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We consider a decentralized wireless network with several source-destination pairs sharing a limited number of orthogonal frequency bands. Sources learn to adapt their transmissions (specifically, their band selection strategy) over time, in a decentralized manner, without sharing information with each other. Sources can only observe the outcome of their own transmissions (i.e., success or collision), having no prior knowledge of the network size or of the transmission strategy of other sources. The goal of each source is to maximize their own throughput while striving for network-wide fairness. We propose a novel fully decentralized Reinforcement Learning (RL)-based solution that achieves fairness without coordination. The proposed Fair Share RL (FSRL) solution combines: (i) state augmentation with a semi-adaptive time reference; (ii) an architecture that leverages risk control and time difference likelihood; and (iii) a fairnessdriven reward structure. We evaluate FSRL in several network settings. Simulation results suggest that, when we compare FSRL with a common baseline RL algorithm from the literature, FSRL can be up to 89.0% fairer (as measured by Jain’s fairness index) in stringent settings with several sources and a single frequency band, and 48.1% fairer on average. 

Urban environments pose significant challenges to pedestrian safety and mobility. This paper introduces a novel modular sensing framework for developing real-time, multimodal streetscape applications in smart cities. Prior urban sensing systems predominantly rely either on fixed data modalities or centralized data processing, resulting in limited flexibility, high latency, and superficial privacy protections. In contrast, our framework integrates diverse sensing modalities, including cameras, mobile IMU sensors, and wearables into a unified ecosystem leveraging edge-driven distributed analytics. The proposed modular architecture, supported by standardized APIs and message-driven communication, enables hyper-local sensing and scalable development of responsive pedestrian applications. A concrete application demonstrating multimodal pedestrian tracking is developed and evaluated. It is based on the cross-modal inference module, which fuses visual and mobile IMU sensor data to associate detected entities in the camera domain with their corresponding mobile device.We evaluate our framework’s performance in various urban sensing scenarios, demonstrating an online association accuracy of 75% with a latency of ≈39 milliseconds. Our results demonstrate significant potential for broader pedestrian safety and mobility scenarios in smart cities. 

Recent advances in Visual Language Models (VLMs) have significantly enhanced video analytics. VLMs capture complex visual and textual connections. While Convolutional Neural Networks (CNNs) excel in spatial pattern recognition, VLMs provide a global context, making them ideal for tasks like complex incidents and anomaly detection. However, VLMs are much more computationally intensive, posing challenges for large-scale and real-time applications. This paper introduces EdgeCloudAI, a scalable system integrating VLMs and CNNs through edge-cloud computing. Edge- CloudAI performs initial video processing (e.g., CNN) on edge devices and offloads deeper analysis (e.g., VLM) to the cloud, optimizing resource use and reducing latency. We have deployed EdgeCloudAI on the NSF COSMOS testbed in NYC. In this demo, we will demonstrate EdgeCloudAI’s performance in detecting user-defined incidents in real-time. 

Backscatter power measurements are collected to characterize indoor radar clutter in monostatic sensing applications. A narrowband 28 GHz sounder used a quasimonostatic radar arrangement with an omnidirectional transmit antenna illuminating an indoor scene and a spinning horn receive antenna offset vertically (less than 1 m away) collecting backscattered power as a function of azimuth. Power variation in azimuth around the local average is found to be within 1 dB of a lognormal distribution with a standard deviation of 6.8 dB. Backscatter azimuth spectra are found to be highly variable with location, with cross-correlation coefficients on the order of 0.3 at separations as small as 0.1 m. These statistics are needed for system-level evaluation of RF sensing performance. 

Backscatter power measurements are collected to characterize indoor radar clutter in monostatic sensing applications. A narrowband 28 GHz sounder used a quasi-monostatic radar arrangement with an omnidirectional transmit antenna illuminating an indoor scene and a spinning horn receive antenna offset vertically (less than 1 m away) collecting backscattered power as a function of azimuth. Power variation in azimuth around the local average is found to be within 1 dB of a lognormal distribution with a standard deviation of 6.8 dB. Backscatter azimuth spectra are found to be highly variable with location, with cross-correlation coefficients on the order of 0.3 at separations as small as 0.1 m. These statistics are needed for system-level evaluation of RF sensing performance. 

We present a novel data-driven simulation environment for modeling traffic in metropolitan street intersections. Using real-world tracking data collected over an extended period of time, we train trajectory forecasting models to learn agent interactions and environmental constraints that are difficult to capture conventionally. Trajectories of new agents are first coarsely generated by sampling from the spatial and temporal generative distributions, then refined using state-of-the-art trajectory forecasting models. The simulation can run either autonomously, or under explicit human control conditioned on the generative distributions. We present the experiments for a variety of model configurations. Under an iterative prediction scheme, the way-pointsupervised TrajNet++ model obtained 0.36 Final Displacement Error (FDE) in 20 FPS on an NVIDIA A100 GPU. 

Efficient spectrum use represents an important objective given the rapid growth in mobile data and emergence of Beyond-5G networks. ● NOAA passive radiometer receivers operating at the same millimeter-wave (mmWave) frequency used by COSMOS and 5G at 28 GHz and have experienced interference, particularly from a nearby bridge. ● We manually create interference using programmable 28 GHz COSMOS mobile phased array antenna modules (PAAMs) for the creation of Spectrum Consumption Models (SCMs). 

Massive MIMO has the potential to support demands of next generation networks and emerging applications such as V2V/V2X communication and augmented reality. ● Millimeter-Wave (mmWave) frequencies allow for larger bandwidth as well as compact form factor of antenna arrays with many elements. ● The COSMOS testbed has deployed indoor and outdoor 28GHz phased array antenna modules (PAAMs) to support experimentation with these emerging technologies. ● Mobile PAAMs have been developed to enable experimentation anywhere and with mobility. 

We introduce Boundless, a photo-realistic synthetic data generation system for enabling highly accurate object detection in dense urban streetscapes. Boundless can replace massive real-world data collection and manual groundtruth object annotation (labeling) with an automated and configurable process. Boundless is based on the Unreal Engine 5 (UE5) City Sample project with improvements enabling accurate collection of 3D bounding boxes across different lighting and scene variability conditions. We evaluate the performance of object detection models trained on the dataset generated by Boundless when used for inference on a real-world dataset acquired from medium-altitude cameras. We compare the performance of the Boundless-trained model against the CARLA-trained model and observe an improvement of 7.8 mAP. The results we achieved support the premise that synthetic data generation is a credible methodology for training/fine-tuning scalable object detection models for urban scenes.