Our lab has focused on vehicular sensing systems, mobile computing, and networked sensing. The following sections list some papers in each of these topics.
Vehicular sensing systems
MobiSys
AutoCast: Scalable Infrastructure-Less Cooperative Perception for Distributed Collaborative Driving
Hang Qiu, Po-Han Huang, Namo Asavisanu, and 3 more authors
In Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services 2022
Autonomous vehicles use 3D sensors for perception. Cooperative perception enables vehicles to share sensor readings with each other to improve safety. Prior work in cooperative perception scales poorly even with infrastructure support. AUTOCAST1 enables scalable infrastructure-less cooperative perception using direct vehicle-to-vehicle communication. It carefully determines which objects to share based on positional relationships between traffic participants, and the time evolution of their trajectories. It coordinates vehicles and optimally schedules transmissions in a distributed fashion. Extensive evaluation results under different scenarios show that, unlike competing approaches, AUTOCAST can avoid crashes and near-misses which occur frequently without cooperative perception, its performance scales gracefully in dense traffic scenarios providing 2-4x visibility into safety critical objects compared to existing cooperative perception schemes, its transmission schedules can be completed on the real radio testbed, and its scheduling algorithm is near-optimal with negligible computation overhead.
NSDI
CarMap-Fast 3D Feature Map Updates for Automobiles
Fawad Ahmad, Hang Qiu, Ray Eells, and 2 more authors
In 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 20) 2020
QuickSketch: Building 3D Representations in Unknown Environments Using Crowdsourcing
Fawad Ahmad, Hang Qiu, Xiaochen Liu, and 2 more authors
In 2018 21st International Conference on Information Fusion (FUSION) 2018
IoTDI
Kestrel: Video Analytics for Augmented Multi-Camera Vehicle Tracking
H Qiu, X Liu, S Rallapalli, and 5 more authors
In 2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI) 2018
TVT
Towards Robust Vehicular Context Sensing
H Qiu, J Chen, S Jain, and 7 more authors
IEEE Transactions on Vehicular Technology 2018
MobiSys
AVR: Augmented Vehicular Reality
Hang Qiu, Fawad Ahmad, Fan Bai, and 2 more authors
In Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services (Mobisys) 2018
Augmented Vehicular Reality: Enabling Extended Vision for Future Vehicles
Hang Qiu, Fawad Ahmad, Ramesh Govindan, and 3 more authors
In Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications, HotMobile 2017, Sonoma, CA, USA, February 21 - 22, 2017 2017
SEC
Real-time Traffic Estimation at Vehicular Edge Nodes
Gorkem Kar, Shubham Jain, Marco Gruteser, and 2 more authors
In Proceedings of the Second ACM/IEEE Symposium on Edge Computing, San Jose / Silicon Valley, SEC 2017, CA, USA, October 12-14, 2017 2017
SEC
Pre-DriveID: Pre-trip Driver Identification from In-vehicle Data
Gorkem Kar, Shubham Jain, Marco Gruteser, and 3 more authors
In Proceedings of the Second ACM/IEEE Symposium on Edge Computing, San Jose / Silicon Valley, SEC 2017, CA, USA, October 12-14, 2017 2017
TVT
Towards Robust Vehicular Context Sensing
Hang Qiu, Jinzhu Chen, Shubham Jain, and 7 more authors
IEEE Transactions on Vehicular Technology 2017
SenSys
CARLOC: Precisely Tracking Automobile Position
Yurong Jiang, Hang Qiu, Matthew McCartney, and 5 more authors
In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems (SenSys) no 2015
Mobile computing
SoCC
Scrooge: A Cost-Effective Deep Learning Inference System
Yitao Hu, Rajrup Ghosh, and Ramesh Govindan
In SoCC ’21: ACM Symposium on Cloud Computing, Seattle, WA, USA, November 1 - 4, 2021 2021
IoTDI
Rim: Offloading Inference to the Edge
Yitao Hu, Weiwu Pang, Xiaochen Liu, and 4 more authors
In Proceedings of the 6th ACM/IEEE Conference on Internet of Things Design and Implementation, 2021 2021
Middleware
Olympian: Scheduling GPU Usage in a Deep Neural Network Model Serving System
Yitao Hu, Swati Rallapalli, Bongjun Ko, and 1 more author
In Proceedings of the 19th International Middleware Conference 2018
MobiSys
Gnome: A Practical Approach to NLOS Mitigation for GPS Positioning in Smartphones
Xiaochen Liu, Suman Nath, and Ramesh Govindan
In Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services (Mobisys) 2018
Ubicomp
ALPS: Accurate Landmark Positioning at City Scales
Yitao Hu, Xiaochen Liu, Suman Nath, and 1 more author
In the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016) se 2016
MobiSys
Efficient Privilege De-Escalation for Ad Libraries in Mobile Apps
Bin Liu, Bin Liu, Hongxia Jin, and 1 more author
In The 13th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys 2015) ma 2015
MobiSys
FlexiWeb: Network-Aware Compaction for Accelerating Mobile Web Transfers
S. Singh, H. Madhyastha, S. Krishnamurthy, and 1 more author
In Proc. ACM MobiCom ma 2015
Networked sensing
ToSN
Sensing the Sensor: Estimating Camera Properties with Minimal Information
Pradipta Ghosh, Xiaochen Liu, Hang Qiu, and 3 more authors
Public outdoor surveillance cameras often have limited metadata describing their properties. Frequently, a public camera’s precise position, orientation, focal length, and image center are unknown; these attributes are necessary to precisely pinpoint the location of events seen in the camera. In this article, we ask: what is the minimal information needed to accurately estimate these properties for public cameras? We show, using a judicious combination of projective geometry, neural networks, and crowd-sourced annotations from human workers, that it is possible to, for example, localize 95% of the cameras in our test data set to within 12 m using a single image taken from the camera. This performance is an order of magnitude better than PoseNet, a state-of-the-art neural network that needs significantly more information than our approach, and can only estimate position and orientation (and not other properties). Finally, we show that the camera’s inferred pose and properties can help design a number of virtual sensors, all of which have good accuracy.
