Roshan Ayyalasomayajula

@engineering.buffalo.edu

Assistant Professor at the Department of Computer Science and Engineering
University at Buffalo

https://researchid.co/asroshan

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Networks and Communications, Signal Processing, Computer Engineering, Electrical and Electronic Engineering

Scopus Publications

558

Scholar Citations

Scholar h-index

Scholar i10-index

Scopus Publications

WAIS: Leveraging WiFi for Resource-Efficient SLAM
Aditya Arun, William Hunter, Roshan Ayyalasomayajula, and Dinesh Bharadia
ACM
Interest in autonomous navigation and exploration for indoor applications has spurred research into indoor Simultaneous Localization and Mapping (SLAM) robot systems. While most of these SLAM systems use camera and LiDAR sensors in tandem with an odometry sensor, these odometry sensors drift over time. Visual (LiDAR/camera-based) SLAM systems deploy compute and memory-intensive search algorithms to detect 'Loop Closures' to combat this drift, making the trajectory estimate globally consistent. Instead, WAIS (WiFi Assisted Indoor SLAM) demonstrates using WiFi-based sensing can reduce this resource intensiveness drastically. By covering over 1500 m in realistic indoor environments and WiFi deployments, we showcase 4.3× and 4× reduction in compute and memory consumption compared to state-of-the-art Visual and Lidar SLAM systems. Incorporating WiFi into the sensor stack improves the resiliency of the Visual-SLAM system. We find the 90th percentile translation errors improve by ~ 40% and orientation errors by ~ 60% compared with purely camera-based systems. Additionally, we open-source a toolbox, WiROS, to furnish online and compute efficient WiFi measurements. Codebase: https://github.com/ucsdwcsng/WAIS.git Dataset: https://forms.gle/XWLLBnWsMct1BRnR8

Users are Closer than they Appear: Protecting User Location from WiFi APs
Roshan Ayyalasomayajula, Aditya Arun, Wei Sun, and Dinesh Bharadia
ACM
WiFi-based indoor localization has now matured for over a decade. Most of the current localization algorithms rely on the WiFi access points (APs) in the enterprise network to localize the WiFi user accurately. Thus, the WiFi user's location information could be easily snooped by an attacker listening through a compromised WiFi AP. With indoor localization and navigation being the next step towards automation, it is important to give users the capability to defend against such attacks. In this paper, we present MIRAGE, a system that can utilize the downlink physical layer information to create a defense against an attacker snooping on a WiFi user's location information. MIRAGE achieves this by utilizing the beam-forming capability of the transmitter that is already part of the WiFi standard protocols. With this initial idea, we have demonstrated that the user can obfuscate his/her location from the WiFi AP always with no compromise to the throughput of the existing WiFi communication system through the real-world prototype, and reduce the user location accuracy of the attacker from 2.3m to more than 10m through simulation.

Real-time low-latency tracking for UWB tags
Aditya Arun, Tyler Chang, Yizheng Yu, Roshan Ayyalasomayajula, and Dinesh Bharadia
ACM
Wide-scale adoption of VR/AR technologies in gaming, video conferencing, and for other remote telepresence applications demands limb tracking for a more immersive experience. In an attempt to bolster limb tracking, we present UWBTrac, a UWB + IMU based fusion tracker for VR applications. In this demo, and accompanying video1, we showcase this UWB tracker in comparison with HTC Vive VR trackers.

P2SLAM: Bearing Based WiFi SLAM for Indoor Robots
Aditya Arun, Roshan Ayyalasomayajula, William Hunter, and Dinesh Bharadia
Institute of Electrical and Electronics Engineers (IEEE)
A recent spur of interest in indoor robotics has increased the importance of robust simultaneous localization and mapping algorithms in indoor scenarios. This robustness is typically provided by the use of multiple sensors which can correct each others' deficiencies. In this vein, exteroceptive sensors, like cameras and LiDARs, employed for fusion are capable of correcting the drifts accumulated by wheel odometry or inertial measurement units (IMU's). However, these exteroceptive sensors suffer their own deficiencies in highly structured environments and dynamic lighting conditions. This paper will present WiFi as a robust and straightforward sensing modality capable of circumventing these issues. Specifically, we make three contributions. First, we will understand the necessary features to be extracted from WiFi signals. Second, we characterize the quality of these measurements. Third, we integrate these features with odometry into a state-of-art GraphSLAM backend. We present our results in a 25 x 30 m and 50 x 40 environment and robustly test the system by driving the robot a cumulative distance of over 1225 m in these two environments. We show an improvement of at least 6x with respect to odometry-only estimation and perform on par with one of the state-of-the-art Visual-based SLAM.

