We have had a long interest in networked sensing, the idea that information from network-connected sensors can be mined to understand environments and behaviors
ToSN
Sensing the Sensor: Estimating Camera Properties with Minimal Information
Ghosh, Pradipta, Liu, Xiaochen, Qiu, Hang, Vieira, Marcos A. M., Sukhatme, Gaurav S., and Govindan, Ramesh
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.
@article{Ghosh22,author={Ghosh, Pradipta and Liu, Xiaochen and Qiu, Hang and Vieira, Marcos A. M. and Sukhatme, Gaurav S. and Govindan, Ramesh},title={Sensing the Sensor: Estimating Camera Properties with Minimal Information},year={2022},issue_date={May 2022},publisher={Association for Computing Machinery},address={New York, NY, USA},volume={18},number={2},issn={1550-4859},url={https://doi.org/10.1145/3508393},doi={10.1145/3508393},journal={ACM Trans. Sen. Netw.},month=feb,articleno={28},numpages={26},topics={sensing},abbr={ToSN},keywords={mechanical turk, Localization, neural network, camera, vanishing point}}
TMC
Synthesis of Large-Scale Instant IoT Networks
Ghosh, Pradipta, Bunton, Jonathan, Pylorof, Dimitrios, Vieira, Marcos A. M., Chan, Kevin,
Govindan, Ramesh, Sukhatme, Gaurav S., Tabuada, Paulo, and Verma, Gunjan
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.
@article{Ghosh2021b,abbr={TMC},title={Synthesis of Large-Scale Instant IoT Networks},author={Ghosh, Pradipta and Bunton, Jonathan and Pylorof, Dimitrios and Vieira, Marcos A. M. and Chan, Kevin and Govindan, Ramesh and Sukhatme, Gaurav S. and Tabuada, Paulo and Verma, Gunjan},doi={10.1109/TMC.2021.3099005},year={2021},date={2021-09-01},journal={IEEE Transactions on Mobile Computing},keywords={},pubstate={published},topics={sensing},tppubtype={article}}
SoCC
Scrooge: A Cost-Effective Deep Learning Inference System
Hu, Yitao, Ghosh, Rajrup, and Govindan, Ramesh
In SoCC ’21: ACM Symposium on Cloud Computing, Seattle, WA, USA, November
1 - 4, 2021 Feb 2021
@inproceedings{Hu2021b,author={Hu, Yitao and Ghosh, Rajrup and Govindan, Ramesh},editor={Curino, Carlo and Koutrika, Georgia and Netravali, Ravi},title={Scrooge: {A} Cost-Effective Deep Learning Inference System},booktitle={SoCC '21: {ACM} Symposium on Cloud Computing, Seattle, WA, USA, November
1 - 4, 2021},pages={624--638},publisher={{ACM}},year={2021},url={https://doi.org/10.1145/3472883.3486993},doi={10.1145/3472883.3486993},timestamp={Sun, 31 Oct 2021 10:05:01 +0100},abbr={SoCC},topics={sensing,mobile},biburl={https://dblp.org/rec/conf/cloud/HuGG21.bib}}
@inproceedings{Hu2021,title={Rim: Offloading Inference to the Edge},author={Hu, Yitao and Pang, Weiwu and Liu, Xiaochen and Ghosh, Rajrup and Ko, Bongjun and Lee, Wei-Han and Govindan, Ramesh},url={https://dl.acm.org/doi/abs/10.1145/3450268.3453521},year={2021},date={2021-05-18},booktitle={Proceedings of the 6th ACM/IEEE Conference on Internet of Things Design and Implementation, 2021},pubstate={published},abbr={IoTDI},topics={sensing,mobile},tppubtype={inproceedings}}
IoTJ
New Frontiers in IoT: Networking, Systems, Reliability, and Security Challenges
Bagchi, Saurabh, Abdelzaher, Tarek F,
Govindan, Ramesh, Shenoy, Prashant, Atrey, Akanksha, Ghosh, Pradipta, and Xu, Ran
@article{bagchi2020new,title={New Frontiers in IoT: Networking, Systems, Reliability, and Security Challenges},author={Bagchi, Saurabh and Abdelzaher, Tarek F and Govindan, Ramesh and Shenoy, Prashant and Atrey, Akanksha and Ghosh, Pradipta and Xu, Ran},url={https://ieeexplore.ieee.org/document/9136673},year={2020},date={2020-01-01},journal={IEEE Internet of Things Journal},keywords={},topics={sensing},pubstate={published},abbr={IoTJ},tppubtype={article}}
IROS
Persistent Connected Power Constrained Surveillance with Unmanned Aerial Vehicles
Ghosh, Pradipta, Tabuada, Paulo,
