publications

2025

1. EnvSys @ MobiSys’25

   Localization using Angle-of-Arrival Triangulation

   Amod K. Agrawal

   In Proceedings of the International Workshop on Environmental Sensing Systems for Smart Cities (EnvSys ’25), co-located with MobiSys 2025, Anaheim, CA, USA, 2025

   Abs DOI Bib

   Indoor localization is a long-standing challenge in mobile computing, with significant implications for enabling location-aware and intelligent applications within smart environments such as homes, offices, and retail spaces. As AI assistants such as Amazon Alexa and Google Nest become increasingly pervasive, microphone-equipped devices are emerging as key components of everyday life and home automation. This paper introduces a passive, infrastructure-light system for localizing human speakers using speech signals captured by two or more spatially distributed smart devices. The proposed approach, GCC+, extends the Generalized Cross-Correlation with Phase Transform (GCC-PHAT) method to estimate the Angle-of-Arrival (AoA) of audio signals at each device and applies robust triangulation techniques to infer the speaker’s two-dimensional position. To further improve temporal resolution and localization accuracy, feature-space expansion and subsample interpolation techniques are employed for precise Time Difference of Arrival (TDoA) estimation. The system operates without requiring hardware modifications, prior calibration, explicit user cooperation, or knowledge of the speaker’s signal content, thereby offering a highly practical solution for real-world deployment. Experimental evaluation in a real-world home environment yields a median AoA estimation error of 2.2 degree and a median localization error of 1.25m, demonstrating the feasibility and effectiveness of audio-based localization for enabling context-aware, privacy-preserving ambient intelligence.

   @inproceedings{agrawal2025aoa,
     author = {Agrawal, Amod K.},
     title = {Localization using Angle-of-Arrival Triangulation},
     booktitle = {Proceedings of the International Workshop on Environmental Sensing Systems for Smart Cities (EnvSys ’25), co-located with MobiSys 2025},
     year = {2025},
     location = {Anaheim, CA, USA},
     pages = {1--6},
     publisher = {ACM},
     address = {New York, NY, USA},
     doi = {10.1145/3742460.3742984},
     isbn = {979-8-4007-1986-8},
     url = {https://doi.org/10.1145/3742460.3742984}
   }

2. Media Article

   Ultra‑Wideband: The Next Innovation in Location‑Based Services

   IoT World Today

   Jun 2025

   Published June 17, 2025; Guest Expert Commentary

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   Ultra-Wideband (UWB) technology is rapidly emerging as a key enabler of high-precision location-based services across industries. This article explores how UWB’s fine-grained spatial accuracy, low latency, and robustness to interference make it ideal for applications such as indoor navigation, asset tracking, and secure device-to-device communication. Unlike traditional wireless technologies like Wi-Fi or Bluetooth, UWB offers centimeter-level accuracy, which opens new possibilities in smart homes, logistics, automotive, and augmented reality. The piece also highlights recent adoption trends, standardization efforts, and future directions for UWB in the broader context of IoT connectivity and context-aware experiences.

   @misc{iotworldtoday2025uwb,
     author = {{IoT World Today}},
     title = {Ultra‑Wideband: The Next Innovation in Location‑Based Services},
     howpublished = {\url{https://www.iotworldtoday.com/connectivity/ultra-wideband-the-next-innovation-in-location-based-services}},
     month = jun,
     year = {2025},
     note = {Published June 17, 2025; Guest Expert Commentary}
   }

2024

1. U.S. Patent

   Methods for automatic device grouping from wireless signals

   Amod Agrawal, Maurizio Bocca, Md Fazlay Rabbi Masum Billah, and 2 more authors

   , United States Patent and Trademark Office, Jun 2024

   US Patent App. 18/900,343, Pending

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   This patent application introduces techniques for automatically grouping devices based on observed wireless signals. The method enables context-aware behaviors, co-location detection, and spatial organization without requiring explicit configuration. The system leverages Bluetooth and Wi-Fi signal characteristics, signal strength, and transmission patterns to cluster devices and infer shared physical environments.

