Thrust 1: Connectivity
Wireless-optical edge cloud design to overcome fundamental knowledge gaps in the design of next-generation (beyond-5G and 6G) wireless access networks and edge-cloud infrastructure, collectively termed Wi-Edge.
Project 1: Foundations of mmWave Wireless
Millimeter-wave (mmWave) wireless networks operate at high-frequency ranges, enabling low-latency, high-speed, high-bandwidth communication that compares with traditional fiber-optic networks. In addition to being an important component of 5G (and beyond) cellular networks, the technology offers the ability to stream large volumes of data (e.g., from 8K cameras, 256-channel LiDAR) in real-time, from anywhere, to anywhere, without the need to install new fiber infrastructure. CS3 is developing programmable mmWave radios and networks and developing the theoretical and experimental foundations necessary to understand how this technology can be optimized for urban environments.
Understanding the propagation of mmWave in urban environments which includes moving interfering objects as well as moving transmitters and receivers is essential to supporting and assessing the viability of numerous streetscape applications.
Software defined radios are a critical part of wireless communication experimentation and development. Their design and implementation is itself a fundamental aspect of the core wireless research activities of CS3.
Project 2: Cognitive Wireless and Full Duplex Communication
In future streetscapes, wireless networks like mmWave will not exist in isolation. These networks must co-exist within a broad ecosystem of other wireless technologies (e.g., LoRa, 2.4GHz WiFi, 5GHz WiFi, LTE) and RF-emitting devices (e.g., microwave ovens). To ensure optimum performance of these networks in the presence of planned and unplanned (e.g., malicious) congestion and/or interference, the underlying waveforms and networks must be dynamically optimized. CS3 is developing new, intelligent wireless technologies that can dynamically understand the ways in which the available wireless spectrum is being used, and then seamlessly adapt to achieve the best performance possible. This includes the ability to simultaneously communicate in both directions over a single pair of devices.
Current and future streetscapes depend on a variety of wireless networks to enable situational awareness and autonomy. Recognizing the networks that operate within the streetscape is necessary to optimize wireless performance in real-time. To enable this understanding, CS3 is developing new spectrum sensing and classification techniques to identify signals between vehicles, infrastructure (e.g., traffic lights, road signs), pedestrians, and other transmitters and receivers. The innovations enable a range of important features, from spectrum sharing and optimization, to fault detection, to security and regulatory monitoring, and more.
The challenge of reliance on wireless communications to support the ecosystem of streetscape applications is that the urban wireless spectrum is already crowded. Dynamic management of the spectrum to ensure robustness of communications is critical.
Streetscape applications envisioned by CS3 often rely on low latency and high-bandwidth wireless communications. Standard wireless communications is half-duplex - two-way communication, but not simultaneously. Full duplex would permit simultaneous transmission and receiving, increasing data throughput and reducing latency.
Project 3: Edge-Cloud Integration and Management
Current streetscape applications largely adopt a sense-understand-decide-act model, where sensing and actuation occur within the physical streetscape, and understanding and decision-making occur within the compute cloud. As data volumes associated with sensing grow, it becomes impractical to transmit this data to the cloud and precludes the ability to realize applications that require low decide-act latencies (e.g., smart intersections, assistive wayfinding). CS3 is developing new models and mechanisms to dynamically manage streetscape workloads collaboratively between edge devices and cloud devices. The solutions offer improvements in latency, reductions in compute and networking costs, and narrowed data locality – keeping streetscape data local to the neighborhoods where it is collected.
CS3’s Streetscape applications run concurrently in shared spaces on shared edge-cloud computing resources. This requires a resource management strategy to robustly and efficiently control data flows and computational resources to achieve required tasks within application latency requirements.
The goal of this project is to develop a 5G/edge cloud platform for real time smart city applications such as augmented reality, smart intersections, and traffic safety. The team has recently prototyped a Kubernetes-based system with a distributed control plane that identifies computing and network resources for optimized placement of latency sensitive tasks across cloud servers. Demos and performance benchmarks are planned as the next step.
Real-time video analytics is crucial for smart city applications and cloud-connected vehicle control. To improve analytics accuracy, it is desirable to process the video at the highest resolution and frame rate. Intelligent adaptation of cameras' resolutions and frame rates based on network conditions and the monitored scene is crucial in order to optimize performance. We are developing ASTRA a low-overheard architecture for online adaptation of video analytics in multi-camera/edge-cloud settings.
Project 4: Joint Communication and Sensing
In addition to their fundamental purpose of enabling device-to-device communication, networking technologies underlying future streetscape applications offer novel situational awareness benefits. As examples, mmWave radios can be used as radar devices, and fiber-optic networks can be used to sense vibrations from human activities. CS3 is advancing these fundamental sensing primitives, as well as exploring how these primitives may be used while the networking technologies are in active use for communication. This offers the potential to enhance situational awareness without degrading communication performance.
mmWave transmission is affected by environmental conditions over the path. The attenuation and other effects can be used to infer environmental conditions between transmitter and receiver, permitting the use of the wireless transmission as not only a data link, but also as an environmental sensor. This can be very useful additional information to support various streetscape applications.
Localization of wireless transmission sources plays an important role in a variety of streetscape applications, including pedestrian mobility monitoring and vehicle mobility monitoring in camera-free zones. CS3 is developing RF-based localization solutions that vary in their infrastructure requirements. In streetscapes with traditional WiFi infrastructure, for example, CS3 is developing solutions based on multi-point measurement of received signal strength. In streetscapes with advanced mmWave capabilities, CS3 is leveraging mmWave’s ability to provide radar functionality, yielding finer-grained localization, including for fast moving objects.
Recent Publications
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Researchers
Gil Zussman
Wi-Edge Research Co-Lead; Professor of Electrical Engineering, Columbia University
Zoran Kostic
Professor of Professional Practice, Electrical Engineering, Columbia University
Dimitris Pados
Wi-Edge Research Co-Lead; Professor and I-SENSE Fellow, Florida Atlantic University
Henning Schulzrinne
Professor Dept. of Computer Science; Dept. of Electrical Engineering, Columbia University
Trainees
Alon S. Levin
Ph.D. Student in Electrical Engineering at Columbia UniversityBatsheva Gil
Undergraduate Student in Engineering at Florida Atlantic University
Georgios Orfanidis
Ph.D. Student in Electrical Engineering and Computer Science, Florida Atlantic University
Miguel Cruz Santos
Undergraduate Student in Electrical Engineering and Computer Science at Florida Atlantic University
Sanaz Naderi
Postdoctoral Scholar in Electrical Engineering and Computer Science, Florida Atlantic University
Alumni
