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Millimeter-wave (mmWave) links are increasingly utilized in wireless x-haul transport to meet growing service demands. However, the inherent susceptibility of mmWave links to weather-related attenuation creates uncertainty about future network capacity which can significantly affect Quality of Service (QoS). This creates a critical challenge: how to make admission control decisions for slices with QoS requirements, balancing acceptance rewards against the risk of future QoS-violation penalties due to capacity uncertainty? To address this, we develop a proactive slice admission control framework that tightly integrates: (i) a predictor that leverages historical link measurements to forecast short-term attenuation and quantify uncertainty; and (ii) an admission control algorithm that incorporates both the predictions and uncertainties to maximize rewards and minimize QoS-violation penalties. We compare our framework against baseline, state-of-the-art, and idealized oracle algorithms using real-world mmWave x-haul data and residential traffic traces. Simulations suggest that our framework can achieve revenues that are 250% larger than baseline algorithms and 75% larger than state-of-the-art algorithms. 

In this work, we present an unmanned aerial vehicle (UAV) wireless dataset collected as part of the AERPAW Autonomous Aerial Data Mule (AADM) challenge, organized by the NSF Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) project. The AADM challenge was the second competition in which an autonomous UAV acted as a data mule, where the UAV downloaded data from multiple base stations (BSs) in a dynamic wireless environment. Participating teams designed flight control and decision-making algorithms for choosing which BSs to communicate with and how to plan flight trajectories to maximize data download within a mission completion time. The competition was conducted in two stages: Stage 1 involved development and experimentation using a digital twin (DT) environment, and in Stage 2, the final test run was conducted on the outdoor testbed. The total score for each team was compiled from both stages. The resulting dataset includes link quality and data download measurements, both in DT and physical environments. Along with the USRP measurements used in the contest, the dataset also includes UAV telemetry, Keysight RF sensors position estimates, link quality measurements from LoRa receivers, and Fortem radar measurements. It supports reproducible research on autonomous UAV networking, multi-cell association and scheduling, air-to-ground propagation modeling, DT-to-real-world transfer learning, and integrated sensing and communication, which serves as a benchmark for future autonomous wireless experimentation. 

Edge-cloud systems are increasingly deployed to meet the demands of real-time applications by processing latencysensitive tasks at the edge site, near the end-devices. When load exceeds capacity, offloading of requests to the cloud allows the system to balance the spike. As their popularity increases, edge servers become an attractive target for attackers whose goal is to degrade system performance. This paper investigates worst-case (i.e., damage-maximizing) attacks on reactive edge-cloud systems, focusing on two key performance metrics: expected end-to-end latency and the probability of violating service-level agreements (SLAs). We use a general modeling framework based on two layers of computation and analyze how adversarial removal of edge servers affects delay under reactive offloading mitigation. We adopt common queueing models and prove that the system’s performance remain convex in the attack size, even when accounting for reactive offloading by the edge. This enables a characterization of worst-case policies as concentrated attacks, targeting only a few critical sites to maximize damage, yielding greater performance degradation than spreading efforts across the network. Simulation results validate the analysis and demonstrate that the identified worst-case attack strategies can increase damage by up to 40% using the same attack resources. 

This paper introduces PAVE (Pedestrian Awareness Via Edge analytics), a scalable real-time video analytics system that uses street cameras to enhance pedestrian safety while preserving their privacy. PAVE processes live camera streams on an edge server to track pedestrians and vehicles in realtime, predict vehicles’ trajectories, and identify danger zones where pedestrians are present. The coordinates of these zones are sent to pedestrians’ mobile devices via a custom iOS app, which locally determines if they are at risk without sharing any data with the edge server, hence preserving privacy. Moreover, anonymized metadata, including real-time location and speed/direction of pedestrians and vehicles, are visualized on a public map. PAVE’s effectiveness was validated through deployment on the NSF COSMOS testbed, processing live video from cameras in diverse urban environments. Live field tests show that PAVE can alert at-risk pedestrians ∼0.9 s before a vehicle reaches them. Through extensive profiling, we show that optimizing memory/compute configuration per pipeline stage can reduce latency by up to 10× compared to the default operating system configurations. 

We develop novel RF canceler tuning algorithms for the phase and amplitude (P&A) based full-duplex (FD) radios in the open-access NSF PAWR COSMOS testbed. The RF canceler is a critical component in an FD radio, supporting FD operation by performing self-interference cancellation (SIC). To enable algorithm development, we use a mobile P&A-based FD radio to collect a dataset of canceler performance across different environments within the COSMOS testbed. This dataset is used to train neural networks (NNs) to predict the optimal configuration of the RF canceler using input features derived from an estimate of the self-interference channel. The best model achieves within 1 dB of the optimal cancellation with a one-shot prediction, while also achieving a 99% speedup compared to a baseline gradient descent algorithm. To the best of our knowledge, this is the first practical implementation of a NN-based tuning algorithm for a custom RF SI canceler suitable for small form factor devices, utilizing a software-defined radio for evaluation.