Research
The Center’s underlying technologies integrate advances in wireless optical communications, edge-cloud computing, situational awareness, and privacy and security, while balancing public sphere data collection requirements with community-defined benefits.
With its extensive network of partners, CS3 delivers innovations across five engineering and scientific areas:
THRUST 1
Connectivity & Wi-Edge
Current Research Projects:
- Foundations of mmWave Wireless
- Cognitive Wireless and Full Duplex Communication
- Edge-Cloud Integration and Management
- Joint Communication and Sensing
THRUST 2
Situational Awareness
Current Research Projects:
- Data and Systems Support for Scene and Activity Understanding
- Scene and Activity Understanding
- Trajectory Analysis and Prediction
- Multi-Modal Integration
THRUST 3
Privacy, Security & Fairness
Current Research Projects:
- Analysis of Emerging Smart Streetscape Threats
- Mitigation of Emerging Smart Streetscape Threats
- Community Legibility of Threats and Guarantees
THRUST 4
Public Interest Technology
Current Research Projects:
- Needs Assessment and Priority Mapping through Participatory Research
- Analysis of Community Tradeoffs
- Community Co-Production and Co-Design
THRUST 5
Streetscape Applications
Current Research Projects:
- Human-Computer Interaction Design for Smart Streetscapes
- Streetscape Application Stack and Runtime Design
- Smart Streetscape Operating System or Hypervisor Design
Demonstration Testbeds
CS3 operates three distinct urban testbeds, providing a shared experimental substrate for researchers and partners:
New York City, New York
Testbed Innovation Partners:
West Palm Beach, Florida
New Brunswick, New Jersey
Recent Publications
Connectivity & Wi-Edge
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Adhikari, Abhishek, et al. “28 GHz Phased Array Interference Measurements and Modeling for a NOAA Microwave Radiometer in Manhattan.” Proceedings of the 30th Annual International Conference on Mobile Computing and Networking, ACM, 2024, pp. 1695–97, https://doi.org/10.1145/3636534.3697463.
Mazokha, Stepan. Infrastructure and Methods for WiFi-Based Passive Device Localization, Fingerprinting, and Re-Identification for Mobility Intelligence. Dec. 2024, https://www.proquest.com/openview/574c07f2a9c123e7f1785b2a3f65fcaf/1?pq-origsite=gscholar&cbl=18750&diss=y.
Bao, Fanchen, et al. “Addressing Temporal RSSI Fluctuation in Passive Wi-Fi-Based Outdoor Localization.” IEEE Access, vol. 12, Oct. 2024, pp. 152998–3018, https://doi.org/10.1109/ACCESS.2024.3480828.
Situational Awareness
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Dave, Ishan Rajendrakumar, et al. “FinePseudo: Improving Pseudo-Labelling through Temporal-Alignablity for Semi-Supervised Fine-Grained Action Recognition.” Computer Vision – Eccv 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part Viii, Springer-Verlag, 2024, pp. 389–408, https://doi.org/10.1007/978-3-031-73242-3_22.
Dave, Ishan Rajendrakumar, et al. “FinePseudo: Improving Pseudo-Labelling through Temporal-Alignablity for Semi-Supervised Fine-Grained Action Recognition.” Computer Vision – Eccv 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part Viii, Springer-Verlag, 2024, pp. 389–408, https://doi.org/10.1007/978-3-031-73242-3_22.
Dave, Ishan Rajendrakumar, et al. “FinePseudo: Improving Pseudo-Labelling through Temporal-Alignablity for Semi-Supervised Fine-Grained Action Recognition.” Computer Vision – Eccv 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part Viii, Springer-Verlag, 2024, pp. 389–408, https://doi.org/10.1007/978-3-031-73242-3_22.
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Tholoniat, Pierre, et al. “Cookie Monster: Efficient On-Device Budgeting for Differentially-Private Ad-Measurement Systems.” Proceedings of the ACM SIGOPS 30th Symposium on Operating Systems Principles, ACM, 2024, pp. 693–708, https://doi.org/10.1145/3694715.3695965.
Tholoniat, Pierre, et al. DPack: Efficiency-Oriented Privacy Budget Scheduling. Oct. 2024, https://doi.org/10.48550/arXiv.2212.13228.
Hao, Luoyao, and Henning Schulzrinne. “Poster: Identity-Independent IoT for Overarching Policy Enforcement.” 2024 IEEE Security and Privacy Workshops (SPW), IEEE, 2024, pp. 296–296, https://doi.org/10.1109/SPW63631.2024.00036.
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Walter, Hedaya, et al. Enhancing Urban Data Analysis through Large Language Models: A Case Study with NYC 311 Service Requests. 2024, https://human-llm-interaction.github.io/workshop/hri24/papers/hllmi24_paper_11.pdf.
Walter, Hedaya, et al. Enhancing Urban Data Analysis through Large Language Models: A Case Study with NYC 311 Service Requests. 2024, https://human-llm-interaction.github.io/workshop/hri24/papers/hllmi24_paper_11.pdf.
Walter, Hedaya, et al. Enhancing Urban Data Analysis through Large Language Models: A Case Study with NYC 311 Service Requests. 2024, https://human-llm-interaction.github.io/workshop/hri24/papers/hllmi24_paper_11.pdf.
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