Sound source localization based on multi-task learning and image translation network
Yifan Wu, Roshan Ayyalasomayajula, Michael J. Bianco, Dinesh Bharadia, and Peter Gerstoft
Acoustical Society of America (ASA)
Supervised learning-based sound source localization (SSL) methods have been shown to achieve a promising localization accuracy in the past. In this paper, MTIT, SSL for indoors using Multi-Task learning and Image Translation network, an image translation-based deep neural networks (DNNs) framework for SSL is presented to predict the locations of sound sources with random positions in a continuous space. We extract and represent the spatial features of the sound signals as beam response at each direction which can indicate the chance of the source in each point of the room. We utilize the multi-task learning (MTL) based training framework. There are one encoder and two decoders in our DNN. The encoder aims to obtain a compressed representation of the input beamspectrum surfaces while the two decoders focus on two tasks in parallel. One decoder focuses on resolving the multipath caused by reverberation and the other decoder predicts the source location. Since these two decoders share the same encoder, by training these two decoders in parallel, the shared representations are refined. We comprehensively evaluate the localization performance of our method in the simulated data, measured impulse response and real recordings datasets and compare it with multiple signal classification, steered response power with phase transform, and a competing convolutional neural network approach. It turns out that MTIT can outperform all of the baseline methods in a dynamic environment and also can achieve a good generalization performance.

ULoc: Low-Power, Scalable and cm-Accurate UWB-Tag Localization and Tracking for Indoor Applications
Minghui Zhao, Tyler Chang, Aditya Arun, Roshan Ayyalasomayajula, Chi Zhang, and Dinesh Bharadia
Association for Computing Machinery (ACM)
A myriad of IoT applications, ranging from tracking assets in hospitals, logistics, and construction industries to indoor tracking in large indoor spaces, demand centimeter-accurate localization that is robust to blockages from hands, furniture, or other occlusions in the environment. With this need, in the recent past, Ultra Wide Band (UWB) based localization and tracking has become popular. Its popularity is driven by its proposed high bandwidth and protocol specifically designed for localization of specialized "tags". This high bandwidth of UWB provides a fine resolution of the time-of-travel of the signal that can be translated to the location of the tag with centimeter-grade accuracy in a controlled environment. Unfortunately, we find that high latency and high-power consumption of these time-of-travel methods are the major culprits which prevent such a system from deploying multiple tags in the environment. Thus, we developed ULoc, a scalable, low-power, and cm-accurate UWB localization and tracking system. In ULoc, we custom build a multi-antenna UWB anchor that enables azimuth and polar angle of arrival (henceforth shortened to '3D-AoA') measurements, with just the reception of a single packet from the tag. By combining multiple UWB anchors, ULoc can localize the tag in 3D space. The single-packet location estimation reduces the latency of the entire system by at least 3×, as compared with state of art multi-packet UWB localization protocols, making UWB based localization scalable. ULoc's design also reduces the power consumption per location estimate at the tag by 9×, as compared to state-of-art time-of-travel algorithms. We further develop a novel 3D-AoA based 3D localization that shows a stationary localization accuracy of 3.6 cm which is 1.8× better than the state-of-the-art two-way ranging (TWR) systems. We further developed a temporal tracking system that achieves a tracking accuracy of 10 cm in mobile conditions which is 4.3× better than the state-of-the-art TWR systems.

