Romit Roy Choudhury

University of Illinois, Urbana-Champaign, Urbana, Illinois, United States

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Publications (120)32.67 Total impact

  • ACM SIGMOBILE Mobile Computing and Communications Review 01/2015; 18(4):24-31. DOI:10.1145/2721914.2721923
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    ABSTRACT: This paper revisits the randomized backoff problem in CSMA networks and identifies opportunities of improvement. The key observation is that today's backoff operation, such as in WiFi, attempts to create a total ordering among all nodes contending for the channel. Total ordering indeed assigns a unique backoff to each node (thus avoiding collisions), but pays the penalty of choosing the random back-offs from a large range, ultimately translating to channel wastage. We envision breaking away from total ordering. Briefly, we force nodes to pick random numbers from a smaller range, so that groups of nodes pick the same random number (i.e., partial order). Now, the group that picks the smallest number - the winners - is advanced to a second round, where they again perform the same operation. We show that narrowing down the contenders through multiple rounds improves channel utilization. The intuition is that time for partially ordering all nodes plus totally ordering each small group is actually less than the time needed to totally order all nodes. We instantiate the idea with two well known CSMA protocols - WiFi and oCSMA. We resolve new challenges regarding multi domain contentions and group signaling. USRP and simulation based microbenchmarks are promising. We believe the idea of "hierarchical backoff" applies to other CSMA systems as well, exploration of which is left to future work.
  • Puneet Jain, Romit Roy Choudhury
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    ABSTRACT: This video presents a demo of indoor localization in multiple settings. In the demo, a user walks with a smartphone and the user's location is shown on the phone's screen in real time. Our system, called Unsupervised Indoor Localization (UnLoc) utilizes the sensor data from smartphones to learn "invisible landmarks" in the environment. Example landmarks could be a unique magnetic fluctuation experienced when the phone is near a water-cooler, or a distinct gyroscope rotation when the user turns a corner. We use these indoor "landmarks" to periodically reset the user's location. To track the user between these landmarks, we use an optimized variant of dead reckoning, ultimately leading to a robust location tracking system. We call our system UnLoc, since the landmarks are generated in an unsupervised manner, requiring no manual effort or floorplan of the building. The demo describes the high level intuitions, shows UnLoc in operation, and shares experiences from running UnLoc in various real-world environments.
  • Source
    Nirupam Roy, He Wang, Romit Roy Choudhury
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    ABSTRACT: We present a demonstration of WalkCompass, a system to appear in the MobiSys 2014 main conference. WalkCompass exploits smartphone sensors to estimate the direction in which a user is walking. We find that several smartphone localization systems in the recent past, including our own, make a simplifying assumption that the user's walking direction is known. In trying to relax this assumption, we were not able to find a generic solution from past work. While intuition suggests that the walking direction should be detectable through the accelerometer, in reality this direction gets blended into various other motion patterns during the act of walking, including up and down bounce, side-to-side sway, swing of arms or legs, etc. WalkCompass analyzes the human walking dynamics to estimate the dominating forces and uses this knowledge to find the heading direction of the pedestrian. In the demonstration we will show the performance of this system when the user holds the smartphone on the palm. A collection of YouTube videos of the demo is posted at localization/walkcompass.
  • Nirupam Roy, He Wang, Romit Roy Choudhury
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    ABSTRACT: This paper describes WalkCompass, a system that exploits smartphone sensors to estimate the direction in which a user is walking. We find that several smartphone localization systems in the recent past, including our own, make a simplifying assumption that the user's walking direction is known. In trying to relax this assumption, we were not able to find a generic solution from past work. While intuition suggests that the walking direction should be detectable through the accelerometer, in reality this direction gets blended into various other motion patterns during the act of walking, including up and down bounce, side-to-side sway, swing of arms or legs, etc. Moreover, the walking direction is in the phone's local coordinate system (e.g., along Y axis), and translation to global directions, such as 45 degree North, can be challenging when the compass is itself erroneous. WalkCompass copes with these challenges and develops a stable technique to estimate the user's walking direction within a few steps. Results drawn from 15 different environments demonstrate median error of less than 8 degrees, across 6 different users, 3 surfaces, and 3 holding positions. While there is room for improvement, we believe our current system can be immediately useful to various applications centered around localization and human activity recognition.
