Roshan Ayyalasomayajula

2023

1. 

   Users are Closer than they Appear: Protecting User Location from WiFi APs

   Roshan Ayyalasomayajula, Aditya Arun, Wei Sun, and Dinesh Bharadia

   In Proceedings of the 24th International Workshop on Mobile Computing Systems and Applications, 2023

   Abstract Bib PDF Slides Website

   Wi-Fi-based indoor localization has now matured for over a decade. Most of the current localization algorithms rely on the Wi-Fi access points (APs) in the enterprise network to localize the Wi-Fi user accurately. Thus, the Wi-Fi user’s location information could be easily snooped by an attacker listening through a compromised Wi-Fi 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 Wi-Fi user’s location information. MIRAGE achieves this by utilizing the beamforming capability of the transmitter that is already part of the Wi-Fi standard protocols. With this initial idea, we have demonstrated that the user can obfuscate his/her location from the Wi-Fi AP always with no compromise to the throughput of the existing Wi-Fi 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.

   @inproceedings{ayyalasomayajula2023users,
     title = {Users are Closer than they Appear: Protecting User Location from WiFi APs},
     author = {Ayyalasomayajula, Roshan and Arun, Aditya and Sun, Wei and Bharadia, Dinesh},
     booktitle = {Proceedings of the 24th International Workshop on Mobile Computing Systems and Applications},
     pages = {124--130},
     year = {2023},
     url = {https://dl.acm.org/doi/pdf/10.1145/3572864.3580345},
   }

2022

1. 

   P2slam: Bearing based wifi slam for indoor robots

   Aditya Arun, Roshan Ayyalasomayajula, William Hunter, and Dinesh Bharadia

   IEEE Robotics and Automation Letters, 2022

   Abstract Bib PDF Website

   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 LiDAR’s, employed for fusion are capable of correcting the drifts accumulated by wheel odometry or inertial measurement units (IMU’s). However, these exteroceptive sensors are deficient in highly structured environments and dynamic lighting conditions. This letter 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 25×30 m and 50×40 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 6× compared odometry-only estimation and perform on par with one of the state-of-the-art Visual-based SLAM.

   @article{arun2022p2slam,
     title = {P2slam: Bearing based wifi slam for indoor robots},
     author = {Arun, Aditya and Ayyalasomayajula, Roshan and Hunter, William and Bharadia, Dinesh},
     journal = {IEEE Robotics and Automation Letters},
     volume = {7},
     number = {2},
     pages = {3326--3333},
     year = {2022},
     publisher = {IEEE},
     url = {https://ieeexplore.ieee.org/abstract/document/9691786},
   }

2. 

   ViWiD: Leveraging WiFi for Robust and Resource-Efficient SLAM

   Aditya Arun, William Hunter, Roshan Ayyalasomayajula, and Dinesh Bharadia

   arXiv preprint arXiv:2209.08091, 2022

   Abstract Bib PDF Code Website

   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.

   @article{arun2022viwid,
     title = {ViWiD: Leveraging WiFi for Robust and Resource-Efficient SLAM},
     author = {Arun, Aditya and Hunter, William and Ayyalasomayajula, Roshan and Bharadia, Dinesh},
     journal = {arXiv preprint arXiv:2209.08091},
     year = {2022},
     url = {https://arxiv.org/pdf/2209.08091},
   }

2021

1. 

   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

   Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2021

   Abstract Bib PDF Website

   A myriad of IoT applications demand centimeter-accurate localization that is robust to blockages from hands, furniture, or other occlusions in the environment. To address these needs, we developed ULoc, a scalable, low-power, and cm-accurate UWB localization and tracking system. ULoc’s builds a multi-antenna UWB anchor and develops a novel 3D tracking algorithm to deliver a stationary localization accuracy of less than 5 cm and a tracking accuracy of 10 cm in mobile conditions. Follow the demo links below to see ULoc in action. Furthermore, we have also open sourced our hardware design files and source code.

   @article{zhao2021uloc,
     title = {Uloc: Low-power, scalable and cm-accurate uwb-tag localization and tracking for indoor applications},
     author = {Zhao, Minghui and Chang, Tyler and Arun, Aditya and Ayyalasomayajula, Roshan and Zhang, Chi and Bharadia, Dinesh},
     journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
     volume = {5},
     number = {3},
     pages = {1--31},
     year = {2021},
     publisher = {ACM New York, NY, USA},
     url = {https://dl.acm.org/doi/pdf/10.1145/3478124},
   }

2020

1. 

   Deep learning based wireless localization for indoor navigation

   Roshan Ayyalasomayajula, Aditya Arun, Chenfeng Wu, Sanatan Sharma, Abhishek Rajkumar Sethi, Deepak Vasisht, and Dinesh Bharadia

   In Proceedings of the 26th Annual International Conference on Mobile Computing and Networking, 2020

   Abstract Bib PDF Code Slides Website

   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-establishedplatforms 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, as GPS is known not to work indoors. WiFi 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.). DLoc uses data from the mapping platform we developed, MapFind, that can construct location-tagged maps of the environment. Together, they allow off-the-shelf WiFi devices like smartphones toaccess a map of the environment and to estimate their position withrespect to that map. During our evaluation, MapFind has collected location estimates of over 120 thousand points under 10 different scenarios across two different spaces covering 2000 sq. Ft. DLoc outperforms state-of-the-art methods in WiFi-based localizationby 80% (median and 90th percentile) across the 2000 sq. ft. spanningtwo different spaces.

   @inproceedings{ayyalasomayajula2020deep,
     title = {Deep learning based wireless localization for indoor navigation},
     author = {Ayyalasomayajula, Roshan and Arun, Aditya and Wu, Chenfeng and Sharma, Sanatan and Sethi, Abhishek Rajkumar and Vasisht, Deepak and Bharadia, Dinesh},
     booktitle = {Proceedings of the 26th Annual International Conference on Mobile Computing and Networking},
     pages = {1--14},
     year = {2020},
     url = {https://dl.acm.org/doi/pdf/10.1145/3372224.3380894},
   }