SSLIDE: Sound source localization for indoors based on deep learning
Yifan Wu, Roshan Ayyalasomayajula, Michael J. Bianco, Dinesh Bharadia, and Peter Gerstoft
IEEE
This paper presents SSLIDE, Sound Source Localization for Indoors using DEep learning, which applies deep neural networks (DNNs) with encoder-decoder structure to localize sound sources with random positions in a continuous space. The spatial features of sound signals received by each microphone are extracted and represented as likelihood surfaces for the sound source locations in each point. Our DNN consists of an encoder network followed by two decoders. The encoder obtains a compressed representation of the input likelihoods. One decoder resolves the multipath caused by reverberation, and the other decoder estimates the source location. Experiments based on both the simulated and experimental data show that our method can not only outperform multiple signal classification (MUSIC), steered response power with phase transform (SRP-PHAT), sparse Bayesian learning (SBL), and a competing convolutional neural network (CNN) approach in the reverberant environment but also achieve a good generalization performance.

Deep learning based wireless localization for indoor navigation
Roshan Ayyalasomayajula, Aditya Arun, Chenfeng Wu, Sanatan Sharma, Abhishek Rajkumar Sethi, Deepak Vasisht, and Dinesh Bharadia
ACM
Location services, fundamentally, rely on two components: a mapping system and a positioning system. The mapping system provides the physical map of the space, and the positioning system identifies the position within the map. Outdoor location services have thrived over the last couple of decades because of well-established platforms for both these components (e.g. Google Maps for mapping, and GPS for positioning). In contrast, indoor location services haven't caught up because of the lack of reliable mapping and positioning frameworks. Wi-Fi positioning lacks maps and is also prone to environmental errors. In this paper, we present DLoc, a Deep Learning based wireless localization algorithm that can overcome traditional limitations of RF-based localization approaches (like multipath, occlusions, etc.). We augment DLoc with an automated mapping platform, MapFind. MapFind constructs location-tagged maps of the environment and generates training data for DLoc. Together, they allow off-the-shelf Wi-Fi devices like smartphones to access a map of the environment and to estimate their position with respect to that map. During our evaluation, MapFind has collected location estimates of over 105 thousand points under 8 different scenarios with varying furniture positions and people motion across two different spaces covering 2000 sq. Ft. DLoc outperforms state-of-the-art methods in Wi-Fi-based localization by 80% (median & 90th percentile) across the two different spaces.

LocAP: Autonomous millimeter accurate mapping of WiFi infrastructure

BLoc: CSI-based accurate localization for BLE tags
Roshan Ayyalasomayajula, Deepak Vasisht, and Dinesh Bharadia
ACM
2. Les TAC i les NEE a l’Educació Infantil i Primària Curs presencial de 36 hores Analitzar els problemes d’aprenentatge i les aportacions que poden fer-hi les eines TIC. Revisar els casos més freqüents de discapacitats i determinar les necessitats d’adaptació de l’entorn informàtic. Presentar materials específics per a necessitats educatives especials. Presentar els recursos que ofereixen les diverses entitats que desenvolupen accions en aquest àmbit. Conèixer els serveis bàsics de la xarxa Internet: navegació i correu electrònic

Differentiating photographic and PRCG images using tampering localization features
Roshan Sai Ayyalasomayajula and Vinod Pankajakshan
Springer Singapore

RECENT SCHOLAR PUBLICATIONS

WiSenseHub: Architecture to deploy a building-scale WiFi-sensing system
P Mundra, Z Huang, W Hunter, A Arun, D Khadela, P Sinha, ...
Proceedings of the 30th Annual International Conference on Mobile Computing 2024

DOLOS: Tricking the Wi-Fi APs with Incorrect User Locations
A Arun, V Anand, W Sun, R Ayyalasomayajula, D Bharadia
arXiv preprint arXiv:2407.16138 2024

WAIS: Leveraging WiFi for Resource-Efficient SLAM
A Arun, W Hunter, R Ayyalasomayajula, D Bharadia
Proceedings of the 22nd Annual International Conference on Mobile Systems 2024

Ultra-wideband localization
M Zhao, SR Ayyalasomayajula, C Zhang, D Bharadia, A Arun, T Chang
US Patent App. 18/257,568 2024

Users are closer than they appear: Protecting user location from WiFi APs
R Ayyalasomayajula, A Arun, W Sun, D Bharadia
Proceedings of the 24th International Workshop on Mobile Computing Systems 2023

ML Based Wireless Sensing Systems for Robotics and IoT Applications
SR Ayyalasomayajula
UC San Diego 2023