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    ABSTRACT: We present eNav, a smartphone-based vehicular GPS navigation system that has an energy-saving location sensing mode capable of drastically reducing navigation energy needs. Traditional implementations sample the phone GPS at the highest possible rate (usually 1Hz) to ensure constant highest possible localization accuracy. This practice results in excessive phone battery consumption and reduces the attainable length of a navigation session. The seemingly most common solution would be to always use a car-charger and keep the phone plugged-in during navigation at all times. However, according to a comprehensive survey we conducted, only a small percent of people would actually always carry around their phones' car-chargers and cables, as doing so is inconvenient and defeats the true ''wireless'' nature of mobile phones. In addressing this problem, eNav exploits the phone's lower-energy on-board motion sensors for approximate location sensing when the vehicle is sufficiently far from the next navigation waypoint, using actual GPS sampling only when close. Our user study shows that, while remaining virtually transparent to users, eNav can reduce navigation energy consumption by over 80% without compromising navigation quality or user experience.
    Proceedings of the 13th international symposium on Information processing in sensor networks; 04/2014
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    ABSTRACT: A variety of techniques have been used by prior work on the problem of smartphone location. In this paper, we propose a novel approach using sound source localization (SSL) with microphone arrays to determine where in a room a smartphone is located. In our system called Daredevil, smartphones emit sound at particular times and frequencies, which are received by microphone arrays. Using SSL that we modified for our purposes, we can calculate the angle between the center of each microphone array and the phone, and thereby triangulate the phone’s position. In this early work, we demonstrate the feasibility of our approach and present initial results. Daredevil can locate smartphones in a room with an average precision of 3.19 feet. We identify a number of challenges in realizing the system in large deployments, and we hope this work will benefit researchers who pursue such techniques.
    ACM Mobile Computing and Communications Review (MC2R); 04/2014
  • Conference Paper: Injecting life into toys
    Songchun Fan, Hyojeong Shin, Romit Roy Choudhury
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    ABSTRACT: This paper envisions a future in which smartphones can be inserted into toys, such as a teddy bear, to make them interactive to children. Our idea is to leverage the smartphones' sensors to sense children's gestures, cues, and reactions, and interact back through acoustics, vibration, and when possible, the smartphone display. This paper is an attempt to explore this vision, ponder on applications, and take the first steps towards addressing some of the challenges. Our limited measurements from actual kids indicate that each child is quite unique in his/her "gesture vocabulary", motivating the need for personalized models. To learn these models, we employ signal processing-based approaches that first identify the presence of a gesture in a phone's sensor stream, and then learn its patterns for reliable classification. Our approach does not require manual supervision (i.e., the child is not asked to make any specific gesture); the phone detects and learns through observation and feedback. Our prototype, while far from a complete system, exhibits promise -- we now believe that an unsupervised sensing approach can enable new kinds of child-toy interactions.
    Proceedings of the 15th Workshop on Mobile Computing Systems and Applications; 02/2014
  • Network and Distributed System Security Symposium; 01/2014
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    ABSTRACT: Mobile phones are becoming the convergent platform for personal sensing, computing, and communication. This paper attempts to exploit this convergence toward the problem of automatic image tagging. We envision TagSense, a mobile phone-based collaborative system that senses the people, activity, and context in a picture, and merges them carefully to create tags on-the-fly. The main challenge pertains to discriminating phone users that are in the picture from those that are not. We deploy a prototype of TagSense on eight Android phones, and demonstrate its effectiveness through 200 pictures, taken in various social settings. While research in face recognition continues to improve image tagging, TagSense is an attempt to embrace additional dimensions of sensing toward this end goal. Performance comparison with Apple iPhoto and Google Picasa shows that such an out-of-band approach is valuable, especially with increasing device density and greater sophistication in sensing and learning algorithms.
