envision our solution being adopted in public places, such as museums, aquariums, etc., for location-specific information delivery purposes, like enhancing interactive experience for visitors.

Spatiotemporal variation in cellular bandwidth availability is well-known and could affect a mobile user's quality of experience (QoE), especially while using bandwidth intensive streaming applications such as movies, podcasts, and music videos during commute. If such variations are made available to a streaming service in advance it could perhaps plan better to avoid sub-optimal performance while the user travels through regions of low bandwidth availability. The intuition is that such future knowledge could be used to buffer additional content in regions of higher bandwidth availability to tide over the deficits in regions of low bandwidth availability. Foresight is a service designed to provide this future knowledge for client apps running on a mobile device. It comprises three components: (a) a crowd-sourced bandwidth estimate reporting facility, (b) an on-cloud bandwidth service that records the spatiotemporal variations in bandwidth and serves queries for bandwidth availability from mobile users, and (c) an on-device bandwidth manager that caters to the bandwidth requirements from client apps by providing them with bandwidth allocation schedules. Foresight is implemented in the Android framework. As a proof of concept for using this service, we have modified an open-source video player---Exoplayer---to use the results of Foresight in its video buffer management. Our performance evaluation shows Foresight's scalability. We also showcase the opportunity that Foresight offers to ExoPlayer to enhance video quality of experience (QoE) despite spatiotemporal bandwidth variations for metrics such as overall higher bitrate of playback, reduction in number of bitrate switches, and reduction in the number of stalls during video playback.

High-precision tracking of a pen-like instrument's movements is desirable in a wide range of fields spanning education, robotics, and art, to name a few. The key challenge in doing so stems from the impracticality of embedding electronics in the tip of such instruments (a pen, marker, scalpel, etc.) as well as the difficulties in instrumenting the surface that it works on. In this paper, we present ITrackU, a movement digitization system that does not require modifications to the surface or the tracked instrument's tip. ITrackU fuses locations obtained using ultra-wideband radios (UWB), with an inertial and magnetic unit (IMU) and a pressure sensor, yielding multidimensional improvements in accuracy, range, cost, and robustness, over existing works. ITrackU embeds a micro-transmitter at the base of a pen which creates a trackable beacon, that is localized from the corners of a writing surface. Fused with inertial motion sensor and a pressure sensor, ITrackU enables accurate tracking. Our prototype of ITrackU covers a large 2.5m × 2m area, while obtaining around 2.9mm median error. We demonstrate the accuracy of our system by drawing numerous shapes and characters on a whiteboard, and compare them against a touchscreen and a camera-based ground-truthing system. Finally, the produced stream of digitized data is minuscule in volume, when compared with a video of the whiteboard, which saves both network bandwidth and storage space.

The coronavirus pandemic is altering our way of life. As more establishments open, there is an expectation that people will follow physical distancing guidelines. The implementation, however, is poor; just putting up warning signs appealing the general public to keep a distance of 6 feet from others is hardly enough. In this paper we consider the design of a wearable device that raises an alarm if another similar device is detected within a set distance. It uses off-the-shelf ultra-wideband radio technology for real-time, accurate distance estimation from others in the vicinity. We design an one-to-all ranging protocol that is able to accurately estimate distance to neighboring devices and warn the user if the distance falls below a certain established threshold within a short time. The device must compensate for human occlusions and avoid unnecessary warnings when physical barriers exist between devices. We implement and evaluate our protocol in a small testbed with custom prototype hardware as well as in simulation. Our ranging protocol is capable of performing up to 10 distance measurements per second, while avoiding packet collisions. The overall percentage of rangings completed is around 65% in a 10-node network, and the distance accuracy is around 20cm even with frequent human occlusions. We believe this prototype will provide the first steps to ensure physical distancing in various real-world settings.