TMC
Synthesis of Large-Scale Instant IoT Networks
Pradipta Ghosh, Jonathan Bunton, Dimitrios Pylorof, and 6 more authors
While most networks have long lifetimes, temporary network infrastructure is often useful for special events, pop-up retail, or disaster response. An instant IoT network is one that is rapidly constructed, used for a few days, then dismantled. We consider the synthesis of instant IoT networks in urban settings. This synthesis problem must satisfy complex and competing constraints: sensor coverage, line-of-sight visibility, and network connectivity. The central challenge in our synthesis problem is quickly scaling to large regions while producing cost-effective solutions. We explore two qualitatively different representations of the synthesis problems using satisfiability modulo convex optimization (SMC), and mixed-integer linear programming (MILP). The former is more expressive, for our problem, than the latter, but is less well-suited for solving optimization problems like ours. We show how to express our network synthesis in these frameworks. To scale to problem sizes beyond what these frameworks are capable of, we develop a hierarchical synthesis technique that independently synthesizes networks in sub-regions of the deployment area, then combines these. We find that, while MILP outperforms SMC in some settings for smaller problem sizes, the fact that SMC’s expressivity matches our problem ensures that it uniformly generates better quality solutions at larger problem sizes.
SoCC
Scrooge: A Cost-Effective Deep Learning Inference System
Yitao Hu, Rajrup Ghosh, and Ramesh Govindan
In SoCC ’21: ACM Symposium on Cloud Computing, Seattle, WA, USA, November 1 - 4, 2021 Feb 2021
IoTDI
Rim: Offloading Inference to the Edge
Yitao Hu, Weiwu Pang, Xiaochen Liu, and 4 more authors
In Proceedings of the 6th ACM/IEEE Conference on Internet of Things Design and Implementation, 2021 Feb 2021
IoTJ
New Frontiers in IoT: Networking, Systems, Reliability, and Security Challenges
Saurabh Bagchi, Tarek F Abdelzaher, Ramesh Govindan, and 4 more authors
IEEE Internet of Things Journal Feb 2020
IROS
Persistent Connected Power Constrained Surveillance with Unmanned Aerial Vehicles
Pradipta Ghosh, Paulo Tabuada, Ramesh Govindan, and 1 more author
In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Feb 2020
Grab: Fast and Accurate Sensor Processing for Cashier-Free Shopping
Xiaochen Liu, Yurong Jiang, Kyu-Han Kim, and 1 more author
In Feb 2020
Rapid Top-Down Synthesis of Large-Scale IoT Networks
Pradipta Ghosh, Jonathan Bunton, Dimitrios Pylorof, and 6 more authors
In Proceedings of the IEEE International Conference on Computer Communications and Networks (ICCCN) Feb 2020
Sensys
Caesar: Cross-camera Complex Activity Recognition
Xiaochen Liu, Pradipta Ghosh, Oytun Ulutan, and 3 more authors
Feb 2019
IoTDI
Kestrel: Video Analytics for Augmented Multi-Camera Vehicle Tracking
H Qiu, X Liu, S Rallapalli, and 5 more authors
In 2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI) Feb 2018
ToN
Scalability and Satisfiability of Quality-of-Information in Wireless Networks
Scott T Rager, Ertugrul N Ciftcioglu, Ram Ramanathan, and 2 more authors