    IEEE Transactions on Mobile Computing 01/2014; 13(1):61-74. DOI:10.1109/TMC.2012.235 · 2.91 Impact Factor
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    ABSTRACT: We intend to develop a smartphone app that can tell whether its user is a driver or a passenger in an automobile. While the core problem can be solved relatively easily with special installations in new high-end vehicles (e.g., NFC), constraints of backward compatibility makes the problem far more challenging. We design a Driver Detection System (DDS) that relies entirely on smartphone sensors, and is thereby compatible with all automobiles. Our approach harnesses smartphone sensors to recognize micro-activities in humans, that in turn discriminate between the driver and the passenger. We demonstrate an early prototype of this system on Android NexusS and Apple iPhones. Reported results show greater than 85% accuracy across 6 users in 2 different cars.
    2014 Sixth International Conference on Communication Systems and Networks (COMSNETS); 01/2014
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    ABSTRACT: This issue of MC2R features the posters and demonstrations submitted to ACM HotMobile 2013.
    ACM SIGMOBILE Mobile Computing and Communications Review 11/2013; 17(3):19-20. DOI:10.1145/2542095.2542106
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    ABSTRACT: This paper describes a system for automatically rating content - mainly movies and videos - at multiple granularities. Our key observation is that the rich set of sensors available on today's smartphones and tablets could be used to capture a wide spectrum of user reactions while users are watching movies on these devices. Examples range from acoustic signatures of laughter to detect which scenes were funny, to the stillness of the tablet indicating intense drama. Moreover, unlike in most conventional systems, these ratings need not result in just one numeric score, but could be expanded to capture the user's experience. We combine these ideas into an Android based prototype called Pulse, and test it with 11 users each of whom watched 4 to 6 movies on Samsung tablets. Encouraging results show consistent correlation between the user's actual ratings and those generated by the system. With more rigorous testing and optimization, Pulse could be a candidate for real-world adoption.
    Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing; 09/2013
  • Xuan Bao, Yin Lin, Uichin Lee, Ivica Rimac, Romit Roy Choudhury
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    ABSTRACT: The proliferation of pictures and videos in the Internet is imposing heavy demands on mobile data networks. Though emerging wireless technologies will provide more bandwidth, the increase in demand will easily consume the additional capacity. To alleviate this problem, we explore the possibility of serving user requests from other mobile devices located geographically close to the user. For instance, when Alice reaches areas with high device density – Data Spots – the cellular operator learns Alice’s content request, and guides her device to nearby devices that have the requested content. Importantly, communication between the nearby devices can be mediated by servers, avoiding many of the known problems of pure ad hoc communication. This paper argues this viability through systematic prototyping, measurements, and measurement-driven analysis.
    IEEE INFOCOM 2013; 04/2013
  • Xuan Bao, Mahanth Gowda, Ratul Mahajan, Romit Roy Choudhury
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    ABSTRACT: This paper envisions a new research direction that we call psychological computing. The key observation is that, even though computing systems are missioned to satisfy human needs, there has been little attempt to bring understandings of human need/psychology into core system design. This paper makes the case that percolating psychological insights deeper into the computing layers is valuable, even essential. Through examples from content caching, vehicular systems, and network scheduling, we argue that psychological awareness can not only offer performance gains to known technological problems, but also spawn new kinds of systems that are difficult to conceive otherwise.
    Proceedings of the 14th Workshop on Mobile Computing Systems and Applications; 02/2013
  • He Wang, Xuan Bao, Romit Roy Choudhury, Srihari Nelakuditi
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    ABSTRACT: Wearable cameras and displays, such as the Google Glass, are around the corner. This paper explores techniques that jointly leverage camera-enabled glasses and smartphones to recognize individuals in the visual surrounding. While face recognition would be one approach to this problem, we believe that it may not be always possible to see a person's face. Our technique is complementary to face recognition, and exploits the intuition that colors of clothes, decorations, and even human motion patterns, can together make up a "fingerprint". When leveraged systematically, it may be feasible to recognize individuals with reasonable consistency. This paper reports on our attempts, with early results from a prototype built on Android Galaxy phones and PivotHead's camera-enabled glasses. We call our system InSight.