ViWiD: Leveraging WiFi for Robust and Resource-Efficient SLAM
A Arun, W Hunter, R Ayyalasomayajula, D Bharadia
arXiv preprint arXiv:2209.08091 2022

Real-time low-latency tracking for UWB tags
A Arun, T Chang, Y Yu, R Ayyalasomayajula, D Bharadia
Proceedings of the 20th Annual International Conference on Mobile Systems 2022

Wireless device localization
SR Ayyalasomayajula, D Bharadia, S Ganesaraman, I Jain, ...
US Patent App. 17/604,380 2022

P2SLAM: Bearing based WiFi SLAM for indoor robots
A Arun, R Ayyalasomayajula, W Hunter, D Bharadia
IEEE Robotics and Automation Letters 7 (2), 3326-3333 2022

Sound source localization based on multi-task learning and image translation network
Y Wu, R Ayyalasomayajula, MJ Bianco, D Bharadia, P Gerstoft
The Journal of the Acoustical Society of America 150 (5), 3374-3386 2021

Location determination of wireless communications devices
SR Ayyalasomayajula, D Bharadia, D Vasisht, D Katabi
US Patent 11,140,651 2021

Uloc: Low-power, scalable and cm-accurate uwb-tag localization and tracking for indoor applications
M Zhao, T Chang, A Arun, R Ayyalasomayajula, C Zhang, D Bharadia
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous 2021

Sslide: Sound source localization for indoors based on deep learning
Y Wu, R Ayyalasomayajula, MJ Bianco, D Bharadia, P Gerstoft
ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and 2021

Deep learning based wireless localization for indoor navigation
R Ayyalasomayajula, A Arun, C Wu, S Sharma, AR Sethi, D Vasisht, ...
Proceedings of the 26th Annual International Conference on Mobile Computing 2020

LocAP: Autonomous millimeter accurate mapping of WiFi infrastructure
R Ayyalasomayajula, A Arun, C Wu, S Rajagopalan, S Ganesaraman, ...
17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2020

BLoc: CSI-based accurate localization for BLE tags
R Ayyalasomayajula, D Vasisht, D Bharadia
Proceedings of the 14th International Conference on emerging Networking 2018

Differentiating photographic and PRCG images using tampering localization features
RS Ayyalasomayajula, V Pankajakshan
Proceedings of International Conference on Computer Vision and Image 2017

Towards CSI enabled Closed-loop WiFi based SLAM
A Arun, C Wu, R Ayyalasomayajula, I Jain, D Bharadia

MOST CITED SCHOLAR PUBLICATIONS

BLoc: CSI-based accurate localization for BLE tags
R Ayyalasomayajula, D Vasisht, D Bharadia
Proceedings of the 14th International Conference on emerging Networking 2018
Citations: 111

P2SLAM: Bearing based WiFi SLAM for indoor robots
A Arun, R Ayyalasomayajula, W Hunter, D Bharadia
IEEE Robotics and Automation Letters 7 (2), 3326-3333 2022
Citations: 44

Users are closer than they appear: Protecting user location from WiFi APs
R Ayyalasomayajula, A Arun, W Sun, D Bharadia
Proceedings of the 24th International Workshop on Mobile Computing Systems 2023
Citations: 18

Location determination of wireless communications devices
SR Ayyalasomayajula, D Bharadia, D Vasisht, D Katabi
US Patent 11,140,651 2021
Citations: 4

Differentiating photographic and PRCG images using tampering localization features
RS Ayyalasomayajula, V Pankajakshan
Proceedings of International Conference on Computer Vision and Image 2017
Citations: 3

WAIS: Leveraging WiFi for Resource-Efficient SLAM
A Arun, W Hunter, R Ayyalasomayajula, D Bharadia
Proceedings of the 22nd Annual International Conference on Mobile Systems 2024
Citations: 2

Real-time low-latency tracking for UWB tags
A Arun, T Chang, Y Yu, R Ayyalasomayajula, D Bharadia
Proceedings of the 20th Annual International Conference on Mobile Systems 2022
Citations: 2

Wireless device localization
SR Ayyalasomayajula, D Bharadia, S Ganesaraman, I Jain, ...
US Patent App. 17/604,380 2022
Citations: 1