Govindan, Ramesh, and Sukhatme, Gaurav S
In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Feb 2020
@inproceedings{ghosh2020iros,title={Persistent Connected Power Constrained Surveillance with Unmanned Aerial Vehicles},author={Ghosh, Pradipta and Tabuada, Paulo and Govindan, Ramesh and Sukhatme, Gaurav S},year={2020},date={2020-01-01},booktitle={Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},keywords={},abbr={IROS},pubstate={published},topics={sensing},tppubtype={inproceedings}}
Grab: Fast and Accurate Sensor Processing for Cashier-Free Shopping
Liu, Xiaochen, Jiang, Yurong, Kim, Kyu-Han, and Govindan, Ramesh
@inproceedings{Liu2020,title={Grab: Fast and Accurate Sensor Processing for Cashier-Free Shopping},author={Liu, Xiaochen and Jiang, Yurong and Kim, Kyu-Han and Govindan, Ramesh},url={https://arxiv.org/abs/2001.01033},year={2020},date={2020-01-07},publisher={arXiv preprint arXiv:2001.01033},keywords={},pubstate={published},topics={sensing},tppubtype={inproceedings}}
Rapid Top-Down Synthesis of Large-Scale IoT Networks
Ghosh, Pradipta, Bunton, Jonathan, Pylorof, Dimitrios, Vieira, Marcos, Chan, Kevin,
Govindan, Ramesh, Sukhatme, Gaurav, Tabuada, Paulo, and Verma, Gunjan
In Proceedings of the IEEE International Conference on Computer Communications and Networks (ICCCN) Feb 2020
@inproceedings{ghosh2020rapid,title={Rapid Top-Down Synthesis of Large-Scale IoT Networks},author={Ghosh, Pradipta and Bunton, Jonathan and Pylorof, Dimitrios and Vieira, Marcos and Chan, Kevin and Govindan, Ramesh and Sukhatme, Gaurav and Tabuada, Paulo and Verma, Gunjan},url={https://arxiv.org/abs/2002.04244},year={2020},date={2020-01-01},booktitle={Proceedings of the IEEE International Conference on Computer Communications and Networks (ICCCN)},keywords={},pubstate={published},topics={sensing},tppubtype={inproceedings}}
@conference{Liu2019,title={Caesar: Cross-camera Complex Activity Recognition},author={Liu, Xiaochen and Ghosh, Pradipta and Ulutan, Oytun and Manjunath, B.S. and Chan, Kevin and Govindan, Ramesh},url={https://nsl.usc.edu/wp-content/uploads/2019/09/Caesar_SenSys19.pdf},doi={doi.org/10.1145/3356250.3360041},year={2019},date={2019-11-11},booktitle={SenSys ’19: Conference on Embedded Networked Sensor Systems},publisher={ACM},address={New York, USA},pubstate={published},tppubtype={conference},topics={sensing},abbr={Sensys}}
IoTDI
Kestrel: Video Analytics for Augmented Multi-Camera Vehicle Tracking
Qiu, H, Liu, X, Rallapalli, S, Bency, A J, Chan, K, Urgaonkar, R, Manjunath, B S, and Govindan, R
In 2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI) Feb 2018
@inproceedings{Qiu18c,title={Kestrel: Video Analytics for Augmented Multi-Camera Vehicle Tracking},author={Qiu, H and Liu, X and Rallapalli, S and Bency, A J and Chan, K and Urgaonkar, R and Manjunath, B S and Govindan, R},doi={10.1109/IoTDI.2018.00015},year={2018},date={2018-04-01},booktitle={2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)},pages={48-59},pubstate={published},tppubtype={inproceedings},topics={vehicular,sensing},abbr={IoTDI}}
ToN
Scalability and Satisfiability of Quality-of-Information in Wireless Networks
Rager, Scott T, Ciftcioglu, Ertugrul N, Ramanathan, Ram, Porta, Thomas F La, and Govindan, Ramesh
@article{Rager18a,title={Scalability and Satisfiability of Quality-of-Information in Wireless Networks},author={Rager, Scott T and Ciftcioglu, Ertugrul N and Ramanathan, Ram and Porta, Thomas F La and Govindan, Ramesh},url={https://doi.org/10.1109/TNET.2017.2781202},doi={10.1109/TNET.2017.2781202},issn={1063-6692},year={2018},date={2018-01-01},journal={IEEE/ACM Trans. Netw.},volume={26},number={1},pages={398--411},publisher={IEEE Press},address={Piscataway, NJ, USA},keywords={},abbr={ToN},pubstate={published},topics={sensing},tppubtype={article}}