Unmanned Aerial Vehicles (UAVs) are quickly becoming a viable delivery platform for physical packages with promise to transform the retail industry's supply chains. This work focuses on the last leg of such delivery: physically approaching a customer's landing zone. This has traditionally relied on a combination of GPS and computer-vision to locate and identify a landing zone. Instead of using computer vision, we propose to use ultra-wideband beacons (UWB) to assist in the landing process. The UAV's location relative to the landing zone is continuously measured based on the wireless propagation delay between the UAV and the landing zone's corners. We show that a single pair of wireless devices, one at the UAV and one at the landing zone, suffices to obtain the UAV's location. The landing zone's UWB device, connected to multiple antennas, receives multiple copies of the UAV's signals, that enables a sub-decimeter 3D-localization of the UAV. This helps the UAV's control logic governing the approach and landing process.

This paper shows the feasibility of identifying liquids by shining ultra-wideband (UWB) wireless signals through them. The core opportunity arises from the fact that wireless signals experience distinct slow-down and attenuation when passing through a liquid, manifesting in the phase, strength, and propagation delay of the outgoing signal. While this intuition is simple, building a robust system entails numerous challenges, including (1) pico-second scale time of flight estimation, (2) coping with integer ambiguity due to phase wraps, (3) pollution from hardware noise and multipath, and (4) compensating for the liquid-container's impact on the measurements. We address these challenges through multiple stages of signal processing without relying on any feature extraction or machine learning. Instead, we model the behavior of radio signals inside liquids (using principles of physics), and estimate the liquid's permittivity, which in turn identifies the liquid. Experiments across 33 different liquids (spread over the whole permittivity spectrum) show median permittivity error of 9%. This implies that coke can be discriminated from diet coke or pepsi, whole milk from 2% milk, and distilled water from saline water. Our end system, LiquID, is cheap, non-invasive, and amenable to real-world applications.

This paper explores the possibility of injecting mobility into wireless network infrastructure. We envision WiFi APs on wheels that move to optimize user performance. Movements need not be all around the floor, neither do they have to operate on batteries. As a first step, WiFi APs at home could remain tethered to power and Ethernet outlets while moving in small areas (perhaps under the couch). If such systems prove successful, perhaps future buildings could offer explicit support for network infrastructure mobility. This paper begins with a higher level discussion of robotic wireless networks-the opportunities and the hurdles-and then pivots by developing a smaller slice of the vision through a system called iMob. With iMob, a WiFi AP is mounted on a Roomba robot and made to periodically move within a 2x2 sqft region. The core questions pertain to finding the best location to move to, such that the SNRs from its clients are strong, and the interferences from other APs are weak. Our measurements show that the richness of wireless multipath offers significant opportunities-even within a 2x2 sqft region, locations exist that are 1:7x better than the average location in terms of throughput. When multiple APs in a neighborhood coordinate, the gains can be even higher. In sum, although infrastructure mobility has been discussed in the context of Google Balloons, ad hoc networks, and delay tolerant networks, we believe that the possibility of moving our personal devices in homes and offices is relatively unexplored, and could open doors to new kinds of innovation.

Internet connectivity on mobile devices is an essential commodity in today's world. While outdoors, most people connect through cellphone towers on 3G or 4G. However, cellphone tower coverage is not uniform and is affected by electromagnetic shadows cast by large structures, multipath, and absorption by various surfaces. Users with high data needs suffer in such locations due to insufficient network bandwidth. A similar insufficiency can also be felt by flash crowds in locations with otherwise moderate signal strength due to division of the available bandwidth.

