WiROS: WiFi sensing toolbox for robotics

Many recent works have explored using WiFi-based sensing to improve SLAM, robot manipulation, or exploration. Moreover, widespread availability makes WiFi the most advantageous RF signal to leverage. But WiFi sensors lack an accurate, tractable, and versatile toolbox, which hinders their widespread adoption with robot's sensor stacks. We develop WiROS to address this immediate need, furnishing many WiFi-related measurements as easy-to-consume ROS topics. Specifically, WiROS is a plug-and-play WiFi sensing toolbox providing access to coarse-grained WiFi signal strength (RSSI), fine-grained WiFi channel state information (CSI), and other MAC-layer information (device address, packet id's or frequency-channel information). Additionally, WiROS open-sources state-of-art algorithms to calibrate and process WiFi measurements to furnish accurate bearing information for received WiFi signals. The open-sourced repository is: https://github.com/ucsdwcsng/WiRO

XRLoc: Accurate UWB Localization for XR Systems

Understanding the location of ultra-wideband (UWB) tag-attached objects and people in the real world is vital to enabling a smooth cyber-physical transition. However, most UWB localization systems today require multiple anchors in the environment, which can be very cumbersome to set up. In this work, we develop XRLoc, providing an accuracy of a few centimeters in many real-world scenarios. This paper will delineate the key ideas which allow us to overcome the fundamental restrictions that plague a single anchor point from localization of a device to within an error of a few centimeters. We deploy a VR chess game using everyday objects as a demo and find that our system achieves $2.4$ cm median accuracy and $5.3$ cm $90^\mathrm{th}$ percentile accuracy in dynamic scenarios, performing at least $8\times$ better than state-of-art localization systems. Additionally, we implement a MAC protocol to furnish these locations for over $10$ tags at update rates of $100$ Hz, with a localization latency of $\sim 1$ ms

Spoofing Attack Detection in the Physical Layer with Commutative Neural Networks

In a spoofing attack, an attacker impersonates a legitimate user to access or tamper with data intended for or produced by the legitimate user. In wireless communication systems, these attacks may be detected by relying on features of the channel and transmitter radios. In this context, a popular approach is to exploit the dependence of the received signal strength (RSS) at multiple receivers or access points with respect to the spatial location of the transmitter. Existing schemes rely on long-term estimates, which makes it difficult to distinguish spoofing from movement of a legitimate user. This limitation is here addressed by means of a deep neural network that implicitly learns the distribution of pairs of short-term RSS vector estimates. The adopted network architecture imposes the invariance to permutations of the input (commutativity) that the decision problem exhibits. The merits of the proposed algorithm are corroborated on a data set that we collected

ViWiD: Leveraging WiFi for Robust and Resource-Efficient SLAM

Recent interest towards autonomous navigation and exploration robots for indoor applications has spurred research into indoor Simultaneous Localization and Mapping (SLAM) robot systems. While most of these SLAM systems use Visual and LiDAR sensors in tandem with an odometry sensor, these odometry sensors drift over time. To combat this drift, Visual SLAM systems deploy compute and memory intensive search algorithms to detect `Loop Closures', which make the trajectory estimate globally consistent. To circumvent these resource (compute and memory) intensive algorithms, we present ViWiD, which integrates WiFi and Visual sensors in a dual-layered system. This dual-layered approach separates the tasks of local and global trajectory estimation making ViWiD resource efficient while achieving on-par or better performance to state-of-the-art Visual SLAM. We demonstrate ViWiD's performance on four datasets, covering over 1500 m of traversed path and show 4.3x and 4x reduction in compute and memory consumption respectively compared to state-of-the-art Visual and Lidar SLAM systems with on par SLAM performance

WiForceSticker: Batteryless, Thin Sticker-like Flexible Force Sensor

Any two objects in contact with each other exert a force that could be simply due to gravity or mechanical contact, such as a robotic arm gripping an object or even the contact between two bones at our knee joints. The ability to naturally measure and monitor these contact forces allows a plethora of applications from warehouse management (detect faulty packages based on weights) to robotics (making a robotic arms' grip as sensitive as human skin) and healthcare (knee-implants). It is challenging to design a ubiquitous force sensor that can be used naturally for all these applications. First, the sensor should be small enough to fit in narrow spaces. Next, we don't want to lay cumbersome cables to read the force values from the sensors. Finally, we need to have a battery-free design to meet the in-vivo applications. We develop WiForceSticker, a wireless, battery-free, sticker-like force sensor that can be ubiquitously deployed on any surface, such as all warehouse packages, robotic arms, and knee joints. WiForceSticker first designs a tiny $4$~mm~$\times$~$2$~mm~$\times$~$0.4$~mm capacitative sensor design equipped with a $10$~mm~$\times$~$10$~mm antenna designed on a flexible PCB substrate. Secondly, it introduces a new mechanism to transduce the force information on ambient RF radiations that can be read by a remotely located reader wirelessly without requiring any battery or active components at the force sensor, by interfacing the sensors with COTS RFID systems. The sensor can detect forces in the range of $0$-$6$~N with sensing accuracy of $<0.5$~N across multiple testing environments and evaluated with over $10,000$ varying force level presses on the sensor. We also showcase two application case studies with our designed sensors, weighing warehouse packages and sensing forces applied by bone joints

EdgeRIC: Empowering Realtime Intelligent Optimization and Control in NextG Networks

Radio Access Networks (RAN) are increasingly softwarized and accessible via data-collection and control interfaces. RAN intelligent control (RIC) is an approach to manage these interfaces at different timescales. In this paper, we develop a RIC platform called RICworld, consisting of (i) EdgeRIC, which is colocated, but decoupled from the RAN stack, and can access RAN and application-level information to execute AI-optimized and other policies in realtime (sub-millisecond) and (ii) DigitalTwin, a full-stack, trace-driven emulator for training AI-based policies offline. We demonstrate that realtime EdgeRIC operates as if embedded within the RAN stack and significantly outperforms a cloud-based near-realtime RIC (> 15 ms latency) in terms of attained throughput. We train AI-based polices on DigitalTwin, execute them on EdgeRIC, and show that these policies are robust to channel dynamics, and outperform queueing-model based policies by 5% to 25% on throughput and application-level benchmarks in a variety of mobile environments.Comment: 16 pages, 15 figure