   @patent{agrawal2024devicegrouping,
     author = {Agrawal, Amod and Bocca, Maurizio and Billah, Md Fazlay Rabbi Masum and McClain, Sean and VijayaRaghavan, VenkatRaghavan},
     title = {Methods for automatic device grouping from wireless signals},
     year = {2024},
     location = {United States Patent and Trademark Office},
     type = {Patent Application},
     note = {US Patent App. 18/900,343, Pending},
     address = {United States}
   }

2023

1. U.S. Patent

   Methods and apparatus to localize devices in the home

   Amod Agrawal, Chaitanya Desai, Lakshmi Venkatraman, and 3 more authors

   , United States Patent and Trademark Office, Jun 2023

   US Patent App. 18/542,222, Pending

   Abs Bib

   This work describes methods and apparatus to determine the position of devices within a home environment using wireless technologies. By leveraging RF signals and signal processing techniques, the system enables accurate in-home localization for smart devices, enhancing contextual automation, device grouping, and user interaction.

   @patent{agrawal2024localization,
     author = {Agrawal, Amod and Desai, Chaitanya and Venkatraman, Lakshmi and Bocca, Maurizio and Argyropoulos, Paraskevas and Kim, Wontak},
     title = {Methods and apparatus to localize devices in the home},
     year = {2023},
     location = {United States Patent and Trademark Office},
     number = {18/542,222},
     type = {Patent Application},
     note = {US Patent App. 18/542,222, Pending},
     address = {United States}
   }

2. U.S. Patent

   Device localization using WiFi/BLE and audio signals

   Amod Agrawal, Chaitanya Desai, Lakshmi Venkatraman, and 3 more authors

   , United States Patent and Trademark Office, Jun 2023

   US Patent App. 18/542,222, Pending

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   This patent application discloses a hybrid localization technique using WiFi, Bluetooth Low Energy (BLE), and audio signals. By combining RF-based ranging with acoustic signal processing, the system achieves more accurate and robust indoor localization of smart devices, overcoming limitations of single-modality approaches.

   @patent{agrawal2024wifibleaudio,
     author = {Agrawal, Amod and Desai, Chaitanya and Venkatraman, Lakshmi and Bocca, Maurizio and Argyropoulos, Paraskevas and Kim, Wontak},
     title = {Device localization using WiFi/BLE and audio signals},
     year = {2023},
     location = {United States Patent and Trademark Office},
     number = {18/542,222},
     type = {Patent Application},
     note = {US Patent App. 18/542,222, Pending},
     address = {United States}
   }

2019

1. M.S. CS Thesis

   HeadTrack: Tracking Head Orientation Using Wireless Signals

   Amod K. Agrawal, and Romit Roy Choudhury

   University of Illinois at Urbana-Champaign, Jun 2019

   Abs Bib

   Estimating and tracking a user’s head orientation is critical for many mobile computing applications. Existing solutions typically rely on infrastructure-based setups, such as cameras or lasers, combined with expensive inertial measurement units (IMUs). These approaches constrain the user to a fixed area and limit both portability and mobility. This work introduces HeadTrack, a necklace-style wearable consisting of a headset and a chest-piece, which estimates head orientation using wireless radio frequency (RF) signals. The core challenge addressed is to accurately measure multiple distances between the chest-mounted unit and the headset using ultra-wideband (UWB) radios. By decoupling head motion from body motion, this design enables both mobility and portability. Although UWB radios offer 1 GHz bandwidth and high-speed clocks, they typically achieve only  10 cm ranging accuracy. HeadTrack improves this to  5 mm by introducing a wired reference path. The transmitted signal is split and sent over both wired and wireless paths, allowing more precise distance estimation. For evaluation, we use ViCon to collect ground truth data. HeadTrack leverages an onboard IMU to resolve phase ambiguity and achieves head orientation tracking with a mean error of 6.5°. The system offers a wearable, occlusion-free, cost-effective solution for head orientation tracking with bounded and non-diverging error.