Inertial sensors continuously track the 3D orientation of a flying drone, serving as the bedrock for maneuvers and stabilization. However, even the best inertial measurement units (IMU) are prone to various types of correlated failures. We consider using multiple GPS receivers on the drone as a fail-safe mechanism for IMU failures. The core challenge is in accurately computing the relative locations between each receiver pair, and translating these measurements into the drone's 3D orientation. Achieving IMU-like orientation requires the relative GPS distances to be accurate to a few centimeters -- a difficult task given that GPS today is only accurate to around 1-4 meters. Moreover, GPS-based orientation needs to be precise even under sharp drone maneuvers, GPS signal blockage, and sudden bouts of missing data. This paper designs SafetyNet, an off-the-shelf GPS-only system that addresses these challenges through a series of techniques, culminating in a novel particle filter framework running over multi-GNSS systems (GPS, GLONASS, and SBAS). Results from 11 sessions of 5-7 minute flights report median orientation accuracies of 2° even under overcast weather conditions. Of course, these improvements arise from an increase in cost due to the multiple GPS receivers, however, when safety is of interest, we believe that tradeoff is worthwhile.

This paper explores the possibility of injecting mobility into wireless network infrastructure. We envision WiFi access points on wheels that move to optimize user performance. Movements need not be all around the floor, neither do they have to operate on batteries. As a first step, WiFi APs at home could remain tethered to power and Ethernet outlets while moving in small areas (perhaps under the couch). If such systems prove successful, perhaps future buildings and cities could offer explicit support for network infrastructure mobility. This paper begins with a higher level discussion of robotic wireless networks -- the opportunities and the hurdles -- and then pivots by developing a smaller slice of the vision through a system called iMob. With iMob, a WiFi AP is mounted on a Roomba robot and made to periodically move within a 2x2 sqft region. The core research questions pertain to finding the best location to move to, such that the SNRs from its clients are strong, and the interferences from other APs are weak. Our measurements show that the richness of wireless multipath offers significant opportunities -- even within a 2x2 sqft region, locations exist that are 1.7x better than the average location in terms of throughput. When multiple APs in a neighborhood coordinate, the gains can be even higher. In sum, although infrastructure mobility has been discussed in the context of Google Balloons, ad hoc networks, and delay tolerant networks, we believe that the possibility of moving our personal devices in homes and offices is relatively unexplored, and could open doors to new kinds of innovation.

In this paper, we present Esense, a new paradigm of communication between devices that have fundamentally different physical layers. Esense is based on sensing and interpreting energy profiles. While our ideas are generic enough to be applicable in a variety of contexts, we illustrate the usefulness of our ideas by presenting novel solutions to existing problems in three distinct research domains. As part of these solutions, we demonstrate the ability to communicate between devices that follow two different standards: IEEE 802.11 and 802.15.4. We consider two scenarios here: 1) where there is no background traffic and 2) where there is background 802.11 traffic. In each case, we build an "alphabet setâ: a set of signature packet sizes that can be used for Esense communication. Specifically for the second case, we take a measurement-based alphabet set construction by considering WiFi traces from actual deployments. Based on practical observations and experiments, we theoretically quantify the maximum achievable transmission rate when using Esense. With background traffic, we could potentially construct an alphabet of size as high as 100. Such a large alphabet size promises efficient Esense communication. We show via a prototype implementation that effective communication is indeed feasible even when both sides use different physical layers.

In this paper, we present Esense: a new paradigm of communication between devices that have fundamentally different physical layers. The same communication framework also works between devices that have the same physical layer, which are out of communication range but within carrier-sense range. Esense is based on sensing and interpreting energy profiles. While our ideas are generic enough to be applicable in a variety of contexts, we illustrate the usefulness of our ideas by presenting novel solutions to existing problems in three distinct research domains. As part of these solutions, we demonstrate the ability to communicate between devices that follow two different standards: IEEE 802.11 and 802.15.4. We build an ``alphabet set'': a set of signature packet sizes which can be used for Esense. For this, we take a measurement based approach by considering WiFi traces from actual deployments. We then analyze the channel activity resulting from these traces and build an appropriate alphabet set for Esense communication. Our results show that we could potentially construct an alphabet of size as high as 100; such a large alphabet size promises efficient Esense communication. We also validate this alphabet set via a prototype implementation, and show that effective communication is indeed feasible even when both sides use different physical layers.