    Proceedings of the 14th Workshop on Mobile Computing Systems and Applications; 02/2013
  • S. Sen, N. Santhapuri, R.R. Choudhury, S. Nelakuditi
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    ABSTRACT: Successive interference cancellation (SIC) is a PHY capability that allows a receiver to decode packets that arrive simultaneously. While the technique is well known in communications literature, emerging software radio platforms are making practical experimentation feasible. This motivates us to study the extent of throughput gains possible with SIC from a MAC layer perspective and scenarios where such gains are worth pursuing. We find that contrary to our initial expectation, the gains are not high when the bits of interfering signals are not known a priori to the receiver. Moreover, we observe that the scope for SIC gets squeezed by the advances in bitrate adaptation. In particular, our analysis shows that interfering one-to-one transmissions benefit less from SIC than scenarios with many-to-one transmissions (such as when clients upload data to a common access point). In view of this, we develop an SIC-aware scheduling algorithm that employs client pairing and power reduction to extract the most gains from SIC. We believe that our findings will be useful guidelines for moving forward with SIC-aware protocol research.
    IEEE Transactions on Mobile Computing 02/2013; 12(2):346-357. DOI:10.1109/TMC.2012.17 · 2.91 Impact Factor
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    ABSTRACT: Cooperative packet recovery has been widely investigated in wireless networks, where corrupt copies of a packet are combined to recover the original packet. While previous work such as MRD (Multi Radio Diversity) and Soft apply combining to bits and bit-confidences, combining at the symbol level has been avoided. The reason is rooted in the prohibitive overhead of sharing raw symbol information between different APs of an enterprise WLAN. We present Epicenter that overcomes this constraint, and combines multiple copies of incorrectly received “symbols” to infer the actual transmitted symbol. Our core finding is that symbols need not be represented in full fidelity - coarse representation of symbols can preserve most of their diversity, while substantially lowering the overhead. We then develop a rate estimation algorithm that actually exploits symbol level combining. Our USRP/GNURadio testbed confirms the viability of our ideas, yielding 40% throughput gain over Soft, and 25-90% over 802.11. While the gains are modest, we believe that they are realistic, and available with minimal modifications to today's EWLAN systems.
    INFOCOM, 2013 Proceedings IEEE; 01/2013
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    ABSTRACT: Today's smartphones provide a variety of sensors, enabling high-resolution measurements of user behavior. We envision that many services can benefit from short-term predictions of complex human behavioral patterns. While enablement of behavior awareness through sensing is a broad research theme, one possibility is in predicting how quickly a person will move through a space. Such a prediction service could have numerous applications. For one example, we imagine shop owners predicting how long a particular customer is likely to browse merchandise, and issue targeted mobile coupons accordingly - customers in a hurry can be encouraged to stay and consider discounts. Within a space of moderate size, WiFi access points are uniquely positioned to track a statistical framework for user length of stay, passively recording metrics such as WiFI signal strength (RSSI) and potentially receiving client-uploaded sensor data. In this work, we attempt to quantity this opportunity, and show that human dwell time can be predicted with reasonable accuracy, even when restricted to passively observed WiFi RSSI.
    INFOCOM, 2013 Proceedings IEEE; 01/2013

Publication Stats

3k Citations
32.67 Total Impact Points


  • 2002–2014
    • University of Illinois, Urbana-Champaign
      • • Department of Computer Science
      • • Coordinated Science Laboratory
      Urbana, Illinois, United States
  • 2008–2013
    • Duke University
      • • Department of Computer Science
      • • Department of Electrical and Computer Engineering (ECE)
      Durham, North Carolina, United States
  • 2010
    • Institute of Geophysics, China Earthquake Administration
      Peping, Beijing, China
  • 2000
    • Haldia Institute of Technology
      Kolkata, Bengal, India