   @mastersthesis{agrawal2019headtrack,
     title = {HeadTrack: Tracking Head Orientation Using Wireless Signals},
     author = {Agrawal, Amod K. and Choudhury, Romit Roy},
     school = {University of Illinois at Urbana-Champaign},
     year = {2019},
     url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=FNpPnd0AAAAJ&citation_for_view=FNpPnd0AAAAJ:IjCSPb-OGe4C}
   }

2018

1. EPJ Data Science

   Collective aspects of privacy in the online network

   David Garcia, Mansi Goel, Amod K. Agrawal, and 1 more author

   EPJ Data Science, Jun 2018

   Abs DOI Bib

   Preserving individual control over private information is one of the rising concerns in our digital society. Online social networks exist in application ecosystems that allow them to access data from other services, for example gathering contact lists through mobile phone applications. Such data access might allow social networking sites to create shadow profiles with information about non-users that has been inferred from information shared by the users of the social network. This possibility motivates the shadow profile hypothesis: the data shared by the users of an online service predicts personal information of non-users of the service. We test this hypothesis for the first time on Twitter, constructing a dataset of users that includes profile biographical text, location information, and bidirectional friendship links. We evaluate the predictability of the location of a user by using only information given by friends of the user that joined Twitter before the user did. This way, we audit the historical prediction power of Twitter data for users that had not joined Twitter yet. Our results indicate that information shared by users in Twitter can be predictive of the location of individuals outside Twitter. Furthermore, we observe that the quality of this prediction increases with the tendency of Twitter users to share their mobile phone contacts and is more accurate for individuals with more contacts inside Twitter. We further explore the predictability of biographical information of non-users, finding evidence in line with our results for locations. These findings illustrate that individuals are not in full control of their online privacy and that sharing personal data with a social networking site is a decision that is collectively mediated by the decisions of others.

   @article{garcia2018collective,
     title = {Collective aspects of privacy in the online network},
     author = {Garcia, David and Goel, Mansi and Agrawal, Amod K. and Kumaraguru, Ponnurangam},
     journal = {EPJ Data Science},
     volume = {7},
     number = {1},
     pages = {1--13},
     year = {2018},
     publisher = {SpringerOpen},
     doi = {10.1140/epjds/s13688-018-0145-0}
   }

2015

1. AAAI HCOMP’15

   On optimizing human-machine task assignments

   Andreas Veit, Michael Wilber, Rajan Vaish, and 46 more authors

   arXiv preprint, Jun 2015

   Abs Bib

   When crowdsourcing systems are used in combination with machine inference systems in the real world, they benefit the most when the machine system is deeply integrated with the crowd workers. However, if researchers wish to integrate the crowd with "off-the-shelf" machine classifiers, this deep integration is not always possible. This work explores two strategies to increase accuracy and decrease cost under this setting. First, we show that reordering tasks presented to the human can create a significant accuracy improvement. Further, we show that greedily choosing parameters to maximize machine accuracy is sub-optimal, and joint optimization of the combined system improves performance.

   @article{veit2015optimizing,
     title = {On optimizing human-machine task assignments},
     author = {Veit, Andreas and Wilber, Michael and Vaish, Rajan and Belongie, Serge and Davis, James and Anand, Vishal and Aviral, Anshu and Chakrabarty, Prithvijit and Chandak, Yash and Chaturvedi, Sidharth and Devaraj, Chinmaya and Dhall, Ankit and Dwivedi, Utkarsh and Gupte, Sanket and Sridhar, Sharath N and Paga, Karthik and Pahuja, Anuj and Raisinghani, Aditya and Sharma, Ayush and Sharma, Shweta and Sinha, Darpana and Thakkar, Nisarg and Vignesh, K Bala and Verma, Utkarsh and Abhishek, Kanniganti and Agrawal, Amod K. and Aishwarya, Arya and Bhattacharjee, Aurgho and Dhanasekar, Sarveshwaran and Gullapalli, Venkata Karthik and Gupta, Shuchita and Jain, Kinjal and Kapur, Simran and Kasula, Meghana and Kumar, Shashi and Kundaliya, Parth and Mathur, Utkarsh and Mishra, Alankrit and Mudgal, Aayush and Nadimpalli, Aditya and Nihit, Munakala Sree and Periwal, Akanksha and Sagar, Ayush and Shah, Ayush and Sharma, Vikas and Sharma, Yashovardhan and Siddiqui, Faizal and Singh, Virender and Yadav, Anurag},
     journal = {arXiv preprint},
     volume = {arXiv:1509.07543},
     year = {2015},
     url = {https://arxiv.org/abs/1509.07543}
   }